Target region location based on texture analysis and active contour model

Abstract

Traditional texture region location methods with Gabor features are often limited in the selection of Gabor filters and fail to deal with the target which contains both texture and non-texture parts. Thus, to solve this problem, a two-step new model was proposed. In the first step, the original features extracted by Gabor filters are applied to training a self-organizing map (SOM) neural network and a novel merging scheme is presented to achieve the clustering. A back propagation (BP) network is used as a classifier to locate the target region approximately. In the second step, Chan-Vese active contour model is applied to detecting the boundary of the target region accurately and morphological processing is used to create a connected domain whose convex hull can cover the target region. In the experiments, the proposed method is demonstrated accurate and robust in localizing target on texture database and practical barcode location system as well.