Texture analysis based on a human visual model

Abstract

A texture analysis approach superior to previous ones in such aspects as classification/segmentation performance and applicability is presented. It is based on a widely adopted human visual model which hypothesizes that the human visual system (HVS) processes input pictorial signals through a set of parallel and quasi-independent mechanisms or channels. This model is referred to as the multichannel spatial filtering model (MSFM). The core of the MSFM presently applied is the recently formulated cortical channel model (CCM), which attempts to model the process of texture feature extraction in each individual channel in the MSFM. With these models, successful algorithms for both texture classification and segmentation (texture edge detection) have been developed. The algorithm for texture feature extraction and classification is compared with the conventional benchmark, i.e., the gray-level cooccurrence matrix approach, and proves to be superior in many aspects. The algorithm for texture edge detection is tested under a variety of textured images, and good segmentation results are obtained.<>