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Computer Vision Metrics: Chapter Six (Part B)

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For Part A of Chapter Six, please click here.

Bibliography references are set off with brackets, i.e. "[XXX]". For the corresponding bibliography entries, please click here.


SUSAN, and Trajkovic and Hedly

The SUSAN method [164,165] is dependent on segmenting image features based on local areas of similar brightness, which yields a bimodal valued feature. No noise filtering and no gradients are used. As shown in Figure 6-3, the method works by using a center nucleus pixel value as a comparison reference against which neighbor pixels within a given radius region are compared, yielding a set of pixels with similar brightness, called a Univalue Segment Assimilating Nucleus (USAN).


Figure 6-3. SUSAN method of computing interest points. The dark region of the image is a rectangle intersecting USAN’s A, B, and C. USAN A will be labeled as an edge, USAN B will be labeled as a corner, and USAN C will be labeled as neither an edge nor a corner

Each USAN contains structural information about the image in the local region, and the size, centroid, and second-order moments of each USAN can be computed. The SUSAN method can be used for both edge and corner detection. Corners are determined by the ratio of pixels similar to the center pixel in the circular region: a low ratio around 25 percent indicates a corner, and a higher ratio around 50 percent indicates an edge. SUSAN is very robust to noise.

The Trajkovic and Hedly method [214] is similar to SUSAN, and discriminates among points in USAN regions, edge points, and corner points.

SUSAN is also useful for noise suppression, and the bilateral filter [302], discussed in Chapter 2, is closely related to SUSAN. SUSAN uses fairly large circular windows; several implementations use 37 pixel radius windows. The FAST [138] detector is also similar to SUSAN, but uses a smaller 7x7 or 9x9 window and only some of the pixels in the region instead of all of them; FAST yields a local binary descriptor.

Fast, Faster, AGHAST

The FAST methods [138] are derived from SUSAN with respect to a bimodal segmentation goal. However, FAST relies on a connected set of pixels in a circular pattern to determine a corner. The connected region size is commonly 9 or 10 out of a possible 16; either number may be chosen, referred to as FAST9 and FAST10. FAST is known to be efficient to compute and fast to match; accuracy is also...