@inproceedings {24, title = {Illumination Robust Facial Feature Detection via Decoupled Illumination and Texture Features}, booktitle = {International Conference on Image Analysis and Recognition 2015}, year = {2015}, abstract = {

A method for illumination robust facial feature detection on frontal images of the human face is proposed. Illumination robust features are produced from weighted contributions of the texture and illumination components of an image where the illumination is estimated via Bayesian least-squares minimization with the required posterior probability inferred using an adaptive Monte-Carlo sampling approach. This estimate is used to decouple the illumination and texture components, from which Haar-like features are extracted. A weighted aggregate of each component\&$\#$39;s features is then compared with a cascade of pre-trained classifiers for the face, eyes, nose, and mouth. Experimental results against the Yale Face Database B suggest higher sensitivity and F1 score values than current methods while maintaining comparable specificity and accuracy in the presence of non-ideal illumination conditions.

}, keywords = {illumination robust, image processing, object detection}, author = {B Chwyl and A Wong and D A. Clausi} } @article {171, title = {Decoupled active contour (DAC) for boundary detection}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {33}, year = {2011}, pages = {310 - 324}, abstract = {

The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.

}, keywords = {Computer Vision, decoupled active contour, Hidden Markov Model, hidden Markov models, image measurement, image segmentation, object boundary detection, object detection, search problems, segmentation accuracy, Viterbi optimizer, Viterbi search}, issn = {0162-8828}, doi = {http://dx.doi.org/10.1109/TPAMI.2010.83}, author = {A Mishra and P Fieguth and D A. Clausi} } @article {170, title = {Decoupled active contour (DAC) for boundary detection}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {33}, year = {2011}, pages = {310 - 324}, abstract = {

The accurate detection of object boundaries via active contours is an ongoing research topic in computer vision. Most active contours converge toward some desired contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. Such an approach is elegant, but suffers from a slow convergence rate and frequently misconverges in the presence of noise or complex contours. To address these limitations, a decoupled active contour (DAC) is developed which applies the two energy terms separately. Essentially, the DAC consists of a measurement update step, employing a Hidden Markov Model (HMM) and Viterbi search, and then a separate prior step, which modifies the updated curve based on the relative strengths of the measurement uncertainty and the nonstationary prior. By separating the measurement and prior steps, the algorithm is less likely to misconverge; furthermore, the use of a Viterbi optimizer allows the method to converge far more rapidly than energy-based iterative solvers. The results clearly demonstrate that the proposed approach is robust to noise, can capture regions of very high curvature, and exhibits limited dependence on contour initialization or parameter settings. Compared to five other published methods and across many image sets, the DAC is found to be faster with better or comparable segmentation accuracy.

}, keywords = {Computer Vision, decoupled active contour, Hidden Markov Model, hidden Markov models, image measurement, image segmentation, object boundary detection, object detection, search problems, segmentation accuracy, Viterbi optimizer, Viterbi search}, issn = {0162-8828}, doi = {http://dx.doi.org/10.1109/TPAMI.2010.83}, author = {A Mishra and P Fieguth and D A. Clausi} } @inproceedings {256, title = {Organ recognition using Gabor filters}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, abstract = {

The aim of this research is to investigate the possibility of using medical image information to extract unique features and classify different patients\&$\#$39; organ tissues, such as the prostate, based on concepts related to what is already done in iris recognition. This paper therefore presents a new approach in medical imaging, an organ recognition system, tested on a standard database of grey scale prostate images in order to validate its performance. In this research, features of the prostate image were encoded by convolving the normalized organ region with a 2D Gabor filter and then quantizing its phase in order to produce a bit-wise biometric template. Our experiments prove that prostate patterns have a low degree of freedom to be used in organ recognition systems and inter-class and intraclass distributions are highly correlated. However, there are still open issues that need to be addressed for future work on organ recognition, including precise segmentation and intensive computation cost.

}, keywords = {2D Gabor filter, bit wise biometric template, Feature Extraction, Gabor filters, grey scale prostate image, grey systems, medical image information, medical image processing, object detection, organ recognition system, patient treatment, patients organ tissues classification}, doi = {http://dx.doi.org/10.1109/CRV.2010.19}, author = {S Zaboli and A Tabibazar and P Fieguth} }