@inproceedings {25, title = {IMPROVED FINE STRUCTURE MODELING VIA GUIDED STOCHASTIC CLIQUE FORMATION IN FULLY CONNECTED CONDITIONAL RANDOM FIELDS}, booktitle = {IEEE Conference on Image Processing}, year = {2015}, abstract = {

Markov random fields (MRFs) and conditional random fields (CRFs) are influential tools in image modeling, particularly for applications such as image segmentation. Local MRFs and CRFs utilize local nodal interactions when modeling, leading to excessive smoothness on boundaries (i.e., the short-boundary bias problem). Recently, the concept of fully connected conditional random fields with stochastic cliques (SFCRF) was proposed to enable long-range nodal interactions while addressing the computational complexity associated with fully connected random fields. While SFCRF was shown to provide significant improvements in segmentation accuracy, there were still limitations with the preservation of fine structure boundaries. To address these limitations, we propose a new approach to stochastic clique formation for fully connected random fields (G-SFCRF) that is guided by the structural characteristics of different nodes within the random field. In particular, fine structures surrounding a node are modeled statistically by probability distributions, and stochastic cliques are formed by considering the statistical similarities between nodes within the random fields. Experimental results show that G-SFCRF outperforms existing fully connected CRF frameworks, SFCRF, and the principled deep random field framework for image segmentation.

}, keywords = {CRF, Fully Connected, Guided Stochastic Cliques, Segmentation, SFCRF, Stochastic Cliques}, author = {M J. Shafiee and A Chung and A Wong and P Fieguth} } @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 {236, title = {Automated 3D reconstruction and segmentation from optical coherence tomography}, booktitle = {11th European Conference on Computer Vision}, year = {2010}, month = {09/2010}, address = {Crete, Greece}, abstract = {

Ultra-High Resolution Optical Coherence Tomography is a novel imaging technology that allows non-invasive, high speed, cellular resolution imaging of anatomical structures in the human eye, including the retina and the cornea.

\ A three-dimensional study of the cornea, for example, requires the segmentation and mutual alignment of a large number of two-dimensional images. Such segmentation has, until now, only been undertaken by hand for individual two-dimensional images; this paper presents a method for automated segmentation, opening substantial opportunities for 3D corneal imaging and analysis, using many hundreds of 2D slices.

}, keywords = {cornea, non-invasive imaging, OCT, reconstruction, Segmentation, statistical modelling, UHROCT}, isbn = {3-642-15557-X, 978-3-642-15557-4}, author = {J A. Eichel and K Bizheva 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} } @article {385, title = {Automated detection of crack defects in buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {58 - 72}, abstract = {

The detection of cracks in concrete infrastructure is a problem of great interest. In particular, the detection of cracks in buried pipes is a crucial step in assessing the degree of pipe deterioration for municipal and utility operators. The key challenge is that whereas joints and laterals have a predictable appearance, the randomness and irregularity of cracks make them difficult to model. Our previous work has led to a segmented pipe image (with holes, joints, and laterals eliminated) obtained by a morphological approach. This paper presents the development of a statistical filter for the detection of cracks in the pipes. We propose a two-step approach. The first step is local and is used to extract crack features from the buried pipe images; we present two such detectors as well as a method for fusing them. The second step is global and defines the cracks among the segment candidates by processes of cleaning and linking. The influences of the parameters on crack detection are studied and results are presented for various pipe images.

}, keywords = {Automated inspection, Crack detection, image processing, Pipeline assessment, Pipeline Infrastructure, Segmentation, Statistical properties}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.006}, author = {S K. Sinha and P Fieguth} } @article {387, title = {Morphological segmentation and classification of underground pipe images}, journal = {Machine Vision and Applications}, volume = {17}, year = {2006}, pages = {21 - 31}, abstract = {

Visual inspection based on closed circuit television surveys is used widely in North America to assess the condition of underground pipes. Although the human eye is extremely effective at recognition and classification, it is not suitable for assessing pipe defects in thousand of miles of pipeline because of fatigue, subjectivity, and cost. In this paper, simple, robust, and efficient image segmentation and classification algorithm for the automated analysis of scanned underground pipe images is presented. The experimental results demonstrate that the proposed algorithm can precisely segment and classify pipe cracks, holes, laterals, joints and collapse surface from underground pipe images

}, keywords = {Classification, Inspection, Morphology, Segmentation}, issn = {0932-8092}, doi = {http://dx.doi.org/10.1007/s00138-005-0012-0}, author = {S K. Sinha and P Fieguth} } @article {388, title = {Neuro-fuzzy network for the classification of buried pipe defects}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {73 - 83}, abstract = {

Pipeline infrastructure is decaying at an accelerating rate due to reduced funding and insufficient quality control resulting in poor installation, little or no inspection and maintenance, and a general lack of uniformity and improvement in design, construction and operation practices. The current practice that is being followed to inspect the conditions of pipes is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human operators. Buried pipe defect classification is thus a practical and important pattern classification problem. These defects appear in the form of randomly shaped cracks and holes, broken joints and laterals, and others. This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images. A comparative evaluation of the K-NN, fuzzy K-NN, conventional backpropagation network, and proposed neuro-fuzzy projection network classifiers is carried out. Among the five neural methods implemented and tested, the proposed neuro-fuzzy classifier performs the best, with classification accuracies around 90\% on real concrete pipe images.

