Texture is a fundamental characteristic in many natural images that, in addition to color, plays an important role in human visual perception and in turn provides information for image understanding and scene interpretation. Multiresolution simultaneous autoregressive models (MSAR) may be viewed as texture features that can be used for image segmentation The MSAR coefficients at different resolution levels are obtained from the respective level of a Gaussian pyramid, and are normalized before clustering them for segmentation. In this paper, we discuss an improved version of MSAR texture segmentation derived by (a) the method used for the construction of the multiresolution image pyramid whose levels are used for calculating the higher level coefficients, and (b) the coefficient normalization process designed to emphasize the information that is most important in the segmentation process.

Publication Date



"Texture-based segmentation of natural images using multiresolution autoregressive models," Presented at the 1998 IEEE Western New York Image Processing Workshop. The Institute of Electrical and Electronics Engineers. Held at the University of Rochester: Rochester, New York: September 1998. ©1998 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.Note: imported from RIT’s Digital Media Library running on DSpace to RIT Scholar Works in February 2014.

Document Type


Department, Program, or Center

Chester F. Carlson Center for Imaging Science (COS)


RIT – Main Campus