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974:. There is a significant overlap in the range of techniques and applications that these cover. This implies that the basic techniques that are used and developed in these fields are similar, something which can be interpreted as there is only one field with different names. On the other hand, it appears to be necessary for research groups, scientific journals, conferences, and companies to present or market themselves as belonging specifically to one of these fields and, hence, various characterizations which distinguish each of the fields from the others have been presented. In image processing, the input is an image and the output is an image as well, whereas in computer vision, an image or a video is taken as an input and the output could be an enhanced image, an understanding of the content of an image or even behavior of a computer system based on such understanding.
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finger mold and sensors could then be placed on top of a small sheet of rubber containing an array of rubber pins. A user can then wear the finger mold and trace a surface. A computer can then read the data from the strain gauges and measure if one or more of the pins are being pushed upward. If a pin is being pushed upward then the computer can recognize this as an imperfection in the surface. This sort of technology is useful in order to receive accurate data on imperfections on a very large surface. Another variation of this finger mold sensor are sensors that contain a camera suspended in silicon. The silicon forms a dome around the outside of the camera and embedded in the silicon are point markers that are equally spaced. These cameras can then be placed on devices such as robotic hands in order to allow the computer to receive highly accurate tactile data.
1504:; this is a benchmark in object classification and detection, with millions of images and 1000 object classes used in the competition. Performance of convolutional neural networks on the ImageNet tests is now close to that of humans. The best algorithms still struggle with objects that are small or thin, such as a small ant on the stem of a flower or a person holding a quill in their hand. They also have trouble with images that have been distorted with filters (an increasingly common phenomenon with modern digital cameras). By contrast, those kinds of images rarely trouble humans. Humans, however, tend to have trouble with other issues. For example, they are not good at classifying objects into fine-grained classes, such as the particular breed of dog or species of bird, whereas convolutional neural networks handle this with ease.
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be extracted from them also gets damaged. Therefore, we need to recover or restore the image as it was intended to be. The aim of image restoration is the removal of noise (sensor noise, motion blur, etc.) from images. The simplest possible approach for noise removal is various types of filters, such as low-pass filters or median filters. More sophisticated methods assume a model of how the local image structures look to distinguish them from noise. By first analyzing the image data in terms of the local image structures, such as lines or edges, and then controlling the filtering based on local information from the analysis step, a better level of noise removal is usually obtained compared to the simpler approaches.
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833:—indeed, just as many strands of AI research are closely tied with research into human intelligence and the use of stored knowledge to interpret, integrate, and utilize visual information. The field of biological vision studies and models the physiological processes behind visual perception in humans and other animals. Computer vision, on the other hand, develops and describes the algorithms implemented in software and hardware behind artificial vision systems. An interdisciplinary exchange between biological and computer vision has proven fruitful for both fields.
1283:. More advanced systems for missile guidance send the missile to an area rather than a specific target, and target selection is made when the missile reaches the area based on locally acquired image data. Modern military concepts, such as "battlefield awareness", imply that various sensors, including image sensors, provide a rich set of information about a combat scene that can be used to support strategic decisions. In this case, automatic processing of the data is used to reduce complexity and to fuse information from multiple sensors to increase reliability.
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1492: – the image data are scanned for specific objects along with their locations. Examples include the detection of an obstacle in the car's field of view and possible abnormal cells or tissues in medical images or the detection of a vehicle in an automatic road toll system. Detection based on relatively simple and fast computations is sometimes used for finding smaller regions of interesting image data which can be further analyzed by more computationally demanding techniques to produce a correct interpretation.
845:. Many methods for processing one-variable signals, typically temporal signals, can be extended in a natural way to the processing of two-variable signals or multi-variable signals in computer vision. However, because of the specific nature of images, there are many methods developed within computer vision that have no counterpart in the processing of one-variable signals. Together with the multi-dimensionality of the signal, this defines a subfield in signal processing as a part of computer vision.
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automated image analysis which is used in many fields. Machine vision usually refers to a process of combining automated image analysis with other methods and technologies to provide automated inspection and robot guidance in industrial applications. In many computer-vision applications, computers are pre-programmed to solve a particular task, but methods based on learning are now becoming increasingly common. Examples of applications of computer vision include systems for:
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1028:). This implies that image sensor technologies and control theory often are integrated with the processing of image data to control a robot and that real-time processing is emphasized by means of efficient implementations in hardware and software. It also implies that external conditions such as lighting can be and are often more controlled in machine vision than they are in general computer vision, which can enable the use of different algorithms.
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1445:, in the forms of decisions. Understanding in this context means the transformation of visual images (the input of the retina) into descriptions of the world that can interface with other thought processes and elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and learning theory.
1716:, tomography devices, radar, ultra-sonic cameras, etc. Depending on the type of sensor, the resulting image data is an ordinary 2D image, a 3D volume, or an image sequence. The pixel values typically correspond to light intensity in one or several spectral bands (gray images or colour images) but can also be related to various physical measures, such as depth, absorption or reflectance of sonic or electromagnetic waves, or
954:. Finally, a significant part of the field is devoted to the implementation aspect of computer vision; how existing methods can be realized in various combinations of software and hardware, or how these methods can be modified in order to gain processing speed without losing too much performance. Computer vision is also used in fashion eCommerce, inventory management, patent search, furniture, and the beauty industry.
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mechanical actuators, planning, information databases, man-machine interfaces, etc. The specific implementation of a computer vision system also depends on whether its functionality is pre-specified or if some part of it can be learned or modified during operation. Many functions are unique to the application. There are, however, typical functions that are found in many computer vision systems.
679:-based methods used in conjunction with machine learning techniques and complex optimization frameworks. The advancement of Deep Learning techniques has brought further life to the field of computer vision. The accuracy of deep learning algorithms on several benchmark computer vision data sets for tasks ranging from classification, segmentation and optical flow has surpassed prior methods.
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how natural vision systems operate in order to solve certain vision-related tasks. These results have led to a sub-field within computer vision where artificial systems are designed to mimic the processing and behavior of biological systems at different levels of complexity. Also, some of the learning-based methods developed within computer vision (
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vision system contains software, as well as a display in order to monitor the system. Vision systems for inner spaces, as most industrial ones, contain an illumination system and may be placed in a controlled environment. Furthermore, a completed system includes many accessories, such as camera supports, cables, and connectors.
1006:, by pixel-wise operations such as contrast enhancement, local operations such as edge extraction or noise removal, or geometrical transformations such as rotating the image. This characterization implies that image processing/analysis neither requires assumptions nor produces interpretations about the image content.
1317:). The level of autonomy ranges from fully autonomous (unmanned) vehicles to vehicles where computer-vision-based systems support a driver or a pilot in various situations. Fully autonomous vehicles typically use computer vision for navigation, e.g., for knowing where they are or mapping their environment (
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There are many kinds of computer vision systems; however, all of them contain these basic elements: a power source, at least one image acquisition device (camera, ccd, etc.), a processor, and control and communication cables or some kind of wireless interconnection mechanism. In addition, a practical
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Subsequent run of the network on an input image (left): The network correctly detects the starfish. However, the weakly weighted association between ringed texture and sea urchin also confers a weak signal to the latter from one of two intermediate nodes. In addition, a shell that was not included in
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of the scene. In the simplest case, the model can be a set of 3D points. More sophisticated methods produce a complete 3D surface model. The advent of 3D imaging not requiring motion or scanning, and related processing algorithms is enabling rapid advances in this field. Grid-based 3D sensing can be
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has greatly influenced the development of computer vision algorithms. Over the last century, there has been an extensive study of eyes, neurons, and brain structures devoted to the processing of visual stimuli in both humans and various animals. This has led to a coarse yet convoluted description of
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Image restoration comes into the picture when the original image is degraded or damaged due to some external factors like lens wrong positioning, transmission interference, low lighting or motion blurs, etc., which is referred to as noise. When the images are degraded or damaged, the information to
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Materials such as rubber and silicon are being used to create sensors that allow for applications such as detecting microundulations and calibrating robotic hands. Rubber can be used in order to create a mold that can be placed over a finger, inside of this mold would be multiple strain gauges. The
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of computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, multi-dimensional data from a 3D scanner, 3D point clouds from LiDaR sensors, or medical scanning
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Convolutional neural networks (CNNs) represent deep learning architectures that are currently used in a wide range of applications, including computer vision, speech recognition, identification of albuminous sequences in bioinformatics, production control, time series analysis in finance, and many
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in the human analog) into descriptions of the world that make sense to thought processes and can elicit appropriate action. This image understanding can be seen as the disentangling of symbolic information from image data using models constructed with the aid of geometry, physics, statistics, and
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While inference refers to the process of deriving new, not explicitly represented facts from currently known facts, control refers to the process that selects which of the many inference, search, and matching techniques should be applied at a particular stage of processing. Inference and control
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has made high-speed image acquisition, processing, and display possible for real-time systems on the order of hundreds to thousands of frames per second. For applications in robotics, fast, real-time video systems are critically important and often can simplify the processing needed for certain
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systems which, say, inspect bottles speeding by on a production line, to research into artificial intelligence and computers or robots that can comprehend the world around them. The computer vision and machine vision fields have significant overlap. Computer vision covers the core technology of
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Above is a silicon mold with a camera inside containing many different point markers. When this sensor is pressed against the surface, the silicon deforms, and the position of the point markers shifts. A computer can then take this data and determine how exactly the mold is pressed against the
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The organization of a computer vision system is highly application-dependent. Some systems are stand-alone applications that solve a specific measurement or detection problem, while others constitute a sub-system of a larger design which, for example, also contains sub-systems for control of
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Image-understanding systems (IUS) include three levels of abstraction as follows: low level includes image primitives such as edges, texture elements, or regions; intermediate level includes boundaries, surfaces and volumes; and high level includes objects, scenes, or events. Many of these
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from coming to market in an unusable manner. Another example is a measurement of the position and orientation of details to be picked up by a robot arm. Machine vision is also heavily used in the agricultural processes to remove undesirable foodstuff from bulk material, a process called
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can do. "Computer vision is concerned with the automatic extraction, analysis, and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve automatic visual understanding." As a
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Each of the application areas described above employ a range of computer vision tasks; more or less well-defined measurement problems or processing problems, which can be solved using a variety of methods. Some examples of typical computer vision tasks are presented below.
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Adopting computer vision technology might be painstaking for organizations as there is no single-point solution for it. Very few companies provide a unified and distributed platform or
Operating System where computer vision applications can be easily deployed and managed.
1258:, where information is extracted for the purpose of supporting a production process. One example is quality control where details or final products are being automatically inspected in order to find defects. One of the most prevalent fields for such inspection is the
864:. A detailed understanding of these environments is required to navigate through them. Information about the environment could be provided by a computer vision system, acting as a vision sensor and providing high-level information about the environment and the robot
1246:, about the structure of the brain or the quality of medical treatments. Applications of computer vision in the medical area also include enhancement of images interpreted by humans—ultrasonic images or X-ray images, for example—to reduce the influence of noise.
