@inproceedings {187, title = {Tensor vector field based active contours}, booktitle = {18th IEEE International Conference on Image Processing (ICIP 2011)}, year = {2011}, month = {09/2011}, abstract = {

Among the main limitations of active contours are their high noise sensitivity and poor capture range from the target object. One of the most promising approaches for addressing these limitations is the concept of Vector Field Convolution (VFC). However, due to its isotropic vector field kernel, VFC does not take full advantage of the underlying image structural characteristics. By specifically addressing this idea, a novel local tensor vector field approach is developed to adaptively account for these structural characteristics. Experimental results demonstrate that the proposed adaptive methodleads to more accurate segmentation.

}, keywords = {Active Contour, Segmentation, Snake, Tensor Vector Field, Vector Field Convolution}, author = {A Kumar and A Wong and A Mishra and D A. Clausi and P Fieguth} } @inproceedings {323, title = {Accurate boundary localization using dynamic programming on snakes}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2008}, month = {05/2008}, address = {Windsor, Ontario, Canada}, abstract = {

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 four other published methods and across six different images (two original, four published), the proposed method is demonstrated to be, on average, 7 times faster with a 45 percent reduction in the mean square error. Only the proposed method was able to successfully segment the desired object in each test image

}, keywords = {Curvature guided importance sampling, HMM, Snake, Statistical data fusion, Viterbi Algorithm}, doi = {http://dx.doi.org/10.1109/CRV.2008.13}, author = {A Mishra and P Fieguth and D A. Clausi} }