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Computer Vision Metrics: Chapter Five (Part C)

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For Part B of Chapter Five, please click here.

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


Feature Pattern

Feature pattern is a major axis in this taxonomy, as shown in Figure 5-3, since it affects memory architecture and compute efficiency.


Figure 5-3. Feature shapes. (Left to right) Rectangular patch, symmetric polygon region, irregular segmented region, and volumetric region

Feature shape and pattern are related. Shape refers to the boundary, and pattern refers to the sampling method. Patterns include:

  • Rectangular kernel: some methods use a kernel to define which elements in the region are included in the sample; see Figure 5-3 (left image) showing a kernel that does not use the corner pixels in the region; see also Figure 4-10.
  • Binary compare pattern: such as FREAK, ORB, and BRISK, where specific pixels in a region are paired to form a complex sampling pattern.
  • DNET line sample strip set: where points along a line segment are sampled densely; see Figure 4-8.
  • Radial line sampling pattern: where points on radial line segments originating at a center point are sampled densely; for example, used to compute Fourier descriptors for polygon region shape; see Figure 6-29.
  • Perimeter or contour edge: where points around the edge of a shape or region are sampled densely.
  • Sample weighting pattern: as shown in Figure 6-17, SIFT uses a circular weighting pattern in the histogram bins to decrease the contribution of points farther away from the center of the patch. The D-NETS method uses binary weighting of samples along the line strips, favoring points away from the endpoints and ignoring points close to the end points. Weighting patterns can provide invariance to noise and occlusion.

See Chapter 4 for more illustrations in the section on patches and shapes.

Feature Density

As shown in Figure 5-1, feature density is a major axis in this taxonomy. The amount of the image used for the descriptor is referred to in this taxonomy as feature density. For example, some descriptors are intended to use smaller regions of local pixels, anchored at interest points, and to ignore the...