1726:– Before a computer vision method can be applied to image data in order to extract some specific piece of information, it is usually necessary to process the data in order to ensure that it satisfies certain assumptions implied by the method. Examples are:
1516: – finding all images in a larger set of images which have a specific content. The content can be specified in different ways, for example in terms of similarity relative to a target image (give me all images similar to image X) by utilizing
1039:
includes substantial work on the analysis of image data in medical applications. Progress in convolutional neural networks (CNNs) has improved the accurate detection of disease in medical images, particularly in cardiology, pathology, dermatology, and
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Several tasks relate to motion estimation, where an image sequence is processed to produce an estimate of the velocity either at each points in the image or in the 3D scene or even of the camera that produces the images. Examples of such tasks are:
1325:, a UAV looking for forest fires. Examples of supporting systems are obstacle warning systems in cars, cameras and LiDAR sensors in vehicles, and systems for autonomous landing of aircraft. Several car manufacturers have demonstrated systems for
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is the process of applying a range of technologies and methods to provide imaging-based automatic inspection, process control, and robot guidance in industrial applications. Machine vision tends to focus on applications, mainly in manufacturing,
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The representational requirements in the designing of IUS for these levels are: representation of prototypical concepts, concept organization, spatial knowledge, temporal knowledge, scaling, and description by comparison and differentiation.
773:. The starfish match with a ringed texture and a star outline, whereas most sea urchins match with a striped texture and oval shape. However, the instance of a ring-textured sea urchin creates a weakly weighted association between them.
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as a stepping stone to endowing robots with intelligent behavior. In 1966, it was believed that this could be achieved through an undergraduate summer project, by attaching a camera to a computer and having it "describe what it saw".
527:, computer vision is concerned with the theory behind artificial systems that extract information from images. The image data can take many forms, such as video sequences, views from multiple cameras, or multi-dimensional data from a
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requirements for IUS are: search and hypothesis activation, matching and hypothesis testing, generation and use of expectations, change and focus of attention, certainty and strength of belief, inference and goal satisfaction.
1329:. There are ample examples of military autonomous vehicles ranging from advanced missiles to UAVs for recon missions or missile guidance. Space exploration is already being made with autonomous vehicles using computer vision,
1542: – estimating the position or orientation of a specific object relative to the camera. An example application for this technique would be assisting a robot arm in retrieving objects from a conveyor belt in an
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Chervyakov, N. I.; Lyakhov, P. A.; Deryabin, M. A.; Nagornov, N. N.; Valueva, M. V.; Valuev, G. V. (2020). "Residue Number System-Based
Solution for Reducing the Hardware Cost of a Convolutional Neural Network".
941:
Besides the above-mentioned views on computer vision, many of the related research topics can also be studied from a purely mathematical point of view. For example, many methods in computer vision are based on
1838:– At this step, the input is typically a small set of data, for example, a set of points or an image region, which is assumed to contain a specific object. The remaining processing deals with, for example:
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and digital libraries. The core challenges are the acquisition, processing, analysis and rendering of visual information (mainly images and video). Application areas include industrial quality control,
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data from the real world in order to produce numerical or symbolic information, e.g. in the forms of decisions. Understanding in this context means the transformation of visual images (the input to the
1013:, how to reconstruct structure or other information about the 3D scene from one or several images. Computer vision often relies on more or less complex assumptions about the scene depicted in an image.
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or other malign changes, and a variety of dental pathologies; measurements of organ dimensions, blood flow, etc. are another example. It also supports medical research by providing new information:
535:
refers to a systems engineering discipline, especially in the context of factory automation. In more recent times, the terms computer vision and machine vision have converged to a greater degree.
1470:) – one or several pre-specified or learned objects or object classes can be recognized, usually together with their 2D positions in the image or 3D poses in the scene. Blippar,
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Russakovsky, Olga; Deng, Jia; Su, Hao; Krause, Jonathan; Satheesh, Sanjeev; Ma, Sean; Huang, Zhiheng; Karpathy, Andrej; Khosla, Aditya; Bernstein, Michael; Berg, Alexander C. (December 2015).
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to provide a complete understanding of the image formation process. Also, various measurement problems in physics can be addressed using computer vision, for example, motion in fluids.
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Soltani, A. A.; Huang, H.; Wu, J.; Kulkarni, T. D.; Tenenbaum, J. B. (2017). "Synthesizing 3D Shapes via
Modeling Multi-view Depth Maps and Silhouettes with Deep Generative Networks".
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is that of determining whether or not the image data contains some specific object, feature, or activity. Different varieties of recognition problem are described in the literature.
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a technology that enables the matching of faces in digital images or video frames to a face database, which is now widely used for mobile phone facelock, smart door locking, etc.
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AI and computer vision are closely linked, with AI enabling machines to interpret and understand visual data. Through techniques like image recognition and object detection,
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techniques, or in terms of high-level search criteria given as text input (give me all images which contain many houses, are taken during winter and have no cars in them).
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Widely adopted open-source container for GPU accelerated computer vision applications. Used by researchers, universities, private companies, as well as the U.S. Gov't.
1650:, vehicles, objects, humans or other organisms) in the image sequence. This has vast industry applications as most high-running machinery can be monitored in this way.
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produces image data from 3D models, and computer vision often produces 3D models from image data. There is also a trend towards a combination of the two disciplines,
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1662:, its apparent motion. This motion is a result of both how the corresponding 3D point is moving in the scene and how the camera is moving relative to the scene.
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structure from images with the goal of achieving full scene understanding. Studies in the 1970s formed the early foundations for many of the computer vision
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Esteva, Andre; Chou, Katherine; Yeung, Serena; Naik, Nikhil; Madani, Ali; Mottaghi, Ali; Liu, Yun; Topol, Eric; Dean, Jeff; Socher, Richard (2021-01-08).
1798:– At some point in the processing, a decision is made about which image points or regions of the image are relevant for further processing. Examples are:
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Military applications are probably one of the largest areas of computer vision. The obvious examples are the detection of enemy soldiers or vehicles and
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Most computer vision systems use visible-light cameras passively viewing a scene at frame rates of at most 60 frames per second (usually far slower).
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Jiao, Licheng; Zhang, Fan; Liu, Fang; Yang, Shuyuan; Li, Lingling; Feng, Zhixi; Qu, Rong (2019). "A Survey of Deep
Learning-Based Object Detection".
1956:, etc. Such hardware captures "images" that are then processed often using the same computer vision algorithms used to process visible-light images.
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used to acquire 3D images from multiple angles. Algorithms are now available to stitch multiple 3D images together into point clouds and 3D models.
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the training gives a weak signal for the oval shape, also resulting in a weak signal for the sea urchin output. These weak signals may result in a
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The next decade saw studies based on more rigorous mathematical analysis and quantitative aspects of computer vision. These include the concept of
1480: – an individual instance of an object is recognized. Examples include identification of a specific person's face or fingerprint,
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devices. The technological discipline of computer vision seeks to apply its theories and models to the construction of computer vision systems.
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which primarily focuses on the process of producing images, but sometimes also deals with the processing and analysis of images. For example,
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A few computer vision systems use image-acquisition hardware with active illumination or something other than visible light or both, such as
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and visualization, surveying, robotics, multimedia systems, virtual heritage, special effects in movies and television, and computer games.
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algorithms. When combined with a high-speed projector, fast image acquisition allows 3D measurement and feature tracking to be realized.
1638: – determining the 3D rigid motion (rotation and translation) of the camera from an image sequence produced by the camera.
531:. As a technological discipline, computer vision seeks to apply its theories and models for the construction of computer vision systems.
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Simplified example of training a neural network in object detection: The network is trained by multiple images that are known to depict
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In reality, textures and outlines would not be represented by single nodes, but rather by associated weight patterns of multiple nodes.
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624:. With the advent of optimization methods for camera calibration, it was realized that a lot of the ideas were already explored in
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648:. This decade also marked the first time statistical learning techniques were used in practice to recognize faces in images (see
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3071:"Optimizing Strawberry Disease and Quality Detection with Vision Transformers and Attention-Based Convolutional Neural Networks"
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Principles, algorithms, Applications, Learning 5th
Edition by E.R. Davies Academic Press, Elsevier 2018 ISBN 978-0-12-809284-2
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is a field that uses various methods to extract information from signals in general, mainly based on statistical approaches and
605:. Researchers also realized that many of these mathematical concepts could be treated within the same optimization framework as
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1619:- deals with recognizing the activity from a series of video frames, such as, if the person is picking up an object or walking.
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3741:." Information Processing and Management of Uncertainty in Knowledge-Based Systems. Springer International Publishing, 2014.
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1751:– Image features at various levels of complexity are extracted from the image data. Typical examples of such features are:
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in images of printed or handwritten text, usually with a view to encoding the text in a format more amenable to editing or
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of one or multiple videos into a series of per-frame foreground masks while maintaining its temporal semantic continuity.
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have enabled researchers to build models that are able to generate and reconstruct 3D shapes from single or multi-view
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powers computer vision to automate tasks such as facial recognition, autonomous driving, and medical imaging analysis.
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Computer vision includes 3D analysis from 2D images. This analyzes the 3D scene projected onto one or several images,
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652:). Toward the end of the 1990s, a significant change came about with the increased interaction between the fields of
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2020 International
Conference on Power Electronics & IoT Applications in Renewable Energy and its Control (PARC)
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Visual
Taxometric Approach to Image Segmentation Using Fuzzy-Spatial Taxon Cut Yields Contextually Relevant Regions
1658: – to determine, for each point in the image, how that point is moving relative to the image plane,
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1313:, land-based vehicles (small robots with wheels, cars, or trucks), aerial vehicles, and unmanned aerial vehicles (
730:. The process by which light interacts with surfaces is explained using physics. Physics explains the behavior of
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Balasundaram, A; Ashokkumar, S; Kothandaraman, D; kora, SeenaNaik; Sudarshan, E; Harshaverdhan, A (2020-12-01).
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While traditional broadcast and consumer video systems operate at a rate of 30 frames per second, advances in
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surface. This can be used to calibrate robotic hands in order to make sure they can grasp objects effectively.
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Rubber artificial skin layer with the flexible structure for the shape estimation of micro-undulation surfaces
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industry in which every single Wafer is being measured and inspected for inaccuracies or defects to prevent a
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Segmentation of image into nested scene architecture comprising foreground, object groups, single objects or
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Aghamohammadesmaeilketabforoosh, Kimia; Nikan, Soodeh; Antonini, Giorgio; Pearce, Joshua M. (January 2024).
1974:
systems are composed of a wearable camera that automatically take pictures from a first-person perspective.
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3445:"trackdem: Automated particle tracking to obtain population counts and size distributions from videos in r"
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Kagami, Shingo (2010). "High-speed vision systems and projectors for real-time perception of the world".
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Barrett, Lisa
Feldman; Adolphs, Ralph; Marsella, Stacy; Martinez, Aleix M.; Pollak, Seth D. (July 2019).
