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Erschienen in:
2010 | OriginalPaper | Buchkapitel
Non-maximum Suppression Using Fewer than Two Comparisons per Pixel
verfasst von : Tuan Q. Pham
Erschienen in: Advanced Concepts for Intelligent Vision Systems
Verlag: Springer Berlin Heidelberg
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Non-Maximum Suppression (NMS) is the task of finding all local maxima in an image. This is often solved using gray-scale image dilation, which requires at least 6 comparisons per pixel in 2-D. We present two solutions that use fewer than 2 comparisons per pixel with little memory overhead. The first algorithm locates 1-D peaks along the image’s scan-line and compares each of these peaks against its 2-D neighborhood in a spiral scan order. The second algorithm selects local maximum candidates from the maxima of non-overlapping blocks of one-fourth the neighborhood size. Both algorithms run considerably faster than current best methods in the literature when applied to feature point detection. Matlab code of the proposed algorithms is provided for evaluation purposes.