@inbook {116, title = {Melanoma decision support using lighting-corrected intuitive feature models}, booktitle = {Computer Vision Techniques for the Diagnosis of Skin Cancer}, year = {2013}, pages = {193-219}, chapter = {7}, abstract = {

Skin lesion analysis using standard camera images has received limited attention from the scientific community due to its technical complexity and scarcity of data. The images are privy to lighting variations caused by uneven source lighting, and unconstrained differences in resolution, scale, and equipment. In this chapter, we propose a framework that performs illumination correction and feature extraction on photographs of skin lesions acquired using standard consumer-grade cameras. We apply a multi-stage illumination correction algorithm and define a set of high-level intuitive features (HLIF) that quantifies the level of asymmetry and border irregularity about a lesion. This lighting-corrected intuitive feature model framework can be used to classify skin lesion diagnoses with high accuracy. The framework accurately corrects the illumination variations and achieves high and precise sensitivity (95\% confidence interval (CI), 73.1\–73.5 \%) and specificity (95\% CI, 72.0\–72.4\%) using a linear support vector machine classifier with cross-validation trials. It exhibits higher test-retest reliability than the much larger state-of-the-art low-level feature set (95\% CI, 78.1\–79.7\% sensitivity, 75.3\–76.3\% specificity). Combining our framework with these low-level features attains sensitivity (95\% CI, 83.3\–84.8\%) and specificity (95\% CI, 79.7\–80.1\%), which is more accurate and reliable than classification using the low-level feature set.

}, keywords = {Decision Support System, Feature Extraction, Illumination Correction, Melanoma, Pigmented Skin Lesion, Standard Camera}, author = {R Amelard and J Glaister and A Wong and D A. Clausi} } @inproceedings {143, title = {Extracting High-Level Intuitive Features (HLIF) For Classifying Skin Lesions Using Standard Camera Images}, booktitle = {9th Conference on Computer and Robot Vision}, year = {2012}, month = {05/2012}, address = {Toronto}, abstract = {

High-level intuitive features (HLIF) that measure asymmetry of skin lesion images obtained using standard cameras are presented. These features can be used to help dermatologists objectively diagnose lesions as cancerous (melanoma) or benign with intuitive rationale. Existing work defines large sets of low-level statistical features for analysing skin lesions. The proposed HLIFs are designed such that smaller sets of HLIFs can capture more deterministic information than large sets of low-level features. Analytical reasoning is given for each feature to show how it aptly describes asymmetry. Promising experimental results show that classification using the proposed HLIF set, although only one-tenth the size of the existing state-of-the-art low-level feature set, labels the data with better or comparable success. The best classification is obtained by combining the low-level feature set with the HLIF set.

}, keywords = {decision support, feature descriptor, Feature Extraction, Melanoma, skin lesion}, author = {R Amelard and A Wong and D A. Clausi} } @article {122, title = {Sorted Random Projections for Robust Rotation Invariant Texture Classification}, journal = {Pattern Recognition}, volume = {45}, year = {2012}, month = {05/12}, pages = {2405-2418}, abstract = {

This paper presents a simple, novel, yet very powerful approach for robust rotation-invariant texture classification based on random projection. The proposed sorted random projection maintains the strengths of random projection, in being computationally efficient and low-dimensional, with the addition of a straightforward sorting step to introduce rotation invariance. At the feature extraction stage, a small set of random measurements is extracted from sorted pixels or sorted pixel differences in local image patches. The rotation invariant random features are embedded into a bag-of-words model to perform texture classification, allowing us to achieve global rotation invariance. The proposed unconventional and novel random features are very robust, yet by leveraging the sparse nature of texture images, our approach outperforms traditional feature extraction methods which involve careful design and complex steps. We report extensive experiments comparing the proposed method to six state-of-the-art methods, RP, Patch, LBP, WMFS and the methods of Lazebnik et al. and Zhang et al., in texture classification on five databases: CUReT, Brodatz, UIUC, UMD and KTH-TIPS. Our approach leads to significant improvements in classification accuracy, producing consistently good results on each database, including what we believe to be the best reported results for Brodatz, UMD and KTH-TIPS

}, keywords = {Bag of Words, Compressed Sensing, Feature Extraction, Image patches, Random projection, Rotation invariance, Texture classification}, author = {L Liu and P Fieguth and D A. Clausi and G Kuang} } @article {169, title = {A Bayesian theoretic approach to multi-scale complex phase order representations}, journal = {IEEE Transactions on Image Processing}, year = {2011}, abstract = {

This paper explores a Bayesian theoretic approach to constructing multi-scale complex phase order representations. We formulate the construction of complex phase order representations at different structural scales based on scale space theory. Linear and nonlinear deterministic approaches are explored, and a Bayesian theoretic approach is introduced for constructing representations in such a way that strong structure localization and noise resilience is achieved. Experiments illustrate its potential for constructing robust multi-scale complex phase order representations with well-localized structures across all scales under high noise situations. Illustrative examples of applications of the proposed approach is presented in the form of multimodal image registration and feature extraction.