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3191:"Rubber artificial skin layer with flexible structure for shape estimation of micro-undulation surfaces"
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822:, is an early example of computer vision taking direct inspiration from neurobiology, specifically the
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58:
3232:"Dexterous object manipulation by a multi-fingered robotic hand with visual-tactile fingertip sensors"
2786:"Computational Vision and Business Intelligence in the Beauty Segment - An Analysis through Instagram"
2317:
The
British Machine Vision Association and Society for Pattern Recognition Retrieved February 20, 2017
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Bruijning, Marjolein; Visser, Marco D.; Hallmann, Caspar A.; Jongejans, Eelke; Golding, Nick (2018).
2005:
1982:
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1646: – following the movements of a (usually) smaller set of interest points or objects (
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3833:"Segment-Tube: Spatio-Temporal Action Localization in Untrimmed Videos with Per-Frame Segmentation"
1964:
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is another field that is closely related to computer vision. Most computer vision systems rely on
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3655:"Emotional Expressions Reconsidered: Challenges to Inferring Emotion From Human Facial Movements"
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2020:
1812:
1808:
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1230:, or medical image processing, characterized by the extraction of information from image data to
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The following characterizations appear relevant but should not be taken as universally accepted:
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based image and feature analysis and classification) have their background in neurobiology. The
613:. By the 1990s, some of the previous research topics became more active than others. Research in
551:
38:
1024:, vision-based robots and systems for vision-based inspection, measurement, or picking (such as
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3014:"Plant Species Identification Using Computer Vision Techniques: A Systematic Literature Review"
1978:
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a subset of facial recognition, emotion recognition refers to the process of classifying human
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148:
3751:
Liu, Ziyi; Wang, Le; Hua, Gang; Zhang, Qilin; Niu, Zhenxing; Wu, Ying; Zheng, Nanning (2018).
3329:"State-of-the-Art Analysis of Modern Drowsiness Detection Algorithms Based on Computer Vision"
2479:
1599:
Psychologists caution, however, that internal emotions cannot be reliably detected from faces.
5839:
5824:
5789:
5477:
5377:
5245:
4962:
4773:
4719:
3831:
Wang, Le; Duan, Xuhuan; Zhang, Qilin; Niu, Zhenxing; Hua, Gang; Zheng, Nanning (2018-05-22).
2000:
1555:
900:
755:
657:
524:
443:
5707:
3935:
2010 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition - Workshops
3400:
1321:), for detecting obstacles. It can also be used for detecting certain task-specific events,
5859:
5814:
5260:
5205:
5051:
5046:
4885:
4868:
4848:
4818:
3847:
3767:
3456:
3396:
2660:
2599:
1995:
1933:
1929:
1615:
1517:
1124:
1059:
602:
463:
90:
2774:." Proceedings of International Conference on Robotics and Automation. Vol. 2. IEEE, 1997.
1873:
Flag for further human review in medical, military, security and recognition applications.
1671:
Given one or (typically) more images of a scene, or a video, scene reconstruction aims at
8:
5434:
5412:
5161:
5156:
5114:
5066:
4890:
4753:
3409:
3384:
1804:
Segmentation of one or multiple image regions that contain a specific object of interest.
1603:
1589:
1259:
1201:
Tracking surfaces or planes in 3D coordinates for allowing Augmented Reality experiences.
1044:
916:
703:
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555:
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242:
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1180:
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861:
819:
645:
475:
471:
292:
2584:
1841:
Verification that the data satisfies model-based and application-specific assumptions.
1501:
1086:
Learning 3D shapes has been a challenging task in computer vision. Recent advances in
1051:. A significant part of this field is devoted to applying these methods to image data.
5885:
5873:
5677:
5329:
5200:
5193:
4843:
4788:
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2458:
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2256:
2214:
2208:
2187:
2159:
2131:
2100:
2045:
2015:
2010:
1971:
1847:
1672:
1339:
1297:
1118:
985:
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908:
888:
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739:
723:
625:
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5109:
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4952:
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4763:
4623:
4351:
4346:
4283:
4140:
3938:
3873:
3855:
3775:
3682:
3666:
3598:
3576:
3568:
3474:
3464:
3404:
3336:
3280:
3243:
3202:
3129:"New AI model developed at Western detects strawberry diseases, takes aim at waste"
3100:
3082:
3041:
3025:
2982:
2944:
2928:
2832:
2824:
2668:
2627:
2607:
2555:
2545:
2433:
2248:
1790:
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1524:
1488:
1438:
1430:
1280:
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912:
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880:
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691:
579:
455:
430:
418:
208:
143:
128:
4100:
4074:
3753:"Joint Video Object Discovery and Segmentation by Coupled Dynamic Markov Networks"
3284:
3068:
2235:
1844:
Estimation of application-specific parameters, such as object pose or object size.
5743:
5687:
5509:
5151:
5071:
4947:
4895:
4534:
3738:
3273:"Drowsiness Detection of a Driver using Conventional Computer Vision Application"
2986:
2771:
2723:
2506:
2452:
2374:
2314:
1949:
1759:
1732:
Noise reduction to ensure that sensor noise does not introduce false information.
1395:
1268:
1036:
1032:
904:
727:
665:
528:
483:
459:
2870:
Turek, Fred (June 2011). "Machine Vision Fundamentals, How to Make Robots See".
2672:
1441:
data from the real world in order to produce numerical or symbolic information,
506:
that deals with how computers can be made to gain high-level understanding from
5717:
5682:
5672:
5497:
5255:
5081:
4972:
4942:
4853:
4778:
4709:
4347:
4327:
4306:
3942:
3328:
3272:
3248:
3231:
3207:
3190:
2932:
2721:
2425:
2329:"Star Trek's "tricorder" medical scanner just got closer to becoming a reality"
2040:
2035:
1774:
1755:
1642:
1611:
systems differentiating human beings (head and shoulder patterns) from objects.
1608:
1529:
1474:, and LikeThat provide stand-alone programs that illustrate this functionality.
1471:
1454:
1434:
1387:
1326:
1255:
1136:
1075:
1055:
1016:
999:
971:
967:
883:
is a generic term for all computer science disciplines dealing with images and
787:
719:
629:
575:
532:
467:
422:
4540:
4220:
3572:
3029:
2550:
2533:
2531:
2504:
1254:
A second application area in computer vision is in industry, sometimes called
550:
In the late 1960s, computer vision began at universities that were pioneering
5922:
5662:
5642:
5559:
5238:
4937:
4524:– a complete list of papers of the most relevant computer vision conferences.
3869:
3787:
3779:
3678:
3670:
3590:
3488:
3418:
3257:
3216:
3096:
3037:
2940:
2569:
2252:
2123:
2050:
2025:
1937:
1543:
1302:
1087:
857:
829:
Some strands of computer vision research are closely related to the study of
811:
735:
715:
707:
507:
426:
138:
3469:
3444:
1811:
object parts (also referred to as spatial-taxon scene hierarchy), while the
5748:
5579:
4994:
4875:
4033:
3887:
3795:
3696:
3114:
3055:
2958:
2619:
1709:
1654:
1559:
1426:
1401:
1184:
815:
799:
583:
414:
282:
3624:
3087:
2828:
5844:
5615:
5524:
5519:
5141:
5119:
4783:
4328:
R. Fisher; K Dawson-Howe; A. Fitzgibbon; C. Robertson; E. Trucco (2005).
4163:
3710:
A. Maity (2015). "Improvised Salient Object Detection and Manipulation".
3581:
2837:
1741:
representation to enhance image structures at locally appropriate scales.
1738:
1735:
Contrast enhancement to ensure that relevant information can be detected.
1570:
1310:
1309:
One of the newer application areas is autonomous vehicles, which include
1025:
669:
594:
515:
311:
296:
2611:
2184:
Computer Vision and Applications, A Guide for Students and Practitioners
5738:
5697:
5692:
5605:
5514:
5422:
5334:
5314:
4537:– news, source code, datasets and job offers related to computer vision
4527:
4412:
3479:
3382:
2437:
2404:. Cambridge, Massachusetts London, England: The MIT Press. p. 28.
1689:
943:
766:
582:, representation of objects as interconnections of smaller structures,
4410:
3860:
2821:
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
5733:
5702:
5600:
5444:
5407:
5344:
5298:
5293:
5278:
4549:– supporting computer vision research within the UK via the BMVC and
4521:
4182:
3744:
2729:
2307:
1864:
Making the final decision required for the application, for example:
1634:
1091:
649:
571:
346:
110:
4516:
4281:
2505:
Nicu Sebe; Ira Cohen; Ashutosh Garg; Thomas S. Huang (3 June 2005).
2244:
5635:
5467:
3716:
3189:
Ando, Mitsuhito; Takei, Toshinobu; Mochiyama, Hiromi (2020-03-03).
2655:
2282:
2206:
1712:, which, besides various types of light-sensitive cameras, include
1002:
tend to focus on 2D images, how to transform one image to another,
951:
884:
762:
661:
183:
105:
4573:
3563:
2122:
1784:
More complex features may be related to texture, shape, or motion.
1729:
Re-sampling to ensure that the image coordinate system is correct.
1369:
5758:
5595:
5549:
5472:
5372:
5367:
5319:
3385:"Computer vision based fatigue detection using facial parameters"
2971:
1981:
are emerging as a new class of processors to complement CPUs and
1856:– comparing and combining two different views of the same object.
1596:
1574:
1361:
598:
351:
3442:
1453:
The classical problem in computer vision, image processing, and
5773:
5753:
5625:
5417:
4241:
4071:
3625:"AI Image Recognition: Inevitable Trending of Modern Lifestyle"
1507:
Several specialized tasks based on recognition exist, such as:
1349:
1235:
1161:
model has been developed to help farmers automatically detect
731:
562:
What distinguished computer vision from the prevalent field of
435:
4396:
4201:
3652:
5574:
5554:
5544:
5539:
5534:
5529:
5492:
5324:
3976:"A Third Type Of Processor For VR/AR: Movidius' Myriad 2 VPU"
1941:
1910:
1566:
1219:
734:
which are a core part of most imaging systems. Sophisticated
511:
4433:
4378:
Digital Image Processing: An Algorithmic Approach Using Java
4304:
3333:
2021 29th Conference of Open Innovations Association (FRUCT)
2765:
Stereo vision-based mapping and navigation for mobile robots
2722:
Steger, Carsten; Markus Ulrich; Christian Wiedemann (2018).
640:
and further multi-view stereo techniques. At the same time,
5564:
4493:
Feature Extraction and Image Processing for Computer Vision
4374:
4221:
Reinhard Klette; Karsten Schluens; Andreas Koschan (1998).
4052:
2532:
William Freeman; Pietro Perona; Bernhard Scholkopf (2008).
1496:
Currently, the best algorithms for such tasks are based on
1334:
3912:. New York: John Wiley & Sons, Inc. pp. 643–646.
3824:
3501:
2181:
1850:– classifying a detected object into different categories.