}, keywords = {Automated inspection, Backpropagation network, Featuresm Neural Network, image processing, Neuro-fuzzy projection network, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.005}, author = {S K. Sinha and P Fieguth} } @inproceedings {405, title = {Pixel-based sea ice classification using the MAGSIC system}, booktitle = {International Society for Photogrammetry and Remote Sensing}, year = {2006}, address = {Enschede, The Netherlands}, abstract = {

MAGSIC is an operation-oriented system in development dedicated to map-guided classification of sea ice for navigation route planning and meteorological modeling. It has already produced promising results in difficult situations such as the Gulf of Saint-Lawrence in late winter. The Canadian Ice Service (CIS) produces ice maps made of large regions with relatively homogeneous concentrations of different ice types. MAGSIC uses the information of these maps to produce a pixel-based (rather than region-based) ice map by labeling a Markov random field (MRF) segmentation of RADARSAT-1 data along with its derived texture features. The system uses a novel implementation of MRF segmentation in combination with a unique labeling approach based on \“cognitive reasoning\”. Although reasonably successful, the system often had difficulties identifying ice type that required cues based on the shape recognition of large ice floes or leads. This article aims at thoroughly testing the MAGSIC system using validation data acquired during the \“2003 Gulf of Saint-Lawrence SAR Validation Field Program\” performed by CIS. Some new features were also added to MAGSIC and were evaluated. Results suggest a reasonable success and that the errors can be partially attributed to the generalized nature of the analysts ́interpretation and to the difficulties of obtaining concurring ground and image data. They suggest that classification metrics that can compare sample distributions were slightly superior for labeling purposes but this could not be confirmed statistically.

}, keywords = {Classification, navigation, Radarsat, Reasoning, Segmentation, Snow Ice, texture}, author = {P Maillard 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} } @article {391, title = {Segmentation of buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {47 - 57}, abstract = {

The enormity of the problem of deteriorating pipeline infrastructure is widely apparent. Since a complete rebuilding of the piping system is not financially realistic, municipal and utility operators require the ability to monitor the condition of buried pipes. Thus, reliable pipeline assessment and management tools are necessary to develop long term cost effective maintenance, repair, and rehabilitation programs. In this paper a simple, robust and efficient image segmentation algorithm for the automated analysis of scanned underground pipe images is presented. The algorithm consists of image pre-processing followed by a sequence of morphological operations to accurately segment pipe cracks, holes, joints, laterals, and collapsed surfaces, a crucial step in the classification of defects in underground pipes. The proposed approach can be completely automated and has been tested on five hundred scanned images of buried concrete sewer pipes from major cities in North America.

}, keywords = {Automated inspection, image processing, Mathematical morphology, Pipeline assessment, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.007}, author = {S K. Sinha and P Fieguth} } @article {419, title = {Unsupervised segmentation of synthetic aperture radar sea ice imagery using a novel Markov random field model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {528 - 538}, chapter = {2940}, abstract = {

\—Environmental and sensor challenges pose difficulties for the development of computer-assisted algorithms to segment synthetic aperture radar (SAR) sea ice imagery. In this research, in support of operational activities at the Canadian Ice Service, images containing visually separable classes of either ice and water or multiple ice classes are segmented. This paper uses image intensity to discriminate ice from water and uses texture features to identify distinct ice types. In order to seamlessly combine image spatial relationships with various image features, a novel Bayesian segmentation approach is developed and applied. This new approach uses a function-based parameter to weight the two components in a Markov random field (MRF) model. The devised model allows for automatic estimation of MRF model parameters to produce accurate unsupervised segmentation results. Experiments demonstrate that the proposed algorithm is able to successfully segment various SAR sea ice images and achieve improvement over existing published methods including the standard MRF-based method, finite Gamma mixture model, and K-means clustering.

}, keywords = {Classification, coocurrence, expectation-maximization (EM), Gamma distribution, intensity, K-means clustering, Markov random field (MRF), mixture model, Pattern Recognition, probabilities, sea ice, Segmentation, synthetic aperture radar (SAR), texture, unsupervised}, author = {H Deng and D A. Clausi} } @inproceedings {442, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {443, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {444, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {441, title = {Texture analysis using Gaussian weighted grey level co-occurrence probabilities}, booktitle = {1st Canadian Conference on Computer and Robot Vision}, year = {2004}, address = {London, Ontario, Canada}, abstract = {

The discrimination of textures is a significant aspect in segmenting SAR sea ice imagery. Texture features calculated from grey level co-occurring probabilities (GLCP) are well accepted and applied in the analysis of many images. When calculating GLCPs, each co-occurring pixel pair within the image window is given a uniform weighting. Although a novel technique, co-occurring texture features have a tendency to misclassify and erode texture boundaries due to the large window sizes needed to capture meaningful statistics.