}, keywords = {Bayesian, complex phase order, Feature Extraction, registration}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2011.2160352}, author = {A Wong} } @inproceedings {245, title = {A cellular automata based semi-automatic algorithm for segmentation of choroidal blood vessels from ultrahigh resolution optical coherence images of rat retina}, booktitle = {23rd Canadian Conference on Electrical and Computer Engineering (CCECE)}, year = {2010}, month = {06/2010}, address = {Calgary, Alberta, Canada}, abstract = {

Abnormal changes in choroidal blood flow have been linked to various retinal diseases, such as Diabetic Retinopathy (DR) and Age related Macular Degeneration (AMD), which at later stages can lead to blindness. Therefore non-invasive and precise evaluation of choroidal blood flow can aid the diagnosis, treatment and monitoring of retinal disease progression. Doppler Optical Coherence Tomography is an imaging technique capable of measuring blood flow velocity and visualization of retinal and choroidal blood vessles. However accurate assessment of retinal and choroidal blood flow requires precise measurement of the blood vessel thickness. The presence of speckle noise and low image contrast of OCT tomograms makes this task very challenging. This paper proposes a cellular automata based semi-automatic algorithm for the segmentation of choroidal blood vessels. The proposed approach propagates user-defined points in order to identify the vessel boundaries, allowing a thickness profile to be extracted. The performance of the algorithm was tested on a series of retinal images acquired from living rats with a high speed, ultrahigh resolution OCT system (UHROCT). Experimental results show that the proposed approach provides precise thickness profiles even in the suboptimal conditions of low image contrast in the UHROCT images.
Abnormal changes in choroidal blood flow have been linked to various retinal diseases, such as Diabetic Retinopathy (DR) and Age related Macular Degeneration (AMD), which at later stages can lead to blindness. Therefore non-invasive and precise evaluation of choroidal blood flow can aid the diagnosis, treatment and monitoring of retinal disease progression. Doppler Optical Coherence Tomography is an imaging technique capable of measuring blood flow velocity and visualization of retinal and choroidal blood vessles. However accurate assessment of retinal and choroidal blood flow requires precise measurement of the blood vessel thickness. The presence of speckle noise and low image contrast of OCT tomograms makes this task very challenging. This paper proposes a cellular automata based semi-automatic algorithm for the segmentation of choroidal blood vessels. The proposed approach propagates user-defined points in order to identify the vessel boundaries, allowing a thickness profile to be extracted. The performance of the algorithm was tested on a series of retinal images acquired from living rats with a high speed, ultrahigh resolution OCT system (UHROCT). Experimental results show that the proposed approach provides precise thickness profiles even in the suboptimal conditions of low image contrast in the UHROCT images.
}, keywords = {age related macular degeneration, cellular automata, choroidal blood flow, choroidal blood vessel segmentation, diabetic retinopathy, Doppler optical coherence tomography, Feature Extraction, image resolution, image segmentation, medical image processing, OCT tomograms, optical tomography, rat retina image, retinal blood flow, retinal disease progression, thickness profile extraction, ultrahigh resolution optical coherence image}, doi = {http://dx.doi.org/10.1109/CCECE.2010.5575182}, author = {A Mishra and S Hariri and A Moayed and K Bizheva and P Fieguth and D A. Clausi} } @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} } @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} } @inproceedings {263, title = {Texture classification using compressed sensing}, booktitle = {This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. }, year = {2010}, abstract = {

This paper presents a simple, novel, yet very powerful approach for texture classification based on compressed sensing and bag of words model, suitable for large texture database applications with images obtained under unknown viewpoint and illumination. At the feature extraction stage, a small set of random features are extracted from local image patches. The random features are embedded into the bag of words model to perform texture classification. Random feature extraction surpasses many conventional feature extraction methods, despite their careful design and complexity. We conduct extensive experiments on the CUReT database to evaluate the performance of the proposed approach. It is demonstrated that excellent performance can be achieved by the proposed approach using a small number of random features, as long as the dimension of the feature space is above certain threshold. Our approach is compared with recent state-of-the-art methods: the Patch method (Varma and Zisserman, TPAMI 09), the MR8 filter bank method (Varma and Zisserman, IJCV 05) and the LBP method (Ojala et al., TPAMI 02). It is shown that the proposed method significantly outperforms MR8 and LBP and is at least as good as the Patch method with drastic reduction in storage and computational complexity.

