@inproceedings {20, title = {Sparse Reconstruction of Compressed Sensing Multi-spectral Data using Cross-Spectral Multi-layered Conditional Random Field Model}, booktitle = {SPIE optics and photonics 2015}, year = {2015}, address = {San Diego}, abstract = {

The broadband spectrum contains more information than what the human eye can detect. Spectral information from different wavelengths can provide unique information about the intrinsic properties of an object. Recently compressed sensing imaging systems with low acquisition time have been introduced. To utilize compressed sensing strategies, strong reconstruction algorithms that can reconstruct a signal from sparse observations are required. This work proposes a Cross-Spectral Multi-layerd Conditional Random Field(CS-MCRF) approach for sparse reconstruction of multi-spectral compressive sensing data in multi-spectral stereoscopic vision imaging systems. The CS-MCRF will use information between neighboring spectral bands to better utilize available information for reconstruction. This method was evaluated using simulated compressed sensing multi-spectral imaging data. Results show improvement over existing techniques in preserving spectral fidelity while effectively inferring missing information from sparsely available observations.

}, keywords = {Compressive Sensing, Computer Vision, Graphical Models, Multi-spectral Imaging, Sparse Reconstruction}, author = {E Li and M J. Shafiee and F Kazemzadeh and A Wong} } @inproceedings {140, title = {Anti-Trap Protection for an Intelligent Smart Car Door System}, booktitle = {Conference on Intelligent Transportation Systems}, year = {2012}, month = {09/2012}, keywords = {Computer Vision, Driver Assistance Systems, Safety System}, author = {C Scharfenberger and S Chakraborty and J S. Zelek and D A. Clausi} } @inproceedings {141, title = {Motion Stereo-based Collision Avoidance for an Intelligent Smart Car Door System}, booktitle = {Conference on Intelligent Transportation Systems}, year = {2012}, month = {09/2012}, keywords = {Computer Vision, Driver Assistance Systems, Safety System}, author = {C Scharfenberger and S Chakraborty and J S. Zelek and D A. Clausi} } @inproceedings {144, title = {Robust Body-Height Estimation for Applications in Automotive Industry}, booktitle = {9th Conference on Computer and Robot Vision}, year = {2012}, month = {05/2012}, keywords = {Computer Vision, Driver Assistance Systems, Height Estimation}, author = {C Scharfenberger and J S. Zelek and D A. Clausi} } @inproceedings {193, title = {Autocalibration: Finding infinity in projective reconstructions}, booktitle = {8th Canadian Conference on Computer and Robot Vision}, year = {2011}, month = {05/2011}, address = {St. Johns, Newfoundland}, abstract = {

Reconstruction of porous media images is required in order to study different properties of these material. Our research interest is on generating samples from the posterior model in which low resolution measurements are combined with a prior model. The reconstruction task becomes intractable when the size of the samples increases, since it is based on simulated annealing which is a slow convergence algorithm. The hierarchical approaches have been applied to tackle this problem, in the case of sampling from the prior model. However, in the posterior sampling case, defining a suitable measurement model at each scale still remains a challenging task. In this paper we define how we can incorporate the measurement in the hierarchical posterior model and then how we generate samples from that model.

}, keywords = {3D reconstruction, autocalibration, Computer Vision}, author = {N Cavan 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} } @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} } @mastersthesis {217, title = {Reconstruction of 3D Points From Uncalibrated Underwater Video}, year = {2011}, school = {University of Waterloo}, type = {M.A.Sc Thesis}, address = {Waterloo, ON}, abstract = {

