@article {133, title = {A robust probabilistic Braille recognition system}, journal = {International Journal of Document Analysis and Recognition}, volume = {15}, year = {2012}, pages = {253 - 266}, abstract = {

As a structured document, Braille is the most common means of reading and study for visually handicapped people. The need for converting Braille documents into a computer-readable format has motivated research into the implementation of Braille recognition systems. The main theme of this research is to propose robust probabilistic approaches to different steps of Braille Recognition. The method is meant to be very general in terms of being independent of those parameters of the Braille document such as skewness, scale, and spacing of the page, lines, and characters. For a given Braille document, a statistical method is proposed for estimating the scaling, spacing, and skewness parameters, whereby the detected dots of the Braille document are modeled using a parameterized probability density function. Skewness, scaling, and line spacing are estimated as a solution of a maximum-likelihood (ML) problem, which is solved using expectation maximization. Based on those parameters, each line of the Braille document is extracted, and each of three rows of individual lines is separated based on the vertical projection of the Braille dots. Finally, a scale independent automatic document gridding procedure is proposed for dot localization and character detection based on a hidden Markov model.

}, keywords = {Braille, Expectation maximization, Hidden Markov Model, Line spacing, Scale independence, Structured document}, author = {M Yousefi and M Famouri and B Nasihatkon and Z Azimifar and P Fieguth} } @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} } @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} } @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} } @inproceedings {762, title = {A multimodal variational approach to learning and inference in switching state space models}, booktitle = {37th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP)}, year = {2004}, address = {Montreal}, abstract = {

An important general model for discrete-time signal processing is the switching state space (SSS) model, which generalizes the hidden Markov model and the Gaussian state space model. Inference and parameter estimation in this model are known to be computationally intractable. This paper presents a powerful new approximation to the SSS model. The approximation is based on a variational technique that preserves the multimodal nature of the continuous state posterior distribution. Furthermore, by incorporating a windowing technique, the resulting EM algorithm has complexity that is just linear in the length of the time series. An alternative Viterbi decoding with frame-based likelihood is also presented which is crucial for the speech application that originally motivates this work. Our experiments focus on demonstrating the effectiveness of the algorithm by extensive simulations. A typical example in speech processing is also included to show the potential of this approach for practical applications.

}, keywords = {continuous state posterior distribution, discrete time systems, discrete-time signal processing, EM algorithm, frame based representation, frame-based likelihood Viterbi decoding, Gaussian processes, Gaussian state space model, Hidden Markov Model, hidden Markov models, inference, learning, learning (artificial intelligence), model-based reasoning, multimodal variational technique, parameter estimation, speech processing, SSS model approximation, state-space methods, switching state space models, time series, variational techniques, Viterbi decoding, windowing technique}, doi = {http://dx.doi.org/10.1109/ICASSP.2004.1327158}, author = {L J. Lee and H Attias and L Deng and P Fieguth} }