This paper develops a new technique for modeling and classifying a growing texture using its evolution function over time. It encompasses morphological texture classification and parameter estimation with the objective of assessing the state of growth achieved by the texture using only a small sample set to train on, consistent with many real world situations for quality control. It is assumed that the texture model evolves over time according to the way in which its evolution function determines the parameters of its defining random process. This paper considers the random Boolean model for both binary and gray-scale images. A multiple linear regression model is used to estimate the Boolean model parameters as functions of the granulometric moments of the textures. Once the texture-model parameters are estimated, the time of the process can be found via the manner in which the parameters are determined by the dynamic evolutionary model.

Some issues are considered which arise when edge detection is used to identify cells in differential interference contrast (DIC) microscope images. A method for outlining the cells in such images is developed and a simple practical example illustrated. Edge detection is complex due to the nature of DIC images, in which an apparent light effect is captured across the image. Further complications arise due to differing cell sizes and orientations. The method presented compensates for these problems. 1 Motivation The work described here results from a need in research into high rate algal ponds, an environmentally important development in applied microbiology. These are simple, energy efficient, low-technology waste treatment systems (Fallowfield and Martin [1]). Achieving optimum efficiency of such systems relies on knowledge of the biomass of algae and bacteria in the mixed microbial populations of the pond. This is determined by viewing pond samples under a microscope, countin...

Markov random fields are typically used as priors in Bayesian image restoration methods to represent spatial information in the image. Commonly used Markov random fields are not in fact capable of representing the moderate-to-large scale clustering present in naturally occurring images and can also be time consuming to simulate, requiring iterative algorithms which can take hundreds of thousands of sweeps of the image to converge. Markov mesh models, a causal subclass of Markov random fields, are, however, readily simulated. We describe an empirical study of simulated realizations from various models used in the literature, and we introduce some new mesh-type models. We conclude, however, that while large-scale clustering may be represented by such models, strong directional effects are also present for all but very limited parameterizations. It is emphasized that the results do not detract from the use of Markov random fields as representers of local spatial properties, which is their main purpose in the implementation of Bayesian statistical approaches to image analysis. Brief allusion is made to the issue of parameter estimation.>