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Computer Vision Metrics: Chapter Three (Part B)

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For Part A of Chapter Three, please click here.

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


Statistical Methods

The topic of statistical methods is vast, and we can only refer the reader to selected literature as we go along. One useful and comprehensive resource is the online NIST National Institute of Science and Technology Engineering Statistics Handbook (See the NIST online resource for engineering statistics: http://www.itl.nist.gov/div898/handbook/), including examples and links to additional resources and tools.

Statistical methods may be drawn upon at any time to generate novel feature metrics. Any feature, such as pixel values or local region gradients, can be expressed statistically by any number of methods. Simple methods, such as the histogram shown in Figure 3-1, are invaluable. Basic statistics such as minimum, maximum, and average values can be seen easily in the histogram shown in Chapter 2 (Figure 2-22). We survey several applications of statistical methods to computer vision here.


Figure 3-1. Histogram with linear scale values (black) and log scale values (gray), illustrating how the same data is interpreted differently based on the chart scale

Texture Region Metrics

Now we look in detail at the specific metrics for feature description based on texture. Texture is one of the most-studied classes of metrics. It can be thought of in terms of the surface—for example, a burlap bag compared to silk fabric. There are many possible textural relationships and signatures that can be devised in a range of domains, with new ones being developed all the time. In this section we survey some of the most common methods for calculating texture metrics:

  • Edge metrics
  • Cross-correlation
  • Fourier spectrum signatures
  • Co-occurrence matrix, Haralick features, extended SDM features
  • Laws texture metrics
  • Tessellation
  • Local binary patterns (LBP)
  • Dynamic textures

Within an image, each image region has a texture signature, where texture is defined as a common structure and pattern within that region. Texture signatures may be a function of position and intensity relationships, as in the spatial domain, or be based...