Introductory remarks

In this chapter the methodology presented in Chapters 3 and 4 is extended to include processing of images using radial masks. The approach produces higher power (improved detectability) in most image processing operations at a small cost of increase in processing time. Also, the radial processing masks are less sensitive to the degree of correlation of the background noise.

Because of the similarity of some of the mathematical developments, many of the details in describing radial mask operations are omitted. The analysis involves the Markov noise model with the general results easily reduced to the independent noise case by replacing the Markov dependence covariance matrix with a diagonal matrix.

In the first part of the chapter, the potential features (line or edge elements) are extracted using a radial version of the masks for the one-way designs. Next, the symmetrical incomplete block design (SBIB) technique is generalized to include radial processing. This results in improvement in the feature extraction process for a fixed alarm rate.

The contrast function approach is also extended to include radial masking techniques. The algorithm is capable of detecting potential features and their locations simultaneously. The decision threshold is determined by the variance of the contrast function and the correlation coefficient of the noise.