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Category:Learning in computer vision

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algorithm, that will learn to predict such labels given novel images or video. Learning-based methods have been used for a variety of computer vision tasks, including low-level problems such as image-denoising, and high-level tasks such as object recognition and scene classification.
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make use of training data to build systems for visual analysis. For example, one may train a system for detecting faces using training images of faces. Training data is often given in the forms of image or video collections, together with target labels. Such data is often fed into a
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The following 5 pages are in this category, out of 5 total.
108: 32:Pages in category "Learning in computer vision" 37:This list may not reflect recent changes 109: 86:One-shot learning (computer vision) 13: 41: 29: 14: 133: 98:Scale-invariant feature operator 1: 30: 7: 50:Boosting (machine learning) 10: 138: 16:Learning-based methods in 62:Constellation model 129: 122:Machine learning 23:machine learning 137: 136: 132: 131: 130: 128: 127: 126: 117:Computer vision 107: 106: 105: 104: 103: 102: 90: 78: 66: 54: 28: 18:computer vision 12: 11: 5: 135: 125: 124: 119: 101: 100: 94: 91: 89: 88: 82: 79: 77: 76: 70: 67: 65: 64: 58: 55: 53: 52: 46: 43: 42: 33: 9: 6: 4: 3: 2: 134: 123: 120: 118: 115: 114: 112: 99: 96: 95: 92: 87: 84: 83: 80: 75: 72: 71: 68: 63: 60: 59: 56: 51: 48: 47: 44: 40: 38: 31: 27: 24: 19: 34: 15: 111:Categories 74:ImageNets 113:: 39:. 93:S 81:O 69:I 57:C 45:B

Index

computer vision
machine learning
This list may not reflect recent changes
Boosting (machine learning)
Constellation model
ImageNets
One-shot learning (computer vision)
Scale-invariant feature operator
Categories
Computer vision
Machine learning

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