}, keywords = {bag of words model, band-pass filters, Compressed Sensing, computational complexity, Feature Extraction, image classification, image texture, LBP method, MR8 filter bank method, patch method, Texture classification, texture database applications}, doi = {http://dx.doi.org/10.1109/CRV.2010.16}, author = {L Liu and P Fieguth} } @inproceedings {289, title = {A novel algorithm for extraction of the layers of the cornea}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

Accurate corneal layer boundary extraction from optical coherence tomograms can provide precise layer thickness measurements required in the analysis of corneal disease. This paper establishes a novel approach to precisely obtain the five primary corneal layer boundaries. The proposed method determines correspondence relationships between the layer boundaries to facilitate robust boundary extraction in the presence of noise and artifacts. The first phase of the method applies morphological operators to enhance the prominent structural features of the cornea. The second phase uses a semi-automated segmentation algorithm to extract the upper and lower boundaries of the cornea; these boundaries are used to register the corneal image. The final phase extracts all five boundaries using a global optimization method exploiting the medial correspondence relationship between each layers. The proposed method is tested and verified using a representative set of optical coherence tomography images and compared against several state of the art methods. The proposed method is demonstrated to be more robust to noise, to provide more accurate segmentation results, and to require fewer user interactions than the other published methods.

}, keywords = {corneal disease, corneal image register, corneal layer boundary extraction, diseases, eye, Feature Extraction, global optimization method, image registration, image segmentation, layer thickness measurements, medical image processing, optical coherence tomography, optical tomography, optimisation, semiautomated segmentation algorithm, thickness measurement}, doi = {http://dx.doi.org/10.1109/CRV.2009.22}, author = {J A. Eichel and A Mishra and D A. Clausi and P Fieguth} } @inproceedings {327, title = {Automatic registration of inter-band and inter-sensor images using robust complex wavelet feature representations}, booktitle = {5th Workshop on Pattern Recognition for Remote Sensing}, year = {2008}, month = {12/2008}, address = {Tampa, Florida, USA}, abstract = {

A robust method for registering inter-band and inter-sensor remote sensing images has been designed and implemented. The proposed method introduces noise-resilient and contrast invariant control point detection and control point matching schemes based on robust complex wavelet feature representations. Furthermore, an iterative refinement scheme is introduced for achieving improved control point pair localization and mapping function estimation between the images being registered. The registration accuracy of the proposed method was demonstrated on the registration of multi-spectral optical and synthetic aperture radar (SAR) images. The proposed method achieves better registration accuracy when compared with the state-of-the-art MSSD and ARRSI registration algorithms.

}, keywords = {ARRSI, automatic registration, contrast invariant control point detection, control point matching schemes, control point pair localization, estimation theory, Feature Extraction, image matching, image registration, image representation, images registration, inter-band images, inter-sensor images, iterative methods, iterative refinement scheme, mapping function estimation, MSSD, multispectral optical images, registration algorithms, Remote Sensing, remote sensing images, robust complex wavelet feature representations, synthetic aperture radar, synthetic aperture radar images, wavelet transforms}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783164}, author = {A Wong and D A. Clausi and P Fieguth} } @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 {368, title = {Robust invariant descriptor for symbol-based image recognition and retrieval}, booktitle = {1st IEEE International Conference on Semantic Computing}, year = {2007}, abstract = {

This paper presents a robust invariant descriptor for symbol-based image recognition and retrieval. A modified Hough-based Transform is used to extract parameter space information (i.e., position data and angular data) from a symbol image to derive an invariant descriptor. The proposed descriptor provides a compact representation of a symbol image that can be evaluated efficiently. The extracted descriptor is highly robust against geometric transformations such as translation, rotation, reflection, and scaling, and image degradation. A series of experiments were conducted using a set of architectural and engineering symbols subjected to geometric transformations and image degradation. The experimental results clearly show that the proposed descriptor can be used effectively for symbol recognition and retrieval.