1412:
Tracking and counting organisms in the biological sciences
769:, which are correlated with "nodes" that represent visual
2379:. Springer Science & Business Media. pp. 10–16.
4145:
Three-Dimensional Computer Vision, A Geometric Viewpoint
3389:
IOP Conference Series: Materials Science and Engineering
1500:. An illustration of their capabilities is given by the
3544:
2534:"Guest Editorial: Machine Learning for Computer Vision"
2177:
2175:
2118:
2116:
1885:
requirements are entirely topics for further research.
962:
The fields most closely related to computer vision are
4183:
James L. Crowley; Henrik I. Christensen, eds. (1995).
3438:
3436:
2818:
2583:
LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey (2015).
2276:
539:
4224:
Computer Vision – Three-Dimensional Data from Images
4120:
2795:. American Research Institute for Policy Development
2693:
2172:
2113:
1437:
and understanding digital images, and extraction of
1301:, an example of an uncrewed land-based vehicle. The
4455:
Algorithms for Image Processing and Computer Vision
4411:Pedram Azad; Tilo Gockel; RĂĽdiger Dillmann (2008).
3547:"ImageNet Large Scale Visual Recognition Challenge"
3433:
2914:
1172:, medical image analysis or topographical modeling;
1157:monitoring agricultural crops, e.g. an open-source
597:, the inference of shape from various cues such as
578:from images, labeling of lines, non-polyhedral and
4357:Handbook of Mathematical Models in Computer Vision
4330:Dictionary of Computer Vision and Image Processing
4099:
4094:
4073:
3495:
3188:
3166:Machine Vision: Theory, Algorithms, Practicalities
2450:
1532:purposes in public places, malls, shopping centers
1106:Assisting humans in identification tasks, e.g., a
634:3-D reconstructions of scenes from multiple images
3910:Encyclopedia of Artificial Intelligence, Volume 1
2582:
2200:
1569:). A related task is reading of 2D codes such as
1502:ImageNet Large Scale Visual Recognition Challenge
1074:Applications range from tasks such as industrial
5920:
4739:
4471:
4431:
4139:
3967:
3830:
3018:Archives of Computational Methods in Engineering
2640:
2372:
2283:Milan Sonka; Vaclav Hlavac; Roger Boyle (2008).
2234:Huang, T. (1996-11-19). Vandoni, Carlo E (ed.).
1708:– A digital image is produced by one or several
1226:One of the most prominent application fields is
841:Yet another field related to computer vision is
4245:Introductory Techniques for 3-D Computer Vision
4007:
3750:
3327:Hasan, Fudail; Kashevnik, Alexey (2021-05-14).
2917:"Deep learning-enabled medical computer vision"
2454:Mind as Machine: A History of Cognitive Science
2368:
2366:
2364:
2362:
2094:
1867:Pass/fail on automatic inspection applications.
1801:Selection of a specific set of interest points.
27:Computerized information extraction from images
4568:Computer Vision Container, Joe Hoeller GitHub:
4161:
3326:
3230:Choi, Seung-hyun; Tahara, Kenji (2020-03-12).
3159:
3157:
3155:
3153:
3151:
3149:
2285:Image Processing, Analysis, and Machine Vision
2207:Dana H. Ballard; Christopher M. Brown (1982).
1879:
1484:, or the identification of a specific vehicle.
5010:
4589:
4543:– Bob Fisher's Compendium of Computer Vision.
3163:
3012:Wäldchen, Jana; Mäder, Patrick (2017-01-07).
3011:
2717:
2715:
2149:
2147:
2090:
2088:
818:, a neural network developed in the 1970s by
391:
5024:
4452:
4435:Computer Vision: Algorithms and Applications
3973:
3659:Psychological Science in the Public Interest
3526:
2891:"The Future of Automated Random Bin Picking"
2576:
2477:
2376:Computer Vision: Algorithms and Applications
2359:
2237:Computer Vision : Evolution And Promise
915:. Visual computing also includes aspects of
4490:
4262:
3520:
3146:
2869:
2863:
2525:
2471:
2428:(1966-07-01). "The Summer Vision Project".
2418:
1870:Match/no-match in recognition applications.
714:, which is typically in the form of either
5017:
5003:
4596:
4582:
4242:Emanuele Trucco; Alessandro Verri (1998).
4072:Barghout, Lauren; Lawrence W. Lee (2003).
4028:
3229:
2725:Machine Vision Algorithms and Applications
2712:
2153:
2144:
2085:
1425:Computer vision tasks include methods for
1094:or silhouettes seamlessly and efficiently.
1058:also overlaps with computer vision, e.g.,
398:
384:
4414:Computer Vision – Principles and Practice
4288:Multiple View Geometry in Computer Vision
4202:Gösta H. Granlund; Hans Knutsson (1995).
3877:
3859:
3715:
3686:
3580:
3562:
3478:
3468:
3408:
3247:
3206:
3104:
3086:
3045:
2948:
2836:
2654:
2559:
2549:
2511:. Springer Science & Business Media.
2484:. Springer Science & Business Media.
2444:
2399:
2227:
1234:. An example of this is the detection of
4517:USC Iris computer vision conference list
4076:Perceptual information processing system
3709:
3551:International Journal of Computer Vision
2814:
2812:
2810:
2538:International Journal of Computer Vision
2498:
1900:
1523:
1368:
1360:
1290:
1209:
1198:databases of images and image sequences.
1081:
686:
636:. Progress was made on the dense stereo
3907:
2347:
1666:
1222:'s Visual Media Reasoning concept video
860:or deliberation for robotic systems to
675:Recent work has seen the resurgence of
450:Sub-domains of computer vision include
14:
5921:
4749:3D reconstruction from multiple images
4375:Wilhelm Burger; Mark J. Burge (2007).
4080:. U.S. Patent Application 10/618,543.
4053:Azriel Rosenfeld; Avinash Kak (1982).
3932:
2424:
2373:Richard Szeliski (30 September 2010).
2326:
1546:situation or picking parts from a bin.
1286:
698:
518:, it seeks to automate tasks that the
4998:
4769:Simultaneous localization and mapping
4577:
4491:Nixon, Mark; Aguado, Alberto (2019).
4204:Signal Processing for Computer Vision
4165:Scale-Space Theory in Computer Vision
3760:IEEE Transactions on Image Processing
3622:
3540:
3538:
3502:David A. Forsyth; Jean Ponce (2003).
2807:
2233:
2182:Bernd Jähne; Horst Haußecker (2000).
848:
566:at that time was a desire to extract
5855:Generative adversarial network (GAN)
3937:. Vol. 2010. pp. 100–107.
3527:Forsyth, David; Ponce, Jean (2012).
3270:
2156:Computer Vision and Image Processing
1679:
1482:identification of handwritten digits
836:
4603:
4123:Computer Vision for robotic systems
3986:from the original on March 15, 2023
2783:
2763:Murray, Don, and Cullen Jennings. "
2508:Machine Learning in Computer Vision
2301:
1554:(OCR) – identifying
1390:creation for cinema and broadcast,
1356:
867:
656:and computer vision. This included
24:
4834:Automatic number-plate recognition
4547:British Machine Vision Association
4474:Computer Vision for Visual Effects
4417:. Elektor International Media BV.
4311:Emerging Topics in Computer Vision
4001:
3535:
3529:Computer vision: a modern approach
3504:Computer Vision, A Modern Approach
3223:
3182:
2308:http://www.bmva.org/visionoverview
2074:Outline of artificial intelligence
1623:
1168:Modeling objects or environments,
45:
25:
5950:
4522:Computer vision papers on the web
4510:
4014:. University of Minnesota Press.
1695:
1382:Other application areas include:
1249:
726:. The sensors are designed using
682:
632:. This led to methods for sparse
5893:
5892:
5872:
4839:Automated species identification
4495:(4th ed.). Academic Press.
3974:Seth Colaner (January 3, 2016).
3897:from the original on 2018-09-07.
3449:Methods in Ecology and Evolution
3341:10.23919/FRUCT52173.2021.9435480
2481:Three-Dimensional Machine Vision
2478:Takeo Kanade (6 December 2012).
2457:. Clarendon Press. p. 781.
2335:from the original on 2 July 2017
2272:from the original on 2018-02-07.
2247:. Geneva: CERN. pp. 21–25.
1416:
1103:, in manufacturing applications;
873:This section is an excerpt from
856:sometimes deals with autonomous
778:
754:
4824:Audio-visual speech recognition
3926:
3901:
3724:
3703:
3646:
3635:from the original on 2022-12-02
3616:
3605:from the original on 2023-03-15
3376:
3365:from the original on 2022-06-27
3320:
3309:from the original on 2022-06-27
3264:
3121:
3062:
3005:
2965:
2908:
2897:from the original on 2018-01-11
2883:
2793:Journal of Marketing Management
2777:
2757:
2746:from the original on 2023-03-15
2694:Ferrie, C.; Kaiser, S. (2019).
2687:
2634:
2400:Sejnowski, Terrence J. (2018).
2393:
1305:is mounted on top of the rover.
1150:, as the input to a device for
1069:
957:
936:
862:navigate through an environment
745:
620:led to better understanding of
66:Artificial general intelligence
5805:Recurrent neural network (RNN)
5795:Differentiable neural computer
4669:Recognition and categorization
4476:. Cambridge University Press.
4290:. Cambridge University Press.
3410:10.1088/1757-899x/981/2/022005
2327:Murphy, Mike (13 April 2017).
2320:
1448:
603:contour models known as snakes
13:
1:
5850:Variational autoencoder (VAE)
5810:Long short-term memory (LSTM)
5077:Computational learning theory
4933:Optical character recognition
4864:Content-based image retrieval
4206:. Kluwer Academic Publisher.
4121:Michael C. Fairhurst (1988).
4038:. W. H. Freeman and Company.
3285:10.1109/PARC49193.2020.236556
3271:Garg, Hitendra (2020-02-29).
2245:19th CERN School of Computing
2126:; George C. Stockman (2001).
2079:
2069:List of emerging technologies
1551:Optical character recognition
1513:Content-based image retrieval
1498:convolutional neural networks
1031:There is also a field called
497:
5830:Convolutional neural network
4011:The Birth of Computer Vision
3623:Quinn, Arthur (2022-10-09).
2987:10.1016/j.neucom.2020.04.018
2402:The deep learning revolution
1926:structured-light 3D scanners
1688:An example in this field is
1604:Shape Recognition Technology
574:that exist today, including
554:. It was meant to mimic the
7:
5825:Multilayer perceptron (MLP)
3908:Shapiro, Stuart C. (1992).
2784:Andrade, Norberto Almeida.
2673:10.1109/ACCESS.2019.2939201
2451:Margaret Ann Boden (2006).
