000087046 001__ 87046
000087046 005__ 20190316233746.0
000087046 037__ $$aREP_WORK
000087046 245__ $$aA Variational Model for Object Segmentation Using Boundary Information, Statistical Shape Prior and the Mumford-Shah Functional
000087046 269__ $$a2004
000087046 260__ $$c2004
000087046 336__ $$aReports
000087046 520__ $$aIn this paper, we propose a variational model to segment an object belonging to a given scale space using the active contour method, a geometric shape prior and the Mumford-Shah functional. We define an energy functional composed by three complementary terms. The first one detects object boundaries from image gradients. The second term constrains the active contour to get a shape compatible with a statistical shape model of the shape of interest. And the third part drives globally the shape prior and the active contour towards a homogeneous intensity region. The segmentation of the object of interest is given by the minimum of our energy functional. This minimum is computed with the calculus of variations and the gradient descent method that provide a system of evolution equations solved with the well-known level set method. We also prove the existence of this minimum in the space of functions with bounded variation. Applications of the proposed model are presented on synthetic and medical images.
000087046 6531_ $$aLTS2
000087046 6531_ $$alts5
000087046 700__ $$0241065$$g140163$$aBresson, X.
000087046 700__ $$0240428$$g120906$$aVandergheynst, P.
000087046 700__ $$aThiran, J.$$g115534$$0240323
000087046 8564_ $$uhttps://infoscience.epfl.ch/record/87046/files/Bresson2004_759.pdf$$zn/a$$s1084208
000087046 909C0 $$xU10380$$0252392$$pLTS2
000087046 909C0 $$pLTS5$$xU10954$$0252394
000087046 909CO $$qGLOBAL_SET$$pSTI$$preport$$ooai:infoscience.tind.io:87046
000087046 937__ $$aEPFL-REPORT-87046
000087046 970__ $$aBresson2004_759/LTS
000087046 973__ $$sPUBLISHED$$aEPFL
000087046 980__ $$aREPORT