}, keywords = {content-based retrieval, Feature Extraction, geometric transformation, Hough transforms, Hough-based transform, image degradation, image recognition, image representation, image retrieval, robust invariant descriptor, symbol-based image recognition}, doi = {http://dx.doi.org/10.1109/ICSC.2007.24}, author = {A Wong and W Bishop} } @inproceedings {393, title = {Filament preserving segmentation for SAR sea ice imagery using a new statistical model}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Modelling spatial context constraints using Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to SAR sea ice segmentation has also been demonstrated by Deng and Clausi (2005). However, most existing MRF models are not capable of preserving filaments, specifically leads and ridges for SAR sea ice, which are valuable for ship navigation applications and helpful for identifying certain ice types. A new statistical context model is proposed that can preserve such narrow elongated features while producing similar smooth segmentation results as those of existing MRF based approaches

}, keywords = {Feature Extraction, filament preserving segmentation, geophysical signal processing, Image Denoising, image segmentation, Markov processes, Markov random field, narrow elongated features, noisy image segmentation, oceanographic techniques, radar imaging, SAR sea ice imagery, sea ice, ship navigation, spatial context constraints, statistical analysis, statistical model, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/ICPR.2006.561}, author = {Q Yu and D A. Clausi} } @inproceedings {750, title = {A hybrid algorithm using discrete cosine transform and Gabor filter bank}, booktitle = {17th Canadian Conference on Electrical and Computer Engineering}, year = {2006}, abstract = {

Gabor filters have been widely used for texture segmentation and feature extraction, however there are important considerations regarding filter parameters, filter bank coverage in the frequency domain and feature dimensional reduction. In this paper, a texture segmentation algorithm based on a hybrid filter bank is presented. The proposed method uses a Gabor filter bank and discrete cosine transform (GDCT) to extract the optimal features for texture segmentation. To reduce the feature vector dimension a competitive network is trained to estimate the principal components of the extracted features. The feature vectors composing both Gabor and DCT features are quantized by estimated eigenvectors. The proposed method enables the use of multiple filter banks or larger filter banks consisting of a higher number of channels.

}, keywords = {channel bank filters, competitive network training, discrete cosine transform, discrete cosine transforms, eigenvalues and eigenfunctions, eigenvector quantization, feature dimensional reduction, Feature Extraction, filter bank coverage, filter parameters, Gabor filter bank, GDCT, hybrid algorithm, hybrid filter bank, image segmentation, image texture, multiple filter banks, neural nets, neural networks, optimal features, principal component analysis, principal components estimation, texture segmentation, unsupervised learning}, doi = {http://dx.doi.org/10.1109/CCECE.2004.1349767}, author = {N Nezamoddin Kachouie and J Alirezaie and P Fieguth} } @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} } @article {438, title = {Comparing cooccurrence probabilities and Markov random fields for texture analysis of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {42}, year = {2004}, pages = {215 - 228}, abstract = {

This paper compares the discrimination ability of two texture analysis methods: Markov random fields (MRFs) and gray-level cooccurrence probabilities (GLCPs). There exists limited published research comparing different texture methods, especially with regard to segmenting remotely sensed imagery. The role of window size in texture feature consistency and separability as well as the role in handling of multiple textures within a window are investigated. Necessary testing is performed on samples of synthetic (MRF generated), Brodatz, and synthetic aperture radar (SAR) sea ice imagery. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, which is important when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than MRFs given their respective segmentation procedures.

}, keywords = {Brodatz, discrimination ability, Feature Extraction, GLCP, gray-level cooccurrence probabilities, image segmentation, image texture, Markov processes, Markov Random Fields, MRF, Remote Sensing, remote sensing by radar, SAR sea ice imagery, sea ice, synthetic aperture radar, texture analysis, texture boundary, texture feature consistency, texture feature separability, texture methods, window size}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2003.817218}, author = {D A. Clausi and B Yue} } @inproceedings {629, title = {Feature fusion for image texture segmentation}, booktitle = {17th International Conference on Pattern Recognition (ICPR)}, year = {2004}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. Feature space separability and unsupervised image segmentation are used for testing. The fused features are robust with respect to the curse of dimensionality and additive noise. Feature reduction methods are typically detrimental to the segmentation performance. Overall, the fused features are a definite improvement over non-fused features and are advocated in texture analysis applications.