2031:Teknomo–Fernandez algorithm
1988:
1896:
1880:Image-understanding systems
1408:Driver drowsiness detection
1274:
1205:
101:Natural language processing
10:
5955:
5901:Artificial neural networks
5815:Gated recurrent unit (GRU)
5041:Differentiable programming
4829:Automatic image annotation
4664:Noise reduction techniques
4055:Digital Picture Processing
3943:10.1109/CVPRW.2010.5543776
3249:10.1186/s40648-020-00162-5
3208:10.1186/s40648-020-00159-0
2933:10.1038/s41746-020-00376-2
2728:(2nd ed.). Weinheim:
2696:Neural Networks for Babies
2430:MIT AI Memos (1959 - 2004)
2064:Outline of computer vision
1965:consumer graphics hardware
1718:magnetic resonance imaging
1616:Human activity recognition
1327:autonomous driving of cars
1152:computer-human interaction
1049:artificial neural networks
921:human-computer interaction
872:
545:
514:. From the perspective of
486:, 3D scene modeling, and
413:tasks include methods for
154:Hybrid intelligent systems
76:Recursive self-improvement
5868:
5782:
5726:
5655:
5588:
5460:
5360:
5353:
5307:
5271:
5234:Artificial neural network
5214:
5090:
5057:Automatic differentiation
5030:
4981:
4794:Free viewpoint television
4730:
4697:
4611:
4472:Richard J. Radke (2013).
4432:Richard Szeliski (2010).
3573:10.1007/s11263-015-0816-y
3030:10.1007/s11831-016-9206-z
2872:NASA Tech Briefs Magazine
2561:21.11116/0000-0003-30FB-C
2551:10.1007/s11263-008-0127-7
2006:Computational photography
1983:graphics processing units
1961:digital signal processing
1946:magnetic resonance images
712:electromagnetic radiation
666:panoramic image stitching
628:theory from the field of
601:, texture and focus, and
5062:Neuromorphic engineering
5025:Differentiable computing
4859:Computer-aided diagnosis
4265:Digital Image Processing
4008:James E. Dobson (2023).
3780:10.1109/tip.2018.2859622
3671:10.1177/1529100619832930
2253:10.5170/CERN-1996-008.21
2095:Reinhard Klette (2014).
2057:
1954:synthetic aperture sonar
1815:is often implemented as
1190:Organizing information,
930:medical image processing
564:digital image processing
278:Artificial consciousness
5835:Residual neural network
5251:Artificial Intelligence
4921:Moving object detection
4911:Medical image computing
4674:Research infrastructure
4644:Image sensor technology
4560:(open-source journal),
4457:(2nd ed.). Wiley.
4227:. Springer, Singapore.
4162:Tony Lindeberg (1994).
3470:10.1111/2041-210X.12975
3401:2020MS&E..981b2005B
2097:Concise Computer Vision
2021:Machine vision glossary
1979:vision processing units
1228:medical computer vision
1113:Controlling processes,
642:variations of graph cut
552:artificial intelligence
504:interdisciplinary field
149:Evolutionary algorithms
39:Artificial intelligence
4958:Video content analysis
4926:Small object detection
4705:Computer stereo vision
4528:Computer Vision Online
3164:E. Roy Davies (2005).
2823:. pp. 1511–1519.
2158:. Palgrave Macmillan.
1914:
1533:
1375:
1366:
1306:
1223:
1108:species identification
1099:Automatic inspection,
1095:
1064:computer stereo vision
790:result for sea urchin.
695:
664:, view interpolation,
638:correspondence problem
502:Computer vision is an
478:, learning, indexing,
50:
5790:Neural Turing machine
5378:Human image synthesis
4963:Video motion analysis
4774:Structure from motion
4720:3D object recognition
4453:J. R. Parker (2011).
3088:10.3390/foods13121869
2829:10.1109/CVPR.2017.269
2001:Computational imaging
1985:(GPUs) in this role.
1934:hyperspectral imagers
1930:thermographic cameras
1904:
1836:High-level processing
1527:
1468:object classification
1372:
1364:
1294:
1218:
1085:
901:computational imaging
824:primary visual cortex
690:
670:light-field rendering
658:image-based rendering
525:scientific discipline
444:scientific discipline
49:
5881:Computer programming
5860:Graph neural network
5435:Text-to-video models
5413:Text-to-image models
5261:Large language model
5246:Scientific computing
5052:Statistical manifold
5047:Information geometry
4886:Foreground detection
4869:Reverse image search
4849:Bioimage informatics
4819:Activity recognition
4564:and one-day meetings
4350:and Yunmei Chen and
4282:Richard Hartley and
4263:Bernd Jähne (2002).
3980:www.tomshardware.com
3335:. pp. 141–149.
2921:npj Digital Medicine
1996:Chessboard detection
1673:computing a 3D model
1667:Scene reconstruction
1528:Computer vision for
1518:reverse image search
1295:Artist's concept of
1165:with 98.4% accuracy.
1060:stereophotogrammetry
611:Markov random fields
464:activity recognition
452:scene reconstruction
429:, and extraction of
91:General game playing
5939:Packaging machinery
5227:In-context learning
5067:Pattern recognition
4953:Autonomous vehicles
4891:Gesture recognition
4754:2D to 3D conversion
4438:. Springer-Verlag.
4187:. Springer-Verlag.
3852:2018Senso..18.1657W
3772:2018ITIP...27.5840L
3730:Barghout, Lauren. "
3461:2018MEcEv...9..965B
3168:. Morgan Kaufmann.
2665:2019IEEEA...7l8837J
2612:10.1038/nature14539
2604:2015Natur.521..436L
2154:Tim Morris (2004).
1907:2020 model iPad Pro
1590:Emotion recognition
1287:Autonomous vehicles
1163:strawberry diseases
1159:vision transformers
1141:restaurant industry
1133:visual surveillance
1045:pattern recognition
917:pattern recognition
899:, computer vision,
704:Solid-state physics
699:Solid-state physics
644:were used to solve
618:3-D reconstructions
580:polyhedral modeling
576:extraction of edges
556:human visual system
520:human visual system
243:Machine translation
159:Systems integration
96:Knowledge reasoning
33:Part of a series on
18:Texture recognition
5820:Echo state network
5708:JĂĽrgen Schmidhuber
5403:Facial recognition
5398:Speech recognition
5308:Software libraries
4968:Video surveillance
4906:Landmark detection
4814:3D pose estimation
4799:Volumetric capture
4759:Gaussian splatting
4715:Object recognition
4629:Commercial systems
4562:BMVA Summer School
4533:2011-11-30 at the
4096:Berthold K.P. Horn
4057:. Academic Press.
3737:2018-11-14 at the
3279:. pp. 50–53.
2770:2020-10-31 at the
2313:2017-02-16 at the
2186:. Academic Press.
1915:
1854:Image registration
1821:temporal attention
1748:Feature extraction
1593: –
1584: –
1582:Facial recognition
1534:
1463:Object recognition
1376:
1367:
1307:
1232:diagnose a patient
1224:
1181:autonomous vehicle
1096:
849:Robotic navigation
820:Kunihiko Fukushima
696:
646:image segmentation
622:camera calibration
476:3D pose estimation
472:object recognition
425:and understanding
51:
5916:
5915:
5678:Stephen Grossberg
5651:
5650:
4992:
4991:
4901:Image restoration
4844:Augmented reality
4809:
4808:
4789:4D reconstruction
4741:3D reconstruction
4634:Feature detection
4483:978-0-521-76687-6
4424:978-0-905705-71-2
4392:978-1-84628-379-6
4367:978-0-387-26371-7
4339:978-0-470-01526-1
4320:978-0-13-101366-7
4313:. Prentice Hall.
4297:978-0-521-54051-3
4274:978-3-540-67754-3
4255:978-0-13-261108-4
4248:. Prentice Hall.
4234:978-981-3083-71-4
4213:978-0-7923-9530-0
4194:978-3-540-58143-7
4185:Vision as Process
4175:978-0-7923-9418-1
4154:978-0-262-06158-2
4132:978-0-13-166919-2
4125:. Prentice Hall.
4113:978-0-262-08159-7
4087:978-0-262-08159-7
4064:978-0-12-597301-4
4045:978-0-7167-1284-8
4021:978-1-5179-1421-9
3952:978-1-4244-7029-7
3919:978-0-471-50306-4
3861:10.3390/s18051657
3766:(12): 5840–5853.
3513:978-0-13-085198-7
3506:. Prentice Hall.
3350:978-952-69244-5-8
3294:978-1-7281-6575-2
3175:978-0-12-206093-9
2848:978-1-5386-0457-1
2739:978-3-527-41365-2
2649:: 128837–128868.
2598:(7553): 436–444.
2518:978-1-4020-3274-5
2491:978-1-4613-1981-8
2464:978-0-19-954316-8
2411:978-0-262-03803-4
2386:978-1-84882-935-0
2294:978-0-495-08252-1
2220:978-0-13-165316-0
2213:. Prentice Hall.
2193:978-0-13-085198-7
2165:978-0-333-99451-1
2137:978-0-13-030796-5
2130:. Prentice Hall.
2106:978-1-4471-6320-6
2046:Visual perception
2016:Egocentric vision
2011:Computer audition
1972:Egocentric vision
1848:Image recognition
1706:Image acquisition
1680:Image restoration
1216:
986:augmented reality
984:, as explored in
978:Computer graphics
909:augmented reality
889:computer graphics
843:signal processing
837:Signal processing
831:biological vision
740:quantum mechanics
724:ultraviolet light
654:computer graphics
626:bundle adjustment
588:motion estimation
568:three-dimensional
488:image restoration
480:motion estimation
439:learning theory.
