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Improving performance of image recognition algorithms by pruning features, image scaling, and spatially constrained feature matching
Improving performance of image recognition algorithms by pruning features, image scaling, and spatially constrained feature matching
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机译：通过修剪特征，图像缩放和空间受限的特征匹配来提高图像识别算法的性能
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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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