000125037 001__ 125037
000125037 005__ 20190316234306.0
000125037 02470 $$2DAR$$a13394
000125037 02470 $$2ISI$$a000259736800109
000125037 037__ $$aCONF
000125037 245__ $$aFast Texture Segmentation Model based on the Shape Operator and Active Contour
000125037 260__ $$c2008
000125037 269__ $$a2008
000125037 336__ $$aConference Papers
000125037 520__ $$aWe present an approach for unsupervised segmentation of natural and textural images based on active contour, differential geometry and information theoretical concept. More precisely, we propose a new texture descriptor which intrinsically defines the geometry of textural regions using the shape operator borrowed from differential geometry. Then, we use the popular Kullback-Leibler distance to define an active contour model which distinguishes the background and textural objects of interest represented by the probability density functions of our new texture descriptor. We prove the existence of a solution to the proposed segmentation model. Finally, a fast and easy to implement texture segmentation algorithm is introduced to extract meaningful objects. We present promising synthetic and real-world results and compare our algorithm to other state-of-the-art techniques.
000125037 6531_ $$aLTS5
000125037 6531_ $$aTexture Segmentation
000125037 6531_ $$aDifferential Geometry
000125037 6531_ $$aActive Contours
000125037 700__ $$aHouhou, Nawal
000125037 700__ $$0240323$$g115534$$aThiran, Jean-Philippe
000125037 700__ $$0241065$$g140163$$aBresson, Xavier
000125037 7112_ $$dJune 24-26,2008$$cAnchorage$$aComputer Vision and Pattern Recognition
000125037 773__ $$tComputer Vision and Pattern Recognition
000125037 8564_ $$zURL
000125037 8564_ $$uhttps://infoscience.epfl.ch/record/125037/files/PID612190.pdf$$zn/a$$s959946
000125037 909C0 $$xU10954$$0252394$$pLTS5
000125037 909CO $$ooai:infoscience.tind.io:125037$$qGLOBAL_SET$$pconf$$pSTI
000125037 937__ $$aEPFL-CONF-125037
000125037 973__ $$rREVIEWED$$sPUBLISHED$$aEPFL
000125037 980__ $$aCONF