This thesis presents a 3D reconstruction software pipeline that is capable of generating point cloud data from uncalibrated underwater video. This research project was undertaken as a partnership with 2G Robotics, and the pipeline described in this thesis will become the 3D reconstruction engine for a software product that can generate photo-realistic 3D models from underwater video. The pipeline proceeds in three stages: video tracking, projective reconstruction, and autocalibration. Video tracking serves two functions: tracking recognizable feature points, as well as selecting well-spaced keyframes with a wide enough baseline to be used in the reconstruction. Video tracking is accomplished using Lucas-Kanade optical flow as implemented in the OpenCV toolkit. This simple and widely used method is well-suited to underwater video, which is taken by carefully piloted and slow-moving underwater vehicles. Projective reconstruction is the process of simultaneously calculating the motion of the cameras and the 3D location of observed points in the scene. This is accomplished using a geometric three-view technique. Results are presented showing that the projective reconstruction algorithm detailed here compares favourably to state-of-the-art methods. Autocalibration is the process of transforming a projective reconstruction, which is not suitable for visualization or measurement, into a metric space where it can be used. This is the most challenging part of the 3D reconstruction pipeline, and this thesis presents a novel autocalibration algorithm. Results are shown for two existing cost function-based methods in the literature which failed when applied to underwater video, as well as the proposed hybrid method. The hybrid method combines the best parts of its two parent methods, and produces good results on underwater video. Final results are shown for the 3D reconstruction pipeline operating on short under- water video sequences to produce visually accurate 3D point clouds of the scene, suitable for photorealistic rendering. Although further work remains to extend and improve the pipeline for operation on longer sequences, this thesis presents a proof-of-concept method for 3D reconstruction from uncalibrated underwater video.

}, keywords = {3D reconstruction, autocalibration, Computer Vision}, author = {Cavan Neil} } @inproceedings {241, title = {Decoupled active surface for volumetric image segmentation}, booktitle = {7th Canadian Conference on Computer and Robot Vision}, year = {2010}, month = {06/2010}, address = {Ottawa, Ontario, Canada}, abstract = {

Finding the surface of a volumetric 3D object is a fundamental problem in computer vision. Energy minimizing splines, such as active surfaces, have been used to carry out such tasks, evolving under the influence of internal and external energies until the model converges to a desired surface. The present deformable model based surface extraction techniques are computationally expensive and are generally unreliable in identifying the surfaces of noisy, high-curvature and cluttered 3D objects. This paper proposes a novel decoupled active surface (DAS) for identifying the surface of volumetric 3D objects. The proposed DAS introduces two novel aspects which leads to robust, efficient and accurate convergence. First, rather than a parameterized surface, which leads to difficulties with complex shapes and parameter singularities, the DAS uses a conforming triangular mesh to represent the surface. Second, motivated by earlier successes in two-dimensional segmentation, the DAS treats the two energy components separately and uses novel solution techniques to efficiently minimize the two energy terms separately. The performance of DAS in segmenting static 3D objects is presented using several natural and synthetic volumetric images, with excellent convergence results.
}, keywords = {2D segmentation, Computer Vision, decoupled active surface, deformable model, energy minimizing splines, external energy, Feature Extraction, image segmentation, internal energy, mesh generation, splines (mathematics), surface extraction techniques, surface fitting, triangular mesh, volumetric 3D object, volumetric image segmentation}, doi = {http://dx.doi.org/10.1109/CRV.2010.45}, author = {A Mishra and P Fieguth and D A. Clausi} } @article {234, title = {Interactive modeling and evaluation of tumor growth}, journal = {Journal of Digital Imaging}, volume = {23}, year = {2010}, pages = {755 - 768}, abstract = {

This paper addresses the need to quantify tumor growth and detect changes as this information is relevant to manage the patient treatment and to aid biotechnological efforts to cure cancer (Silva et al. 2008). An interactive tumor segmentation technique is used to recover the shape and size of tumors without imposing shape constraints. This segmentation algorithm provides good convergence, is robust to the initialization conditions, and requires simple and intuitive user interactions. A parametric approach to model tumor growth analytically is proposed in this paper. The preliminary experimental results are encouraging. The segmentation method is shown to be robust and simple to use, even in situations where the tumor boundary definition is challenging. Also, the experiments indicate that the proposed model potentially can be used to extrapolate the available data and help predict the tumor size (assuming unconstrained growth). Additionally, the proposed method potentially can provide a quantitative reference to compare the tumor shrinkage rate in cancer treatments.

