Active contour segmentation guided by AM-FM dominant component analysis
For the first time, we explore the application of active contours in the modulation domain by computing snakes on image modulations. As we demonstrate in the examples, such snakes are able to utilize information inherent in the dominant image modulations to acquire and track visually and semantically meaningful structures within the image. We use nonlinear AM-FM image representations to capture regions that are homogeneous in intensity and in texture. A geometric snake approach utilizing a fuzzy classifier is then applied to the image modulations. The combination of AM-FM analysis and the active contour evolution produces an efficacious image partition. As a preliminary demonstration of this novel approach, we apply the modulation domain snakes to the classical texture segmentation problem.