@article {223, title = {KPAC: A kernel-based parametric active contour method for fast image segmentation}, journal = {IEEE Signal Processing Letters}, volume = {17}, year = {2010}, pages = {312 - 315}, abstract = {

Object boundary detection has been a topic of keen interest to the signal processing and pattern recognition community. A popular approach for object boundary detection is parametric active contours. Existing parametric active contour approaches often suffer from slower convergence rates, difficulty dealing with complex high curvature boundaries, and are prone to being trapped in local optima in the presence of noise and background clutter. To address these problems, this paper proposes a novel kernel-based active contour (KPAC) approach, which replaces the conventional internal energy term used in existing approaches by incorporating an adaptive kernel derived for the underlying image characteristics. Experimental results demonstrate that the KPAC approach achieves state-of-the-art performance when compared to two other state-of-the-art parametric active contour approaches.

}, keywords = {edge detection, image segmentation, kernel-based parametric active contour approach, KPAC approach, object boundary detection, Pattern Recognition}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2009.2036654}, author = {A Mishra and A Wong} } @article {227, title = {Quasi-random nonlinear scale space}, journal = {Pattern Recognition Letters}, volume = {31}, year = {2010}, pages = {1850 - 1859}, abstract = {

A novel nonlinear scale space framework is proposed for the purpose of multi-scale image representation. The scale space decomposition problem is formulated as a general Bayesian least-squares estimation problem. A quasi-random density estimation approach is introduced for estimating the posterior distribution between consecutive scale space realizations. In addition, the application of the proposed nonlinear scale space framework for edge detection is proposed. Experimental results demonstrate the effectiveness of the proposed scale space framework for constructing scale space representations with significantly better structural localization across all scales when compared to state-of-the-art scale space frameworks such as anisotropic diffusion, regularized nonlinear diffusion, complex nonlinear diffusion, and iterative bilateral scale space methods, especially under scenarios with high noise levels

}, keywords = {Anisotropic diffusion, Bayesian estimation, edge detection, multi-scale, Nonlinear scale space, Quasi-random}, author = {A Mishra and A Wong and D A. Clausi and P Fieguth} } @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 {338, title = {Robust edge detection based on non-local contribution of local frequency characteristics}, booktitle = {IEEE International Symposium on Multimedia}, year = {2008}, abstract = {

This paper introduces a robust approach to the problem of edge detection in situations characterized by high levels of image degradation and illumination non-uniformity. Popular edge detection methods are dependent solely on local image characteristics to localize edge information. In the proposed algorithm, we demonstrate that superior edge detection performance can be achieved by utilizing nonlocal image characteristics. This is accomplished by estimating the edge strength of a given pixel based on the local frequency characteristics of every pixel within an image. The proposed method is highly robust to scenarios characterized by illumination non-uniformity and low signal-to-noise ratios. Experimental results show that using the proposed method, edge detection can be qualitatively improved over existing methods on a small set of test images.

}, keywords = {edge detection, edge strength estimation, estimation theory, illumination nonuniformity, image degradation, local frequency characteristics, robust edge detection, signal-to-noise ratio}, doi = {http://dx.doi.org/10.1109/ISM.2008.79}, author = {A Wong and W Bishop} } @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 {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} } @inproceedings {644, title = {Combining local and global features for image segmentation using iterative classification and region merging}, booktitle = {2nd Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, abstract = {

In MRF based unsupervised segmentation, the MRF model parameters are typically estimated globally. Those global statistics sometimes are far from accurate for local areas if the image is highly non-stationary, and hence will generate false boundaries. The problem cannot be solved if local statistics are not considered. This work incorporates the local feature of edge strength in the MRF energy function, and segmentation is obtained by reducing the energy function using iterative classification and region merging.

}, keywords = {edge detection, edge strength, Feature Extraction, global feature, global statistics, image classification, image segmentation, iterative classification, iterative methods, local feature, local statistics, Markov processes, Markov random field, MRF based unsupervised segmentation, MRF energy function, MRF model parameter, region merging}, author = {Q Yu and D A. Clausi} } @inproceedings {597, title = {A Markov random fields model for hybrid edge and region based colour image segmentation}, booktitle = {15th Canadian Conference on Electrical and Computer Engineering}, year = {2002}, abstract = {

A framework based on a Markov random field approach for color image segmentation enhanced by edge detection is presented. We use a previously developed methodology to transform the image into an R\&$\#$39;G\&$\#$39;B\&$\#$39; space to remove any highlight components preserving the vector-angle component, representing color hue but not intensity, to remove shading effects. To improve the segmentation process we describe the idea of a line process. This allows for the integration of region segmentation with edge detection in a Markov random field framework. We discuss the advantages of this new model with respect to the previously developed image segmentation model.