}, keywords = {Active contours, cancer detection, chest radiographs, Computed tomography (CT), computer analysis, Computer Vision, computer-assisted diagnosis, Diagnostic Imaging, digital image processing, image segmentation, lung cancer}, issn = {0897-1889}, url = {http://dx.doi.org/10.1007/s10278-009-9234-4}, author = {D Koff and J Scharcanski and L da Silva and A Wong} } @inproceedings {335, title = {An iterative approach to improved local phase coherence estimation}, booktitle = {4th Annual Canadian Conference on Computer and Robot Vision}, year = {2008}, abstract = {

This paper introduces a novel iterative approach to estimating local phase coherence in situations characterized by low signal-to-noise ratios. Local phase coherence is used for a wide range of computer vision applications such as edge and corner detection and object description. An issue faced in extracting local phase coherence is the presence of image noise. While existing approaches to dealing with noise when estimating local phase coherence is effective for low noise situations, they are inadequate for situations contaminated by high levels of noise. In the proposed approach, the issue of high image noise is addressed by re-estimating both the local phase coherence and the underlying image content iteratively to improve local phase coherence estimates. This is performed using a feedback loop model, where the local phase coherence estimates are used to re-estimate the image content using a moment-adaptive bilateral estimation scheme and information from the re-estimated image content is used to re-estimate the local phase coherence. Experiment results show that the proposed approach can be used to provide improved local phase coherence estimates.

}, keywords = {Computer Vision, corner detection, edge detection, high image noise, iterative approach, iterative methods, local phase coherence estimation, low signal-to-noise ratios, moment-adaptive bilateral estimation scheme, object description}, doi = {http://dx.doi.org/10.1109/CRV.2008.18}, 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} } @article {389, title = {Preserving boundaries for image texture segmentation using grey level co-occurring probabilities}, journal = {Pattern Recognition}, volume = {39}, year = {2006}, pages = {234 - 235}, abstract = {

Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries.

}, keywords = {Co-occurence probabilities, Co-occurrence matrix, Computer Vision, Digital imaging, Remote Sensing, sea ice, Segmentation, synthetic aperture radar, texture}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/j.patcog.2005.07.010}, url = {http://www.sciencedirect.com/science/article/pii/S0031320305002992}, author = {R Jobanputra and D A. Clausi} } @inproceedings {789, title = {Average cell orientation, shape and size estimated from tissue images}, booktitle = {2nd Annual Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, address = {Victoria, B.C., Canada}, abstract = {

Four computer vision algorithms to measure the average orientation, shape and size of cells in images of biological tissue are proposed and tested. These properties, which can be embodied by an elliptical \&$\#$39;composite cell\&$\#$39; are crucial for biomechanical tissue models. To automatically determine these properties is challenging due to the diverse nature of the image data, with tremendous and unpredictable variability in illumination, cell pigmentation, cell shape, and cell boundary visibility. First, a simple edge detection routine is performed on the raw images to locate cell edges and remove pigmentation variation. The edge map is then converted into the magnitude spatial-frequency domain where the spatial patterns of the cells appear as energy impulses. Four candidate methods that analyze the spatial-frequency data to estimate the properties of the composite cell are presented and compared. These methods are: least squares ellipse fitting, correlation, area moments and Gabor filters. Robustness is demonstrated by successful application on a wide variety of real images.

}, keywords = {area moments, average cell orientation, average cell shape, average cell size, biological tissue, biological tissues, biology computing, biomechanical tissue models, cell boundary visibility, cell geometry, cell pigmentation, Computer Vision, edge detection, elliptical composite cell, embryology, Gabor filters, least squares ellipse fitting, magnitude spatial-frequency domain, medical image processing, morphogenesis, shape detection, simple edge detection, statistical analysis, tissue images}, doi = {http://dx.doi.org/10.1109/CRV.2005.22}, author = {P Iles and D A. Clausi and S Puddister and G W. Brodland} } @article {589, title = {K-means Iterative Fisher (KIF) unsupervised clustering algorithm applied to image texture segmentation}, journal = {Pattern Recognition}, volume = {35}, year = {2002}, pages = {1959 - 1972}, abstract = {

The K-means Iterative Fisher (KIF) algorithm is a robust, unsupervised clustering algorithm applied here to the problem of image texture segmentation. The KIF algorithm involves two steps. First, K-means is applied. Second, the K-means class assignments are used to estimate parameters required for a Fisher linear discriminant (FLD). The FLD is applied iteratively to improve the solution. This combined K-means and iterative FLD is referred to as the KIF algorithm. Two KIF implementations are presented: a mixture resolving approach is extended to an unsupervised binary hierarchical approach. The same binary hierarchical KIF algorithm is used to properly segment images even though the number of classes, the class spatial boundaries, and the number of samples per class vary. The binary hierarchical KIF algorithm is fully unsupervised, requires no a priori knowledge of the number of classes, is a non-parametric solution, and is computationally efficient compared to other methods used for clustering in image texture segmentation solutions. This unsupervised methodology is demonstrated to be an improvement over other published texture segmentation results using a wide variety of test imagery. Gabor filters and co-occurrence probabilities are used as texture features.

