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PCA-SIFT
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Keypoint detection as Linux binary and modified matching program as source code from David Lowe. Works on PCA-SIFT keys and Lowe's; SIFT keys. Includes example images for matching.
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Dataset used in the experiments
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Dataset used in the experiments
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PCA-SIFT
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Keypoint detection as Linux binary and modified matching program as source code from David Lowe. Works on PCA-SIFT keys and Lowe's; SIFT keys. Includes example images for matching.
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Pca-sift: A More Distinctive Representation for Lo
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Stable local feature detection and representation is a fundamental component of many image registration and object recognition algorithms. Mikolajczyk and Schmid recently evaluated; a variety of approaches and identified the SIFT algorithm as being the most resistant to common image deformations. This paper examines (and improves upon) the local image descriptor used by SIFT. Like SIFT, our descriptors encode the salient aspects of the image gradient in the feature point's neighborhood however, instead of using; SIFT's smoothed weighted histograms, we apply Principal Components Analysis (PCA) to the normalized gradient patch. Our experiments demonstrate that the; PCA-based local descriptors are more distinctive, more Robust to image deformations, and more compact than the standard sift representation. we also present results showing that using these descriptors in an image retrieval application results in increased accuracy and faster matching.
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