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Computer Vision Metrics: Chapter Three (Part C)

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For Part B of Chapter Three, please click here.

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


Extended SDM Metrics

Extensions to the Haralick metrics have been developed by the author [26], primarily motivated by a visual study of SDM plots as shown in Figure 3-7. Applications for the extended SDM metrics include texture analysis, data visualization, and image recognition. The visual plots of the SDMs alone are valuable indicators of pixel intensity relationships, and are worth using along with histograms to get to know the data.

The extended SDM metrics include centroid, total coverage, low-frequency coverage, total power, relative Power, locus length, locus mean density, bin mean density, containment, linearity, and linearity strength. The extended SDM metrics capture key information that is best observed by looking at the SDM plots. In many cases the extended SDM metric are be computed four times, once for each SDM direction of 0, 45, 90, and 135 degrees, as shown in Figure 3-5.

The SDMs are interesting and useful all by themselves when viewed as an image. Many of the texture metrics suggested are obvious after viewing and understanding the SDMs; others are neither obvious nor apparently useful until developing a basic familiarity with the visual interpretation of SDM image plots. Next, we survey the following:

  • Example SDMs showing four directional SDM maps: A complete set of SDMs would contain four different plots, one for each orientation. Interpreting the SDM plots visually reveals useful information. For example, an image with a smooth texture will yield a narrow diagonal band of co-occurrence values; an image with wide texture variation will yield a larger spread of values; a noisy image will yield a co-occurrence matrix with outlier values at the extrema. In some cases, noise may only be distributed along one axis of the image—perhaps, across rows or the x axis, which could indicated sensor readout noise as each line is read out of the sensor, suggesting a row- or line-oriented image preparation stage in the vision pipeline to compensate for the camera.
  • Extended SDM texture metrics: The addition of 12 other useful statistical measures to those proposed by Haralick.
  • Some code snippets: These illustrate the extended SDM computations, full source code is shown in Appendix D.

In Figure 3-7, several of the extended SDM metrics can be easily seen, including...