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PERFORMANCE OF IMAGE RECOGNITION ALGORITHMS BY PRUNING FEATURES, IMAGE SCALING, AND SPATIALLY CONSTRAINED FEATURE MATCHING
PERFORMANCE OF IMAGE RECOGNITION ALGORITHMS BY PRUNING FEATURES, IMAGE SCALING, AND SPATIALLY CONSTRAINED FEATURE MATCHING
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机译：通过修剪功能，图像缩放和空间受限的功能匹配来实现图像识别算法
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摘要
A method for feature matching in image recognition is provided. First, image scaling may be based on a feature distribution across scale spaces for an image to estimate image size/resolution, where peak(s) in the keypoint distribution at different scales is used to track a dominant image scale and roughly track object sizes. Second, instead of using all detected features in an image for feature matching, keypoints may be pruned based on cluster density and/or the scale level in which the keypoints are detected. Keypoints falling within high-density clusters may be preferred over features falling within lower density clusters for purposes of feature matching. Third, inlier-to-outlier keypoint ratios are increased by spatially constraining keypoints into clusters in order to reduce or avoid geometric consistency checking for the image.
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