@inproceedings {156, title = {Multi-scale tensor vector field active contour}, booktitle = {IEEE Conference on Image Processing}, year = {2012}, abstract = {

Two major challenges faced in active contours are poor capture range and high sensitivity towards noise. Recently, the concept of tensor vector convolution (TVF) was introduced and shown to be promising in handling these challenges. However, in the presence of high noise levels, TVF may have difficulty in converging to the desired object boundary, particularly if the distance is great between the initial contour and the object boundary. To tackle this challenge, the concept of a multi-scale tensor vector field (MTVF) active contour is introduced to further reduce noise sensitivity. Comparing the performance of MTVF with multi-scale gradient vector field and multi-scale vector field convolution demonstrates that MTVF is more resilient to high noise levels as well as significantly reducing computation time

}, keywords = {Active Contour, multi-scale active contour, Multi-scale tensor vector field, Segmentation, Tensor Vector Field}, author = {A Kumar and A Wong and D A. Clausi and P Fieguth} } @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 {334, title = {Illumination invariant active contour-based segmentation using complex-valued wavelets}, booktitle = {15th IEEE International Conference on Image Processing}, year = {2008}, abstract = {

This paper introduces a novel approach to the problem of active contour-based segmentation through the use of complex- valued wavelets. In traditional active contour-based segmentation techniques based on level set methods, the energy functionals are defined based on intensity gradients. This makes them highly sensitive to situations where the underlying image content is characterized by image non-homogeneities due to illumination and contrast conditions. In the proposed approach, the energy functionals used to evolve a level set function are based on the moments of phase coherence of complex-valued wavelet components. This formulation is highly invariant to non-homogeneities caused by illumination and contrast variations. Experimental results demonstrate that the proposed approach can be used to improve existing active contour-based segmentation methods under situations characterized by image non-homogeneities.

}, keywords = {Active Contour, complex valued wavelets, Segmentation}, author = {A Wong} } @inproceedings {322, title = {Robust snake convergence based on dynamic programming}, booktitle = {15th IEEE International Conference on Image Processing}, year = {2008}, month = {10/2008}, address = {San Diego, California, USA}, 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 other methods.

}, keywords = {Active 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}, doi = {http://dx.doi.org/10.1109/ICIP.2008.4711949}, author = {A Mishra and P Fieguth and D A. Clausi} }