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Computer Vision Metrics: Chapter Six (Part H)

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For Part G of Chapter Six, please click here.

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


Object Shape Metrics for Blobs and Polygons

Object shape metrics are powerful and yield many degrees of freedom with respect to invariance and robustness. Object shape metrics are not like local feature metrics, since object shape metrics can describe much larger features. This is advantageous for tracking from frame to frame. For example, a large object described by just a few simple object shape metrics such as area, perimeter, and centroid can be tracked from frame to frame under a wide range of conditions and invariance. For more information, see references [128,129] for a survey of 2D shape description methods.

Shape can be described by several methods, including:

  • Object shape moments and metrics: the focus of this section.
  • Image moments: see Chapter 3 under “Image Moments.”
  • Fourier descriptors: discussed in this chapter and Chapter 3.
  • Shape Context feature descriptor: discussed in this section.
  • Chain code descriptor for perimeter description: discussed in this section.

Object shape is closely related to the field of morphology, and computer methods for morphological processing are discussed in detail in Chapter 2. Also see the discussion about morphological interest points earlier in this chapter.

In many areas of computer vision research, local features seem to be favored over object shape-based features. The lack of popularity of shape analysis methods may be a reaction to the effort involved in creating pre-processing pipelines of filtering, morphology, and segmentation to prepare the image for shape analysis. If the image is not pre-processed and prepared correctly, shape analysis is not possible. (See Chapter 8 for a discussion of a hypothetical shape analysis pre-processing pipeline.)

Polygon shape metrics can be used for virtually any scene analysis application to find common objects and take accurate measurements of their size and shape; typical applications include biology and manufacturing. In general, most of the polygon shape metrics are rotational and scale invariant. Table 6-7 provides a sampling of some of the common metrics that can be derived from region shapes, both binary shapes and gray scale shapes.

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