Using a genetic algorithm to adapt 1D nonlinear matched sieves for pattern classification in images

摘要

Abstract: Many methods have been developed to recognize objects in a scene; most involving a preprocessing step to extract local information from the image of the scene. The non-linear sieve decomposition has already been shown to be a successful low-level process in machine vision. Matched sieves, where the local granularity is compared to that of a template, are effective for locating and rejecting non-matching signals. A single example of the object to be located is used to build a granularity template. This is unnecessarily restrictive since there is no generalization over a training set of target patterns, nor is the template modified to account for granules that, because of noise, do not contribute to the classification process. This paper addresses the next step towards an automatic classifier based upon the sieve decomposition. A genetic algorithm is used to configure a population of templates. These templates are evaluated at every cycle in order to generalize the population over a series of target patterns, whilst rejecting noise. !15