}, keywords = {additive noise, design based method, Feature Extraction, feature fusion, feature reduction method, Gabor filter, grey level cooccurrence probability, image segmentation, image texture, image texture segmentation, probability, texture analysis, texture recognition, unsupervised image segmentation}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334207}, author = {D A. Clausi and H Deng} } @inproceedings {627, title = {Texture segmentation comparison using grey level co-occurrence probabilities and Markov random fields}, booktitle = {17th International Conference on Pattern Recognition (ICPR)}, year = {2004}, month = {08/2004}, address = {Cambridge, United Kingdom}, abstract = {

The discrimination ability of texture features derived from Gaussian Markov random fields (GMRFs) and grey level co-occurrence probabilities (GLCPs) are compared and contrasted. More specifically, the role of window size in feature consistency and separability as well as the role of multiple textures within a window are investigated. GLCPs are demonstrated to have improved discrimination ability relative to MRFs with decreasing window size, an important concept when performing image segmentation. On the other hand, GLCPs are more sensitive to texture boundary confusion than GMRFs.

}, keywords = {discrimination ability, Feature Extraction, Gaussian Markov random fields, Gaussian processes, grey level co-occurrence probabilities, image segmentation, image texture, Markov processes, probability, texture features, texture segmentation}, doi = {http://dx.doi.org/10.1109/ICPR.2004.1334208}, author = {D A. Clausi and B Yue} } @inproceedings {694, title = {Advanced Gaussian MRF rotation-invariant texture features for classification of remote sensing imagery}, booktitle = {2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition}, year = {2003}, address = {Madison, WI, USA}, abstract = {

The features based on Markov random field (MRF) models are usually sensitive to the rotation of image textures. The paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for modeling rotated image textures and retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate (LSE) method, an approximate least squares estimate (ALSE) method is proposed to estimate the parameters of ACGMRF model. The rotation-invariant features can be obtained from the parameters of the ACGMRF model by the one-dimensional (1D) discrete Fourier transform (DFT). Significantly improved accuracy can be achieved by applying the rotation-invariant features to classify SAR (synthetic aperture radar) sea ice and Brodatz imagery.

}, keywords = {ACGMRF model, ALSE method, anisotropic circular Gaussian MRF, approximate least squares estimate, Brodatz imagery, discrete Fourier transform, discrete Fourier transforms, Feature Extraction, Gaussian distribution, image classification, image texture rotation, least squares approximation, least squares approximations, Markov processes, Markov random field, one-dimensional DFT, Remote Sensing, remote sensing image classification, rotated image texture modeling, rotation-invariant feature, rotation-invariant texture feature, SAR classification, sea ice, singularity problem, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CVPR.2003.1211533}, author = {H Deng and D A. Clausi} } @inproceedings {649, title = {Operational segmentation and classification of SAR sea ice imagery}, booktitle = {2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, An Honorary Workshop for Prof. David A. Landgrebe}, year = {2003}, abstract = {

The Canadian Ice Service (CIS) is a government agency responsible for monitoring ice-infested regions in Canada\&$\#$39;s jurisdiction. Synthetic aperture radar (SAR) is the primary tool used for monitoring such vast, inaccessible regions. Ice maps of different regions are generated each day in support of navigation operations and environmental assessments. Currently, operators digitally segment the SAR data manually using primarily tone and texture visual characteristics. Regions containing multiple ice types are identified, however, it is not feasible to produce a pixel-based segmentation due to time constraints. In this research, advanced methods for performing texture feature extraction, incorporating tonal features, and performing the segmentation are presented. Examples of the segmentation of a SAR image that is difficult to segment manually and that requires the inclusion of both tone and texture features are presented.

}, keywords = {Canada, Canadian Ice Service, environmental assessments, Feature Extraction, geophysical signal processing, government agency, ice maps, image classification, image segmentation, image texture, navigation operations, oceanographic regions, operational segmentation, pixel based segmentation, radar imaging, SAR image, SAR sea ice imagery, sea ice, synthetic aperture radar, texture visual characteristics, tone visual characteristics}, doi = {http://dx.doi.org/10.1109/WARSD.2003.1295204}, author = {D A. Clausi and H Deng} } @inproceedings {592, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @inproceedings {449, title = {An advanced computational method to determine co-occurrence probability texture features}, booktitle = {IEEE International Geoscience and Remote Sensing Symposium}, year = {2002}, abstract = {

