Fast Minimization of Region-Based Active Contours Using the Shape Hessian of the Energy

We propose a novel shape optimization algorithm for region-based active contour models. Region-based active contours are preferred for many segmentation problems, because they incorporate more global information by aggregating cues or statistics over the distinct regions defined by the contour configuration. This makes them effective in a diverse array of segmentation scenarios, also more robust to contour initializations, Unfortunately they are also more expensive computationally, because a significant part of the optimization involves repeated integrations of the image features over the regions through the many iterations of the contour updates. Accordingly, we aim to decrease the overall computational cost of region-based active contours by reducing the cost of an individual iteration, and the total number of iterations. To this end, we first develop a Lagrangian curve representation that is spatially adaptive and economical in terms of the number of nodes used. Then we perform ...

• Type: Journal
• Year: 2015
• Where: SCALESPACE
• Authors: Günay Dogan
• Keywords: Computer Vision, SCALESPACE 2015