Robust snake convergence based on dynamic programming

TitleRobust snake convergence based on dynamic programming
Publication TypeConference Paper
Year of Publication2008
AuthorsMishra, A., P. Fieguth, and D. A. Clausi
Conference Name15th IEEE International Conference on Image Processing
Date Published10/2008
Conference LocationSan Diego, California, USA
KeywordsActive Contour, Computer Vision, contour extraction, deformable model, deterministic iterative statistical data fusion approach, discrete snake, dynamic programming, edge detection, Feature Extraction, Hidden Markov Model, hidden Markov models, HMM, importance sampling, robust snake convergence, Viterbi detection, Viterbi search

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 dramatic speed and accuracy improvement compared to other methods.