}, keywords = {color hue, color theory, dichromatic reflection model, edge detection, highlight components removal, hybrid edge-based color image segmentation, image colour analysis, image segmentation, line process, Markov processes, Markov random fields model, random processes, region segmentation, region-based color image segmentation, RGB space, shading effects removal, vector-angle component}, doi = {http://dx.doi.org/10.1109/CCECE.2002.1013070}, author = {S Wesolkowski and P Fieguth} } @inproceedings {601, title = {Shape Preserving Edge Enhancement in Remote Sensing Imagery}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A novel approach to geometric shape preservation of remote sensing SAR sea ice images is presented in this paper. This approach will complement the existing edge detection schemes. The final edge is evolved by iterations. During each iteration all existing edges are checked for missing link and missing links are found by checking the orientation of the existing edges and by searching for best gradient path. When open edges are reduced considerably, opening of the features that are critical for shape preservation are found by tracing the edges and closing the boundary.

}, keywords = {best gradient path, edge detection, geometric shape preservation, geophysical signal processing, image enhancement, iteration, iterative method, measurement technique, missing link, ocean, oceanographic techniques, orientation, radar imaging, radar remote sensing, remote sensing by radar, SAR, sea ice, shape preserving edge enhancement, spaceborne radar, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026145}, author = {M S. Chowdhury and D A. Clausi} } @inproceedings {531, title = {Image segmentation using MRI vertebral cross-sections}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Computer assisted spinal surgery requires the development of a 3-D image of a patient\&$\#$39;s spine. A method is being developed to construct such a 3-D spinal image from axial MRI cross-sections, using a deformable template. This paper outlines techniques used to register the model with the patient data. First an algorithm based on symmetry of the anatomy is developed to determine the position and orientation of the vertebrae in each cross-section. Second, an active contour is used for segmentation of the spinal cord. Last, a novel edge detection scheme is developed to identify the low contrast edges of the vertebral body in each cross-section

}, keywords = {3D image, 3D spinal image, algorithm, anatomy symmetry, axial MRI cross-sections, biomedical MRI, computer assisted spinal surgery, deformable template, edge detection, image segmentation, low contrast edges identification, medical image processing, MRI vertebral cross-sections, orthopaedics, orthopedic surgery, patient data, patient spine, scale estimation, spinal cord segmentation, surgery, vertebrae orientation, vertebrae position, vertebral body}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933633}, author = {S Booth and D A. Clausi} } @inproceedings {448, title = {Using the Canny edge detector for feature extraction and enhancement of remote sensing images}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Edges are important features in an image since they represent significant local intensity changes. They provide important clues to separate regions within an object or to identify changes in illumination. Most remote sensing applications, such as image registration, image segmentation, region separation, object description, and recognition, use edge detection as a preprocessing stage for feature extraction. Real images, such as remote sensing images, can be corrupted with point noise. The real problem is how to enhance noisy remote sensing images and simultaneously extract the edges. Using the implemented Canny edge detector for feature extraction and as an enhancement tool for remote sensing images, the result was robust with a very high enhancement level

}, keywords = {Canny edge detector, edge detection, enhancement, Feature Extraction, geophysical signal processing, illumination, image enhancement, local intensity changes, noise, point noise, preprocessing stage, real images, remote sensing images, terrain mapping}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977981}, author = {M Ali and D A. Clausi} } @inproceedings {499, title = {Analysis of corneal images for assessing contact lens trauma}, booktitle = {International Conference on Image Processing}, year = {2000}, address = {Vancouver}, abstract = {

Contact lens wearers are often affected by a condition known as conjuctival hyperaemia. This condition is characterised by dilated blood vessels visible in the sclera (the white portion of the eye). Optometrists need to be able to detect changes in the level of redness and to repeatably assess the absolute levels of redness found in a patient\&$\#$39;s eye in order to prescribe and evaluate treatment. The goal of this paper is to develop an automated method for grading the level of redness in human eyes. The selected features examine the average redness and length of arterial edges. The computed features are correlated with expert data collected to train the redness estimator

}, keywords = {arterial edges, blood vessels, conjuctival hyperaemia, contact lens trauma, corneal images, dilated blood vessels, edge detection, eye, Feature Extraction, human eyes, image colour analysis, medical image processing, patient, redness, redness estimator, sclera}, doi = {http://dx.doi.org/10.1109/ICIP.2000.900923}, author = {J Cullen and P Fieguth and S Pounder and K Whitear} }