}, keywords = {Binary hierarchical clustering, Co-occurrence probabilities, Computer Vision, Fisher linear discriminant, Gabor filters, image segmentation, K-means, texture segmentation}, issn = {0031-3203}, doi = {http://dx.doi.org/10.1016/S0031-3203(01)00138-8}, url = {http://www.sciencedirect.com/science/article/pii/S0031320301001388}, author = {D A. Clausi} } @inproceedings {534, title = {Morphological skeleton algorithm for PDP production line inspection}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Morphological skeletonization is an image processing technique that reduces complex, thick-lined images to a series of single pixel lines that accurately represent the original shapes. This procedure is especially useful to simplify automated applications requiring simple shape analysis and continuity checking by reducing the amount of redundant image data. In the semiconductor inspection field, skeletonization is a process that can be used to detect defects during plasma display panel (PDP) inspection. This paper introduces a novel morphological skeletonization algorithm developed for electrode pattern inspection of PDPs. This algorithm has been successfully integrated within a commercial machine vision system

}, keywords = {automated applications, commercial machine vision system, Computer Vision, continuity checking, defects detection, electrode pattern inspection, Feature Extraction, feature point matching, image matching, image processing, image representation, image thinning, Inspection, Mathematical morphology, morphological skeleton algorithm, PDP inspection, PDP production line inspection, plasma display panel, plasma displays, production, redundant image data reduction, semiconductor device testing, semiconductor inspection, shape analysis, shape representation, single pixel lines, thick-lined images}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933598}, author = {R Ge and D A. Clausi} } @inproceedings {502, title = {Incremental shape reconstruction using stereo image sequences}, booktitle = {International Conference on Image Processing}, year = {2000}, abstract = {

The limitations of estimating structure from either stereo or motion alone can be addressed by the use of stereo image sequences; however, many existing techniques for processing stereo sequences rely on having accurate feature correspondences initially. We present an iterative feature matching and shape reconstruction algorithm. The proposed method simultaneously resolves matching ambiguities by multiple hypothesis testing and develops an incrementally accurate and dense representation of the reconstructed object. This approach uses minimal domain specific constraints and can be easily generalised. The method\&$\#$39;s potential is demonstrated in a space vision application.

}, keywords = {artificial satellites, Computer Vision, dense representation, dynamic estimation, feature correspondences, Feature Extraction, image matching, image reconstruction, image representation, image sequences, incremental shape reconstruction, iterative feature matching algorithm, minimal domain specific constraints, multiple hypothesis testing, parameter estimation, reconstructed object, satellite docking, satellite rendezvous, space vision application, stereo image processing, stereo image sequences, stereo sequences processing, structure estimation}, doi = {http://dx.doi.org/10.1109/ICIP.2000.899818}, author = {T J. Moyung and P Fieguth} } @inproceedings {517, title = {Vision-based speaker detection using Bayesian networks}, booktitle = {1999 Conference on Computer Vision and Pattern Recognition}, year = {1999}, abstract = {

The development of user interfaces based on vision and speech requires the solution of a challenging statistical inference problem: The intentions and actions of multiple individuals must be inferred from noisy and ambiguous data. We argue that Bayesian network models are an attractive statistical framework for cue fusion in these applications. Bayes nets combine a natural mechanism for expressing contextual information with efficient algorithms for learning and inference. We illustrate these points through the development of a Bayes net model for detecting when a user is speaking. The model combines four simple vision sensors: face detection, skin color, skin texture, and mouth motion. We present some promising experimental results

}, keywords = {Bayesian networks, belief networks, Computer Vision, speaker detection, user interfaces, vision sensors}, author = {J M. Rehg and K P. Murphy and P Fieguth} }