GEODESIC ACTIVE CONTOURS

A novel scheme for the detection of object boundaries is presented. Th e technique is based on active contours evolving in time according to intrinsic geometric measures of the image. The evolving contours natur ally split and merge, allowing the simultaneous detection of several o bjects and both interior and exterior boundaries. The proposed approac h is based on the relation between active contours and the computation of geodesics or minimal distance curves. The minimal distance curve l ays in a Riemannian space whose metric is defined by the image content . This geodesic approach for object segmentation allows to connect cla ssical ''snakes'' based on energy minimization and geometric active co ntours based on the theory of curve evolution. Previous models of geom etric active contours are improved, allowing stable boundary detection when their gradients suffer from large variations, including gaps. Fo rmal results concerning existence, uniqueness, stability, and correctn ess of the evolution are presented as well. The scheme was implemented using an efficient algorithm for curve evolution. Experimental result s of applying the scheme to real images including objects with holes a nd medical data imagery demonstrate its power. The results may be exte nded to 3D object segmentation as well.