408:
407:
144:Bayesian networks
71:Intelligent agent
16:(Redirected from
5946:
5934:Image processing
5906:Machine learning
5896:
5895:
5876:
5631:Action selection
5621:Self-driving car
5428:Stable Diffusion
5393:Speech synthesis
5358:
5357:
5222:Machine learning
5098:Gradient descent
5019:
5012:
5005:
4996:
4995:
4916:Object detection
4881:Face recognition
4764:Shape from focus
4737:
4736:
4624:Digital geometry
4598:
4591:
4584:
4575:
4574:
4551:MIUA conferences
4506:
4487:
4468:
4449:
4428:
4407:
4405:
4404:
4395:. Archived from
4371:
4352:Olivier Faugeras
4343:
4324:
4305:GĂ©rard Medioni;
4301:
4284:Andrew Zisserman
4278:
4259:
4238:
4217:
4198:
4179:
4158:
4141:Olivier Faugeras
4136:
4117:
4105:
4091:
4079:
4068:
4049:
4025:
3996:
3995:
3993:
3991:
3971:
3965:
3964:
3930:
3924:
3923:
3905:
3899:
3898:
3896:
3881:
3863:
3837:
3828:
3822:
3821:
3819:
3818:
3812:
3806:. Archived from
3757:
3748:
3742:
3728:
3722:
3721:
3719:
3707:
3701:
3700:
3690:
3650:
3644:
3643:
3641:
3640:
3620:
3614:
3613:
3611:
3610:
3584:
3566:
3542:
3533:
3532:
3524:
3518:
3517:
3499:
3493:
3492:
3482:
3472:
3440:
3431:
3430:
3412:
3380:
3374:
3373:
3371:
3370:
3324:
3318:
3317:
3315:
3314:
3268:
3262:
3261:
3251:
3236:ROBOMECH Journal
3227:
3221:
3220:
3210:
3195:ROBOMECH Journal
3186:
3180:
3179:
3161:
3144:
3143:
3141:
3140:
3125:
3119:
3118:
3108:
3090:
3066:
3060:
3059:
3049:
3009:
3003:
3002:
2969:
2963:
2962:
2952:
2912:
2906:
2905:
2903:
2902:
2887:
2881:
2879:
2867:
2861:
2860:
2840:
2816:
2805:
2804:
2802:
2800:
2790:
2781:
2775:
2761:
2755:
2754:
2752:
2751:
2719:
2710:
2709:
2691:
2685:
2684:
2658:
2638:
2632:
2631:
2589:
2580:
2574:
2573:
2563:
2553:
2529:
2523:
2522:
2502:
2496:
2495:
2475:
2469:
2468:
2448:
2442:
2441:
2422:
2416:
2415:
2397:
2391:
2390:
2370:
2357:
2351:
2345:
2344:
2342:
2340:
2324:
2318:
2305:
2299:
2298:
2280:
2274:
2273:
2271:
2242:
2231:
2225:
2224:
2204:
2198:
2197:
2179:
2170:
2169:
2151:
2142:
2141:
2124:Linda G. Shapiro
2120:
2111:
2110:
2092:
1826:Segmentation or
1439:high-dimensional
1357:Tactile feedback
1281:missile guidance
1240:arteriosclerosis
1217:
1125:Detecting events
1119:industrial robot
996:Image processing
964:image processing
925:machine learning
913:video processing
893:image processing
881:Visual computing
875:Visual computing
868:Visual computing
854:Robot navigation
782:
758:
692:Object detection
456:object detection
431:high-dimensional
400:
393:
386:
307:Existential risk
129:Machine learning
30:
29:
21:
5954:
5953:
5949:
5948:
5947:
5945:
5944:
5943:
5929:Computer vision
5919:
5918:
5917:
5912:
5864:
5778:
5744:Google DeepMind
5722:
5688:Geoffrey Hinton
5647:
5584:
5510:Project Debater
5456:
5354:Implementations
5349:
5303:
5267:
5210:
5152:Backpropagation
5086:
5072:Tensor calculus
5026:
5023:
4993:
4988:
4977:
4948:Robotic mapping
4896:Image denoising
4805:
4726:
4693:
4659:Motion analysis
4607:
4605:Computer vision
4602:
4535:Wayback Machine
4513:
4503:
4484:
4465:
4446:
4425:
4402:
4400:
4393:
4368:
4340:
4321:
4298:
4275:
4256:
4235:
4214:
4195:
4176:
4155:
4133:
4114:
4088:
4065:
4046:
4022:
4004:
4002:Further reading
3999:
3989:
3987:
3972:
3968:
3953:
3931:
3927:
3920:
3906:
3902:
3894:
3835:
3829:
3825:
3816:
3814:
3810:
3755:
3749:
3745:
3739:Wayback Machine
3729:
3725:
3708:
3704:
3651:
3647:
3638:
3636:
3621:
3617:
3608:
3606:
3543:
3536:
3525:
3521:
3514:
3500:
3496:
3441:
3434:
3381:
3377:
3368:
3366:
3351:
3325:
3321:
3312:
3310:
3295:
3269:
3265:
3228:
3224:
3187:
3183:
3176:
3162:
3147:
3138:
3136:
3127:
3126:
3122:
3067:
3063:
3010:
3006:
2970:
2966:
2913:
2909:
2900:
2898:
2889:
2888:
2884:
2868:
2864:
2849:
2817:
2808:
2798:
2796:
2788:
2782:
2778:
2772:Wayback Machine
2762:
2758:
2749:
2747:
2740:
2720:
2713:
2706:
2698:. Sourcebooks.
2692:
2688:
2639:
2635:
2587:
2585:"Deep Learning"
2581:
2577:
2530:
2526:
2519:
2503:
2499:
2492:
2476:
2472:
2465:
2449:
2445:
2426:Papert, Seymour
2423:
2419:
2412:
2398:
2394:
2387:
2371:
2360:
2354:Computer Vision
2352:
2348:
2338:
2336:
2325:
2321:
2315:Wayback Machine
2306:
2302:
2295:
2281:
2277:
2269:
2263:
2240:
2232:
2228:
2221:
2210:Computer Vision
2205:
2201:
2194:
2180:
2173:
2166:
2152:
2145:
2138:
2128:Computer Vision
2121:
2114:
2107:
2093:
2086:
2082:
2060:
2055:
1991:
1950:side-scan sonar
1899:
1882:
1862:Decision making
1828:co-segmentation
1813:visual salience
1767:interest points
1698:
1682:
1669:
1626:
1624:Motion analysis
1539:Pose estimation
1451:
1419:
1398:(match moving).
1396:camera tracking
1359:
1289:
1277:
1269:optical sorting
1252:
1210:
1208:
1139:, e.g., in the
1137:people counting
1072:
1037:medical imaging
960:
939:
934:
933:
878:
870:
851:
839:
797:
796:
795:
794:
793:
791:
783:
775:
774:
759:
748:
728:quantum physics
710:, which detect
701:
694:in a photograph
685:
548:
529:medical scanner
500:
484:visual servoing
460:event detection
411:Computer vision
404:
375:
374:
365:
357:
356:
332:
322:
321:
293:Control problem
273:
263:
262:
174:
164:
163:
124:
116:
115:
86:Computer vision
61:
28:
23:
22:
15:
12:
11:
5:
5952:
5942:
5941:
5936:
5931:
5914:
5913:
5911:
5910:
5909:
5908:
5903:
5890:
5889:
5888:
5883:
5869:
5866:
5865:
5863:
5862:
5857:
5852:
5847:
5842:
5837:
5832:
5827:
5822:
5817:
5812:
5807:
5802:
5797:
5792:
5786:
5784:
5780:
5779:
5777:
5776:
5771:
5766:
5761:
5756:
5751:
5746:
5741:
5736:
5730:
5728:
5724:
5723:
5721:
5720:
5718:Ilya Sutskever
5715:
5710:
5705:
5700:
5695:
5690:
5685:
5683:Demis Hassabis
5680:
5675:
5673:Ian Goodfellow
5670:
5665:
5659:
5657:
5653:
5652:
5649:
5648:
5646:
5645:
5640:
5639:
5638:
5628:
5623:
5618:
5613:
5608:
5603:
5598:
5592:
5590:
5586:
5585:
5583:
5582:
5577:
5572:
5567:
5562:
5557:
5552:
5547:
5542:
5537:
5532:
5527:
5522:
5517:
5512:
5507:
5502:
5501:
5500:
5490:
5485:
5480:
5475:
5470:
5464:
5462:
5458:
5457:
5455:
5454:
5449:
5448:
5447:
5442:
5432:
5431:
5430:
5425:
5420:
5410:
5405:
5400:
5395:
5390:
5385:
5380:
5375:
5370:
5364:
5362:
5355:
5351:
5350:
5348:
5347:
5342:
5337:
5332:
5327:
5322:
5317:
5311:
5309:
5305:
5304:
5302:
5301:
5296:
5291:
5286:
5281:
5275:
5273:
5269:
5268:
5266:
5265:
5264:
5263:
5256:Language model
5253:
5248:
5243:
5242:
5241:
5231:
5230:
5229:
5218:
5216:
5212:
5211:
5209:
5208:
5206:Autoregression
5203:
5198:
5197:
5196:
5186:
5184:Regularization
5181:
5180:
5179:
5174:
5169:
5159:
5154:
5149:
5147:Loss functions
5144:
5139:
5134:
5129:
5124:
5123:
5122:
5112:
5107:
5106:
5105:
5094:
5092:
5088:
5087:
5085:
5084:
5082:Inductive bias
5079:
5074:
5069:
5064:
5059:
5054:
5049:
5044:
5036:
5034:
5028:
5027:
5022:
5021:
5014:
5007:
4999:
4990:
4989:
4982:
4979:
4978:
4976:
4975:
4973:Video tracking
4970:
4965:
4960:
4955:
4950:
4945:
4943:Remote sensing
4940:
4935:
4930:
4929:
4928:
4923:
4913:
4908:
4903:
4898:
4893:
4888:
4883:
4878:
4873:
4872:
4871:
4861:
4856:
4854:Blob detection
4851:
4846:
4841:
4836:
4831:
4826:
4821:
4816:
4810:
4807:
4806:
4804:
4803:
4802:
4801:
4796:
4786:
4781:
4779:View synthesis
4776:
4771:
4766:
4761:
4756:
4751:
4745:
4743:
4734:
4728:
4727:
4725:
4724:
4723:
4722:
4712:
4710:Motion capture
4707:
4701:
4699:
4695:
4694:
4692:
4691:
4686:
4681:
4676:
4671:
4666:
4661:
4656:
4651:
4646:
4641:
4636:
4631:
4626:
4621:
4615:
4613:
4609:
4608:
4601:
4600:
4593:
4586:
4578:
4572:
4571:
4565:
4555:Annals of the
4544:
4538:
4525:
4519:
4512:
4511:External links
4509:
4508:
4507:
4502:978-0128149768
4501:
4488:
4482:
4469:
4464:978-0470643853
4463:
4450:
4445:978-1848829343
4444:
4429:
4423:
4408:
4391:
4372:
4366:
4348:Nikos Paragios
4344:
4338:
4332:. John Wiley.
4325:
4319:
4307:Sing Bing Kang
4302:
4296:
4279:
4273:
4260:
4254:
4239:
4233:
4218:
4212:
4199:
4193:
4180:
4174:
4159:
4153:
4137:
4131:
4118:
4112:
4092:
4086:
4069:
4063:
4050:
4044:
4026:
4020:
4003:
4000:
3998:
3997:
3966:
3951:
3925:
3918:
3900:
3823:
3743:
3723:
3702:
3645:
3615:
3557:(3): 211–252.
3534:
3519:
3512:
3494:
3455:(4): 965–973.
3432:
3375:
3349:
3319:
3293:
3263:
3222:
3181:
3174:
3145:
3120:
3061:
3024:(2): 507–543.
