An adaptive texture segmentation approach for applications in digital photogrammetry

Texture plays an important role in visual information processing since texture provides useful information about shape, orientation, and depth of the objects. The human visual system utilizes texture without difficulty as one of the visual cues for image interpretation, scene analysis and object recognition. However, to extract and to analyze texture are difficult tasks in machine perception including computer vision and digital photogrammetry. The ultimate goal of digital photogrammetry is to reconstruct surfaces automatically. Surface reconstruction from raw imagery is an ill-posed problem. In order to solve the ill-posed nature of surface reconstruction, it is crucial to use information from different visual cues.;In this study, an adaptive strategy was developed by using Gabor filters in order to extract texture information and to segment images. The Gabor filters are conceived as hypothetical structures of the retinal receptive fields. Therefore, to develop a texture analysis system which resembles the performance of human visual perception is possible using the Gabor filters A scheme to select appropriate parameters of the Gabor filters without human visual inspection was proposed. The entire framework is based on the theory of human texture perception which was introduced by Julesz (1981).;Various kinds of images were used to evaluate the performance of the proposed strategy. The results show that the proposed adaptive strategy improves performance of the Gabor filters for texture extraction and segmentation.