In this paper, we propose a variational model for object segmentation using the active contour method, a geometric shape prior and the Mumford-Shah functional. We propose an energy functional composed by three terms: the first one is based on image gradient, which detects edges, the second term constrains the active contour to get a shape compatible with a statistical model of the target shape,...

Constrained local models (CLM) are frequently used to locate points on deformable objects. They usually consist of feature response images, defining the local update of object points and a shape prior used to regularize the final shape. Due to the complex shape variation within an object class this is a challenging problem. However in many segmentation tasks a simpler object representation is a...

In perceiving 3D shape from ambiguous shading patterns, humans use the prior knowledge that the light is located above their head and slightly to the left. Although this observation has fascinated scientists and artists for a long time, the neural basis of this "light from above left" preference for the interpretation of 3D shape remains largely unexplored. Combining behavioral and functional M...

Deformable shape models require correspondence across the training population in order to generate a statistical model for use as a future geometric prior. Traditional methods use fixed sampling and assume correspondence, or attempt to induce correspondence by minimizing variance. In this paper, we define a training methodology for sampled medial deformable shape models (m-reps) which generates...

We present a variational PDE based model for tracking objects under occlusion. Here, prior shape information is used to segment object boundaries that are occluded. The novelty in this work is that the shape prior is combined with the image term using logical operations pertaining to a unique occlusion scenario, thus avoiding locally optimal solutions. The model was tested on real and synthetic...