3004:
2975:Neurocomputing
2964:
2907:
2882:
2862:
2847:
2806:
2776:
2756:
2738:
2711:
2705:978-1492671206
2704:
2686:
2633:
2575:
2524:
2517:
2497:
2490:
2470:
2463:
2443:
2417:
2410:
2392:
2385:
2358:
2346:
2319:
2300:
2293:
2275:
2262:978-9290830955
2261:
2226:
2219:
2199:
2192:
2171:
2164:
2143:
2136:
2112:
2105:
2083:
2081:
2078:
2077:
2076:
2071:
2066:
2059:
2056:
2054:
2053:
2048:
2043:
2041:Visual agnosia
2038:
2036:Vision science
2033:
2028:
2023:
2018:
2013:
2008:
2003:
1998:
1992:
1990:
1987:
1898:
1895:
1881:
1878:
1877:
1876:
1875:
1874:
1871:
1868:
1859:
1858:
1857:
1851:
1845:
1842:
1833:
1832:
1831:
1824:
1805:
1802:
1786:
1785:
1781:
1780:
1779:
1778:
1763:
1744:
1743:
1742:
1736:
1733:
1730:
1724:Pre-processing
1721:
1697:
1696:System methods
1694:
1681:
1678:
1668:
1665:
1664:
1663:
1651:
1639:
1625:
1622:
1621:
1620:
1612:
1609:people counter
1600:
1587:
1578:
1547:
1530:people counter
1522:
1521:
1494:
1493:
1485:
1478:Identification
1475:
1472:Google Goggles
1455:machine vision
1450:
1447:
1418:
1415:
1414:
1413:
1410:
1405:
1399:
1388:visual effects
1358:
1355:
1288:
1285:
1276:
1273:
1256:machine vision
1251:
1250:Machine vision
1248:
1207:
1204:
1203:
1202:
1199:
1188:
1173:
1166:
1155:
1144:
1122:
1111:
1104:
1076:machine vision
1071:
1068:
1056:Photogrammetry
1053:
1052:
1041:
1029:
1017:Machine vision
1014:
1007:
1000:image analysis
972:machine vision
968:image analysis
959:
956:
938:
935:
879:
871:
869:
866:
850:
847:
838:
835:
788:false positive
784:
777:
776:
760:
753:
752:
751:
750:
749:
747:
744:
700:
697:
684:
683:Related fields
681:
662:image morphing
630:photogrammetry
607:regularization
547:
544:
533:Machine vision
508:digital images
499:
496:
468:video tracking
427:digital images
406:
405:
403:
402:
395:
388:
380:
377:
376:
373:
372:
366:
363:
362:
359:
358:
355:
354:
349:
344:
339:
333:
328:
327:
324:
323:
320:
319:
314:
309:
304:
299:
290:
285:
280:
274:
269:
268:
265:
264:
261:
260:
255:
250:
245:
240:
239:
238:
228:
223:
218:
217:
216:
211:
206:
196:
191:
189:Earth sciences
186:
181:
179:Bioinformatics
175:
170:
169:
166:
165:
162:
161:
156:
151:
146:
141:
136:
131:
125:
122:
121:
118:
117:
114:
113:
108:
103:
98:
93:
88:
83:
78:
73:
68:
62:
57:
56:
53:
52:
42:
41:
35:
34:
26:
9:
6:
4:
3:
2:
5951:
5940:
5937:
5935:
5932:
5930:
5927:
5926:
5924:
5907:
5904:
5902:
5899:
5898:
5891:
5887:
5884:
5882:
5879:
5878:
5875:
5871:
5870:
5867:
5861:
5858:
5856:
5853:
5851:
5848:
5846:
5843:
5841:
5838:
5836:
5833:
5831:
5828:
5826:
5823:
5821:
5818:
5816:
5813:
5811:
5808:
5806:
5803:
5801:
5798:
5796:
5793:
5791:
5788:
5787:
5785:
5783:Architectures
5781:
5775:
5772:
5770:
5767:
5765:
5762:
5760:
5757:
5755:
5752:
5750:
5747:
5745:
5742:
5740:
5737:
5735:
5732:
5731:
5729:
5727:Organizations
5725:
5719:
5716:
5714:
5711:
5709:
5706:
5704:
5701:
5699:
5696:
5694:
5691:
5689:
5686:
5684:
5681:
5679:
5676:
5674:
5671:
5669:
5666:
5664:
5663:Yoshua Bengio
5661:
5660:
5658:
5654:
5644:
5643:Robot control
5641:
5637:
5634:
5633:
5632:
5629:
5627:
5624:
5622:
5619:
5617:
5614:
5612:
5609:
5607:
5604:
5602:
5599:
5597:
5594:
5593:
5591:
5587:
5581:
5578:
5576:
5573:
5571:
5568:
5566:
5563:
5561:
5560:Chinchilla AI
5558:
5556:
5553:
5551:
5548:
5546:
5543:
5541:
5538:
5536:
5533:
5531:
5528:
5526:
5523:
5521:
5518:
5516:
5513:
5511:
5508:
5506:
5503:
5499:
5496:
5495:
5494:
5491:
5489:
5486:
5484:
5481:
5479:
5476:
5474:
5471:
5469:
5466:
5465:
5463:
5459:
5453:
5450:
5446:
5443:
5441:
5438:
5437:
5436:
5433:
5429:
5426:
5424:
5421:
5419:
5416:
5415:
5414:
5411:
5409:
5406:
5404:
5401:
5399:
5396:
5394:
5391:
5389:
5386:
5384:
5381:
5379:
5376:
5374:
5371:
5369:
5366:
5365:
5363:
5359:
5356:
5352:
5346:
5343:
5341:
5338:
5336:
5333:
5331:
5328:
5326:
5323:
5321:
5318:
5316:
5313:
5312:
5310:
5306:
5300:
5297:
5295:
5292:
5290:
5287:
5285:
5282:
5280:
5277:
5276:
5274:
5270:
5262:
5259:
5258:
5257:
5254:
5252:
5249:
5247:
5244:
5240:
5239:Deep learning
5237:
5236:
5235:
5232:
5228:
5225:
5224:
5223:
5220:
5219:
5217:
5213:
5207:
5204:
5202:
5199:
5195:
5192:
5191:
5190:
5187:
5185:
5182:
5178:
5175:
5173:
5170:
5168:
5165:
5164:
5163:
5160:
5158:
5155:
5153:
5150:
5148:
5145:
5143:
5140:
5138:
5135:
5133:
5130:
5128:
5127:Hallucination
5125:
5121:
5118:
5117:
5116:
5113:
5111:
5108:
5104:
5101:
5100:
5099:
5096:
5095:
5093:
5089:
5083:
5080:
5078:
5075:
5073:
5070:
5068:
5065:
5063:
5060:
5058:
5055:
5053:
5050:
5048:
5045:
5043:
5042:
5038:
5037:
5035:
5033:
5029:
5020:
5015:
5013:
5008:
5006:
5001:
5000:
4997:
4987:
4986:
4985:Main category
4980:
4974:
4971:
4969:
4966:
4964:
4961:
4959:
4956:
4954:
4951:
4949:
4946:
4944:
4941:
4939:
4938:Pose tracking
4936:
4934:
4931:
4927:
4924:
4922:
4919:
4918:
4917:
4914:
4912:
4909:
4907:
4904:
4902:
4899:
4897:
4894:
4892:
4889:
4887:
4884:
4882:
4879:
4877:
4874:
4870:
4867:
4866:
4865:
4862:
4860:
4857:
4855:
4852:
4850:
4847:
4845:
4842:
4840:
4837:
4835:
4832:
4830:
4827:
4825:
4822:
4820:
4817:
4815:
4812:
4811:
4800:
4797:
4795:
4792:
4791:
4790:
4787:
4785:
4782:
4780:
4777:
4775:
4772:
4770:
4767:
4765:
4762:
4760:
4757:
4755:
4752:
4750:
4747:
4746:
4744:
4742:
4738:
4735:
4733:
4729:
4721:
4718:
4717:
4716:
4713:
4711:
4708:
4706:
4703:
4702:
4700:
4696:
4690:
4687:
4685:
4682:
4680:
4677:
4675:
4672:
4670:
4667:
4665:
4662:
4660:
4657:
4655:
4652:
4650:
4647:
4645:
4642:
4640:
4637:
4635:
4632:
4630:
4627:
4625:
4622:
4620:
4617:
4616:
4614:
4610:
4606:
4599:
4594:
4592:
4587:
4585:
4580:
4579:
4576:
4569:
4566:
4563:
4559:
4558:
4552:
4548:
4545:
4542:
4539:
4536:
4532:
4529:
4526:
4523:
4520:
4518:
4515:
4514:
4504:
4498:
4494:
4489:
4485:
4479:
4475:
4470:
4466:
4460:
4456:
4451:
4447:
4441:
4437:
4436:
4430:
4426:
4420:
4416:
4415:
4409:
4399:on 2014-05-17
4398:
4394:
4388:
4384:
4380:
4379:
4373:
4369:
4363:
4359:
4358:
4353:
4349:
4345:
4341:
4335:
4331:
4326:
4322:
4316:
4312:
4308:
4303:
4299:
4293:
4289:
4285:
4280:
4276:
4270:
4266:
4261:
4257:
4251:
4247:
4246:
4240:
4236:
4230:
4226:
4225:
4219:
4215:
4209:
4205:
4200:
4196:
4190:
4186:
4181:
4177:
4171:
4167:
4166:
4160:
4156:
4150:
4147:. MIT Press.
4146:
4142:
4138:
4134:
4128:
4124:
4119:
4115:
4109:
4106:. MIT Press.
4104:
4103:
4097:
4093:
4089:
4083:
4078:
4077:
4070:
4066:
4060:
4056:
4051:
4047:
4041:
4037:
4036:
4031:
4027:
4023:
4017:
4013:
4012:
4006:
4005:
3985:
3981:
3977:
3970:
3962:
3958:
3954:
3948:
3944:
3940:
3936:
3929:
3921:
3915:
3911:
3904:
3893:
3889:
3885:
3880:
3875:
3871:
3867:
3862:
3857:
3853:
3849:
3845:
3841:
3834:
3827:
3813:on 2018-09-07
3809:
3805:
3801:
3797:
3793:
3789:
3785:
3781:
3777:
3773:
3769:
3765:
3761:
3754:
3747:
3740:
3736:
3733:
3727:
3718:
3713:
3706:
3698:
3694:
3689:
3684:
3680:
3676:
3672:
3668:
3664:
3660:
3656:
3649:
3634:
3630:
3626:
3619:
3604:
3600:
3596:
3592:
3588:
3583:
3582:1721.1/104944
3578:
3574:
3570:
3565:
3560:
3556:
3552:
3548:
3541:
3539:
3530:
3523:
3515:
3509:
3505:
3498:
3490:
3486:
3481:
3476:
3471:
3466:
3462:
3458:
3454:
3450:
3446:
3439:
3437:
3428:
3424:
3420:
3416:
3411:
3406:
3402:
3398:
3395:(2): 022005.