A method is proposed whereby co-occurring pixel pairs closer to the center of the image window are assigned larger co-occurring probabilities according to a Gaussian distribution. By using a Gaussian weighting scheme to calculate the GLCPs, less significance is given to pixel pairs that are on the outlying regions of the window, which have a tendency to produce erroneous statistics as the image window overlaps a texture boundary. This method proves to preserve the edge strength between textures and provides better segmentation at the expense of computational complexity.

}, keywords = {Grey level co-occurrence, probabilities, remote sensing imagery, SAR sea ice, Segmentation, texture analysis, texture features}, doi = {http://dx.doi.org/10.1109/CCCRV.2004.1301421}, author = {R Jobanputra and D A. Clausi} } @inproceedings {567, title = {Sea ice segmentation using Markov random fields}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Tools are required to assist the identification of pertinent classes in SAR sea ice imagery. Texture models offer a means of performing this task. The texture information in SAR sea ice imagery can be characterized by two Markov random field models: the Gauss model for conditional distribution of the observed intensity image and the discrete model for the underlying texture label image. The segmentation can be implemented as an optimization process of maximizing a posteriori distribution in a Bayesian framework

}, keywords = {Bayes methods, Bayesian framework, classes, conditional distribution, discrete model, geophysical signal processing, identification, image classification, image segmentation, image texture, intensity image, Markov processes, Markov Random Field models, maximizing a posteriori distribution, maximum likelihood estimation, oceanographic techniques, optimisation, optimization process, radar imaging, remote sensing by radar, SAR sea ice imagery, sea ice, Segmentation, synthetic aperture radar, texture models, underlying texture label image}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977102}, author = {B Yue and D A. Clausi} } @article {576, title = {Multiscale statistical methods for the segmentation of signals and images}, journal = {IEEE Transactions on Image Processing}, volume = {9}, year = {2000}, pages = {456 - 468}, abstract = {

This paper addresses the problem of both segmenting and reconstructing a noisy signal or image. The work is motivated by large problems arising in certain scientific applications, such as medical imaging. Two objectives for a segmentation and denoising algorithm are laid out: it should be computationally efficient and capable of generating statistics for the errors in the reconstruction and estimates of the boundary locations. The starting point for the development of a suitable algorithm is a variational approach to segmentation (Shah 1992). This paper then develops a precise statistical interpretation of a one dimensional (1-D) version of this variational approach to segmentation. The 1-D algorithm that arises as a result of this analysis is computationally efficient and capable of generating error statistics. A straightforward extension of this algorithm to two dimensions would incorporate recursive procedures for computing estimates of inhomogeneous Gaussian Markov random fields. Such procedures require an unacceptably large number of operations. To meet the objective of developing a computationally efficient algorithm, the use of previously developed multiscale statistical methods is investigated. This results in the development of an algorithm for segmenting and denoising which is not only computationally efficient but also capable of generating error statistics, as desired

}, keywords = {biomedical MRI, boundary locations, brain, denoising, error statistics, Gaussian processes, image reconstruction, image segmentation, images, inhomogeneous Gaussian Markov random fields, interference suppression, Markov processes, medical image processing, medical imaging, multiscale methods, noisy image, noisy signal, reconstruction, recursive procedures, scientific applications, Segmentation, signal reconstruction, signals, statistical interpretation, variational approach, variational techniques}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.826782}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} } @inproceedings {491, title = {Multiscale Methods for the Segmentation of Images}, booktitle = {ICASSP {\textquoteright}96}, year = {1996}, abstract = {

This work presents a method for segmenting images based on gradients in the intensity function. Past approaches have centered on formulating the problem in the context of variational calculus as the minimization of a functional involving the image intensity and edge functions. Computational methods for finding the minima of such variational problems are prone to two shortfalls: they are often computationally intensive and almost always incapable of computing error statistics associated with the segmentation. Using a particular variational formulation as a starting point, this paper presents a derivation of an associated statistical formulation using multiscale models. The result is an algorithm which is fast and capable of computing error statistics

}, keywords = {error statistics, functional, functional equations, gradients, image segmentation, images, intensity function, minimisation, minimization, multiscale models, Segmentation, statistical formulation, variational calculus, variational techniques}, doi = {http://dx.doi.org/10.1109/ICASSP.1996.545869}, author = {M Schneider and P Fieguth and W C. Karl and A S. Willsky} }