Active Contour Texture Segmentation in Modulus Wavelet Feature Spaces Jayawardena Ashoka author Computer Science ajayawa2@une.edu.au une-id:ajayawa2 Kwan Paul H author Computer Science wkwan2@une.edu.au une-id:wkwan2 In this paper we discuss a model that is able to segment textures using active contours. Our technique is based on active contour techniques using. curve evolution. We build our model on properties of human vision, in that we segment the textures in a certain feature space. We will show the advantages of using modulus feature spaces. Wavelet coefficients are shown to exhibit local features both in space and frequency domains. We will implement our model in modulus wavelet subbands. http://trove.nla.gov.au/work/171971860 en B1 080106 Image Processing Image Processing 080109 Pattern Recognition and Data Mining Pattern Recognition and Data Mining 080104 Computer Vision Computer Vision 890201 Application Software Packages (excl. Computer Games) 970108 Expanding Knowledge in the Information and Computing Sciences 970109 Expanding Knowledge in Engineering book chapter Innovations and Advances in Computer, Information, Systems Sciences, and Engineering Editor(s): Khaled Elleithy, Tarek Sobh New York, United States of America Springer 2013 1 9781461435341 9781461435358 10.1007/978-1-4614-3535-8_45 1 537 544 101 Lecture Notes in Electrical Engineering 152 1876-1100 1876-1119 vtls086642584 University of New England une-20121026-102130