3394:
3390:
3386:
3379:
3364:
3360:
3356:
3352:
3346:
3342:
3338:
3334:
3330:
3323:
3308:
3304:
3300:
3296:
3290:
3286:
3282:
3278:
3274:
3267:
3259:
3255:
3250:
3245:
3241:
3237:
3233:
3226:
3218:
3214:
3209:
3204:
3200:
3196:
3192:
3185:
3177:
3171:
3167:
3160:
3158:
3156:
3154:
3152:
3150:
3134:
3130:
3124:
3116:
3112:
3107:
3102:
3098:
3094:
3089:
3084:
3080:
3076:
3072:
3065:
3057:
3053:
3048:
3043:
3039:
3035:
3031:
3027:
3023:
3019:
3015:
3008:
3001:
2996:
2992:
2988:
2984:
2980:
2976:
2968:
2960:
2956:
2951:
2946:
2942:
2938:
2934:
2930:
2926:
2922:
2918:
2911:
2896:
2892:
2886:
2877:
2873:
2866:
2858:
2854:
2850:
2844:
2839:
2838:1721.1/126644
2834:
2830:
2826:
2822:
2815:
2813:
2811:
2794:
2787:
2780:
2773:
2769:
2766:
2760:
2745:
2741:
2735:
2732:. p. 1.
2731:
2727:
2726:
2718:
2716:
2707:
2701:
2697:
2690:
2682:
2678:
2674:
2670:
2666:
2662:
2657:
2652:
2648:
2644:
2637:
2629:
2625:
2621:
2617:
2613:
2609:
2605:
2601:
2597:
2593:
2586:
2579:
2571:
2567:
2562:
2557:
2552:
2547:
2543:
2539:
2535:
2528:
2520:
2514:
2510:
2509:
2501:
2493:
2487:
2483:
2482:
2474:
2466:
2460:
2456:
2455:
2447:
2439:
2435:
2431:
2427:
2421:
2413:
2407:
2403:
2396:
2388:
2382:
2378:
2377:
2369:
2367:
2365:
2363:
2355:
2350:
2334:
2330:
2323:
2316:
2312:
2309:
2304:
2296:
2290:
2286:
2279:
2268:
2264:
2258:
2254:
2250:
2246:
2239:
2238:
2230:
2222:
2216:
2212:
2211:
2203:
2195:
2189:
2185:
2178:
2176:
2167:
2161:
2157:
2150:
2148:
2139:
2133:
2129:
2125:
2119:
2117:
2108:
2102:
2098:
2091:
2089:
2084:
2075:
2072:
2070:
2067:
2065:
2062:
2061:
2052:
2051:Visual system
2049:
2047:
2044:
2042:
2039:
2037:
2034:
2032:
2029:
2027:
2026:Space mapping
2024:
2022:
2019:
2017:
2014:
2012:
2009:
2007:
2004:
2002:
1999:
1997:
1994:
1993:
1986:
1984:
1980:
1975:
1973:
1969:
1966:
1962:
1957:
1955:
1951:
1947:
1943:
1939:
1938:radar imaging
1935:
1931:
1927:
1922:
1919:
1912:
1908:
1903:
1894:
1890:
1886:
1872:
1869:
1866:
1865:
1863:
1860:
1855:
1852:
1849:
1846:
1843:
1840:
1839:
1837:
1834:
1829:
1825:
1822:
1818:
1814:
1810:
1806:
1803:
1800:
1799:
1797:
1796:
1792:
1788:
1787:
1783:
1782:
1776:
1772:
1768:
1764:
1761:
1757:
1753:
1752:
1750:
1749:
1745:
1740:
1737:
1734:
1731:
1728:
1727:
1725:
1722:
1719:
1715:
1714:range sensors
1711:
1710:image sensors
1707:
1704:
1703:
1702:
1693:
1691:
1686:
1677:
1674:
1661:
1657:
1656:
1652:
1649:
1645:
1644:
1640:
1637:
1636:
1632:
1631:
1630:
1618:
1617:
1613:
1610:
1606:
1605:
1601:
1598:
1594:
1591:
1588:
1585:
1583:
1579:
1576:
1572:
1568:
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1466:(also called
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1417:Typical tasks
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1303:stereo camera
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1264:computer chip
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1088:deep learning
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865:
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858:path planning
855:
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812:deep learning
809:
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738:even require
737:
736:image sensors
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708:image sensors
705:
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236:Mental health
234:
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199:Generative AI
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139:Deep learning
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5749:Hugging Face
5713:David Silver
5361:Audio–visual
5215:Applications
5194:Augmentation
5039:
4983:
4876:Eye tracking
4732:Applications
4698:Technologies
4684:Segmentation
4604:
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4473:
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4434:
4413:
4401:. Retrieved
4397:the original
4377:
4360:. Springer.
4356:
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4287:
4267:. Springer.
4264:
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4102:Robot Vision
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3808:the original
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3135:. 2024-09-13
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1976:
1970:
1958:
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1835:
1795:segmentation
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1655:Optical flow
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1402:Surveillance
1391:
1381:
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1338:
1330:
1322:
1311:submersibles
1308:
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1185:mobile robot
1176:
1175:Navigation,
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1100:
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1070:Applications
1054:
1021:
1010:
1003:
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981:
976:
961:
958:Distinctions
948:optimization
940:
937:Other fields
852:
840:
828:
816:Neocognitron
804:
800:Neurobiology
798:
746:Neurobiology
702:
674:
592:
584:optical flow
561:
549:
537:
501:
492:
449:
441:
410:
409:
283:Chinese room
172:Applications
85:
5897:Categories
5845:Autoencoder
5800:Transformer
5668:Alex Graves
5616:OpenAI Five
5520:IBM Watsonx
5142:Convolution
5120:Overfitting
4784:Visual hull
4679:Researchers
3846:(5): 1657.
3665:(1): 1–68.
3480:2066/184075
2981:: 439–453.
2880:pages 60–62
2643:IEEE Access
2438:1721.1/6125
2287:. Thomson.
1739:Scale space
1571:data matrix
1449:Recognition
1386:Support of
1026:bin picking
767:sea urchins
595:scale-space
516:engineering
312:Turing test
288:Friendly AI
59:Major goals
5923:Categories
5886:Technology
5739:EleutherAI
5698:Fei-Fei Li
5693:Yann LeCun
5606:Q-learning
5589:Decisional
5515:IBM Watson
5423:Midjourney
5315:TensorFlow
5162:Activation
5115:Regression
5110:Clustering
4654:Morphology
4612:Categories
4403:2007-06-13
4030:David Marr
3817:2018-09-14
3717:1511.02999
3639:2022-12-23
3609:2020-11-20
3531:. Pearson.
3369:2022-11-06
3313:2022-11-06
3139:2024-09-19
2901:2018-01-10
2750:2018-01-30
2656:1907.09408
2080:References
1944:scanners,
1777:or points.
1765:Localized
1690:inpainting
1556:characters
1431:processing
1092:depth maps
1040:radiology.
944:statistics
887:, such as
808:neural net
668:and early
615:projective
572:algorithms
498:Definition
419:processing
317:Regulation
271:Philosophy
226:Healthcare
221:Government
123:Approaches
5769:MIT CSAIL
5734:Anthropic
5703:Andrew Ng
5601:AlphaZero
5445:VideoPoet
5408:AlphaFold
5345:MindSpore
5299:SpiNNaker
5294:Memristor
5201:Diffusion
5177:Rectifier
5157:Batchnorm
5137:Attention
5132:Adversary
3870:1424-8220
3788:1057-7149
3679:1529-1006
3629:TopTen.ai
3591:0920-5691
3564:1409.0575
3489:2041-210X
3427:230639179
3419:1757-899X
3359:235207036
3303:218564267
3258:2197-4225
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3038:1134-3060
2995:219470398
2941:2398-6352
2730:Wiley-VCH
2681:198147317
2570:1573-1405
1791:Detection
1635:Egomotion
1607:(SRT) in
1597:emotions.
1489:Detection
1435:analyzing
1427:acquiring
1340:Curiosity
1298:Curiosity
1043:Finally,
885:3D models
650:Eigenface
423:analyzing
415:acquiring
347:AI winter
248:Military
111:AI safety
5877:Portals
5636:Auto-GPT
5468:Word2vec
5272:Hardware
5189:Datasets
5091:Concepts
4689:Software
4649:Learning
4639:Geometry
4619:Datasets
4541:CVonline
4531:Archived
4383:Springer
4354:(2005).
4309:(2004).
4286:(2003).
4143:(1993).
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4032:(1982).
3984:Archived
3961:14111100
3892:Archived
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3804:51867241
3796:30059300
3735:Archived
3697:31313636
3633:Archived
3603:Archived
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3307:Archived
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3106:11202458
3056:29962832
2959:33420381
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2895:Archived
2857:31373273
2799:11 March
2768:Archived
2744:Archived
2620:26017442
2544:(1): 1.
2333:Archived
2311:Archived
2267:Archived
1989:See also
1897:Hardware
1769:such as
1643:Tracking
1560:indexing
1275:Military
1206:Medicine
1196:indexing
1179:, by an
952:geometry
771:features
763:starfish
720:infrared
370:Glossary
364:Glossary
342:Progress
337:Timeline
297:Takeover
258:Projects
231:Industry
194:Finance
184:Deepfake
134:Symbolic
106:Robotics
81:Planning
5759:Meta AI
5596:AlphaGo
5580:PanGu-ÎŁ
5550:ChatGPT
5525:Granite
5473:Seq2seq
5452:Whisper
5373:WaveNet
5368:AlexNet
5340:Flux.jl
5320:PyTorch
5172:Sigmoid
5167:Softmax
5032:General
3879:5982167
3848:Bibcode
3840:Sensors
3768:Bibcode
3688:6640856
3599:2930547
3457:Bibcode
3397:Bibcode
3047:6003396
3000:others.
2950:7794558
2661:Bibcode
2628:3074096
2600:Bibcode
2339:18 July
1909:with a
1817:spatial
1809:salient
1771:corners
1754:Lines,
1353:rover.
1236:tumours
1110:system;
1033:imaging
905:virtual
716:visible
677:feature
599:shading
546:History
352:AI boom
330:History
253:Physics
5774:Huawei
5754:OpenAI
5656:People
5626:MuZero
5488:Gemini
5483:Claude
5418:DALL-E
5330:Theano
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1577:codes.
1350:Yutu-2
1194:, for
1131:, for
911:, and
732:optics
586:, and
512:videos
436:retina
302:Ethics
5840:Mamba
5611:SARSA
5575:LLaMA
5570:BLOOM
5555:GPT-J
5545:GPT-4
5540:GPT-3
5535:GPT-2
5530:GPT-1
5493:LaMDA
5325:Keras
3957:S2CID
3895:(PDF)
3836:(PDF)
3811:(PDF)
3800:S2CID
3756:(PDF)
3712:arXiv
3595:S2CID
3559:arXiv
3423:S2CID
3355:S2CID
3299:S2CID
3075:Foods
2991:S2CID
2853:S2CID
2789:(PDF)
2677:S2CID
2651:arXiv
2624:S2CID
2588:(PDF)
2270:(PDF)
2241:(PDF)
2058:Lists
1942:lidar
1911:LiDAR
1775:blobs
1756:edges
1567:ASCII
1260:Wafer
1220:DARPA
1117:, an
214:Music
209:Audio
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5565:PaLM
5498:Bard
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