Texture aware image segmentation using graph cuts and active contours Zhou, Hailing, Zheng, J and Wei, Lei 2013, Texture aware image segmentation using graph cuts and active contours, Pattern recognition, vol. 46, no. 6, pp. 1719-1733, doi: 10.1016/j.patcog.2012.12.005. Attached Files Name Description MIMEType Size Downloads Title Texture aware image segmentation using graph cuts and active contours Author(s) Zhou, Hailing orcid.org/0000-0001-5009-4330 Zheng, J Wei, Lei orcid.org/0000-0001-8267-0283 Journal name Pattern recognition Volume number 46 Issue number 6 Start page 1719 End page 1733 Total pages 15 Publisher Elsevier Place of publication Amsterdam, The Netherlands Publication date 2013-06 ISSN 0031-3203 1873-5142 Keyword(s) interactive image segmentation graph cut active contour color-texture structure tensor Science & Technology Technology Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Computer Science Engineering MINIMIZATION FRAMEWORK GRABCUT MODEL Summary The problem of segmenting a foreground object out from its complex background is of great interest in image processing and computer vision. Many interactive segmentation algorithms such as graph cut have been successfully developed. In this paper, we present four technical components to improve graph cut based algorithms, which are combining both color and texture information for graph cut, including structure tensors in the graph cut model, incorporating active contours into the segmentation process, and using a “softbrush” tool to impose soft constraints to refine problematic boundaries. The integration of these components provides an interactive segmentation method that overcomes the difficulties of previous segmentation algorithms in handling images containing textures or low contrast boundaries and producing a smooth and accurate segmentation boundary. Experiments on various images from the Brodatz, Berkeley and MSRC data sets are conducted and the experimental results demonstrate the high effectiveness of the proposed method to a wide range of images Language eng DOI 10.1016/j.patcog.2012.12.005 Field of Research 080109 Pattern Recognition and Data Mining 080106 Image Processing 0899 Other Information And Computing Sciences 0906 Electrical And Electronic Engineering 0801 Artificial Intelligence And Image Processing Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal Copyright notice ©2012, Elsevier Persistent URL http://hdl.handle.net/10536/DRO/DU:30050994 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to published version (restricted access) Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 05 Mar 2013, 15:18:06 EST Tue, 05 Mar 2013, 15:29:32 EST Thu, 14 Nov 2013, 14:50:42 EST Fri, 15 Nov 2013, 07:42:20 EST Tue, 17 Dec 2013, 12:55:42 EST Fri, 13 Jun 2014, 16:53:13 EST Thu, 09 Oct 2014, 10:16:57 EST Fri, 09 Feb 2018, 14:22:09 EST Filtered Full Citation counts:   Cited 55 times in TR Web of Science Cited 76 times in Scopus Search Google Scholar Access Statistics: 810 Abstract Views, 4 File Downloads  -  Detailed Statistics Created: Tue, 05 Mar 2013, 15:18:06 EST