Accurate Boundary Localization using Dynamic Programming on Snakes

The extraction of contours using deformable models, such as snakes, is a problem of great interest in computer vision, particular in areas of medical imaging and tracking. Snakes have been widely studied, and many methods are available. In most cases, the snake converges towards the optimal contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. This approach is elegant, but frequently mis-converges in the presence of noise or complex contours. To address these limitations, a novel discrete snake is proposed which treats the two energy terms separately. Essentially, the proposed method is a deterministic iterative statistical data fusion approach, in which the visual boundaries of the object are extracted, ignoring any prior, employing a Hidden Markov Model(HMM) and Viterbi search, and then applying importance sampling to the boundary points, on which the shape prior is asserted. The proposed implementation is straightforward and achieves dramati...

• Type: Conference
• Year: 2008
• Where: CRV
• Authors: Akshaya Kumar Mishra, Paul W. Fieguth, David A. Clausi
• Keywords: CRV 2008, Deformable Models, Energy Terms, Iterative Statistical Data, Robotics