A study on discrimination of SIFT feature applied to binary images
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Date
2015-10-04
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CERIST
Abstract
Scale Invariant Feature Transform (SIFT) since its first apparition in 2004 has been (and still
is) extensively used in computer vision to classify and match objects in RGB and grey level images and videos.
However, since the descriptor used in SIFT approach is based on gradient magnitude and orientation, it has
always been considered as texture feature and received less interest when treating binary images. In this work
we investigate the power of discrimination of SIFT applied to binary images. A theoretical and experimental
studies show that SIFT can still describe shapes and can be used to distinguish objects of several classes.
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Keywords
Classification, Matching, SIFT, Shape discrimination