A critical shortcoming of determining co-occurrence probability texture features using Haralick\&$\#$39;s popular grey level co-occurrence matrix (GLCM) is the excessive computational burden. Here, a more robust algorithm (the grey level cooccurrence integrated algorithm or GLCIA) to perform this task is presented. The GLCIA is created by integrating the preferred aspects of two algorithms: the grey level cooccurrence hybrid structure (GLCHS) and the grey level cooccurrence hybrid histogram (GLCHH). The GLCHS utilizes a dedicated 2-d data structure to quickly generate the probabilities and apply statistics to generate the features. The GLCHH uses a more efficient 1-d data structure to perform the same tasks. Since the GLCHH is faster than the GLCHS yet the GLCHH is not able to calculate features using all available statistics, the integration of these two methods generates a superior algorithm (the GLCIA). The computational gains vary as a function of window size, quantization level, and statistics selected. The GLCIA computational time relative to that of the standard GLCM method ranges from 0.04\% to 16\%. The GLCIA is a highly recommended technique for anyone wishing to calculate co-occurrence probability texture features, especially from large-scale digital imagery.

}, keywords = {algorithm, co-occurrence probability, co-occurrence probability texture features, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, GLCHH, GLCHS, GLCIA, grey level co occurrence hybrid histogram, grey level co occurrence hybrid structure, grey level co occurrence integrated algorithm, image texture, land surface, Remote Sensing, terrain mapping, texture feature}, doi = {http://dx.doi.org/10.1109/IGARSS.2002.1026575}, author = {D A. Clausi and Y Zhao} } @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 {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} } @article {574, title = {Designing Gabor filters for optimal texture separability}, journal = {Pattern Recognition}, volume = {33}, year = {2000}, pages = {1835 - 1849}, abstract = {

The discrimination of textures is a critical aspect of identification in digital imagery. Texture features generated by Gabor filters have been increasingly considered and applied to image analysis. Here, a comprehensive classification and segmentation comparison of different techniques used to produce texture features using Gabor filters is presented. These techniques are based on existing implementations as well as new, innovative methods. The functional characterization of the filters as well as feature extraction based on the raw filter outputs are both considered. Overall, using the Gabor filter magnitude response given a frequency bandwidth and spacing of one octave and orientation bandwidth and spacing of 303 augmented by a measure of the texture complexity generated preferred results.

}, keywords = {Classification Segmentation, Feature Extraction, Gabor filter, texture analysis}, author = {D A. Clausi and E Jernigan} } @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 {510, title = {Automated analysis and detection of cracks in underground scanned pipes}, booktitle = {International Conference on Image Processing}, year = {1999}, abstract = {

Closed circuit television (CCTV) surveys are used widely in North America to assess the structural integrity of underground pipes. The video images are examined visually and classified into grades according to degrees of damage. The human eye is extremely effective at recognition and classification, but it is not suitable for assessing pipe defects in thousands of miles of pipeline images due to fatigue, subjectivity and cost. This paper presents ongoing research into the automatic assessment of the structural condition of underground pipes for the purpose of preventive maintenance by municipalities

}, keywords = {CCTV, civil engineering computing, closed circuit television, crack analysis, Crack detection, Feature Extraction, image classification, image recognition, maintenance engineering, pipe defects, preventive maintenance, underground pipe structural integrity, underground scanned pipes, video images, video signal processing}, doi = {http://dx.doi.org/10.1109/ICIP.1999.819622}, author = {P Fieguth and S K. Sinha} } @inproceedings {516, title = {Underground pipe cracks classification using image analysis and neuro-fuzzy algorithm}, booktitle = {IEEE Symposium on Intelligent Control}, year = {1999}, abstract = {

Pipeline surface defects such as cracks cause major problems for asset managers, particularly when the pipe is buried under the ground. The manual inspection of surface defects in the underground pipes has a number of drawbacks, including subjectivity, varying standards, and high costs. Automatic inspection systems using image processing and artificial intelligence techniques can overcome many of these disadvantages and offer asset managers an opportunity to significantly improve quality and reduce costs. A recognition and classification method for pipe cracks using image analysis and a neuro-fuzzy algorithm is proposed. In the pre-processing step, the cracks in the pipe are extracted from the homogenous background. Then, based on prior knowledge of cracks, five normalised features are extracted. In the classification step, a neuro-fuzzy algorithm is proposed that employs a trapezoidal fuzzy membership function and modified error backpropagation algorithm

}, keywords = {asset managers, automatic inspection systems, automatic optical inspection, backpropagation, civil engineering, eigenvalues and eigenfunctions, Feature Extraction, fuzzy neural nets, homogenous background, Hough transforms, image analysis, image classification, Mathematical morphology, modified error backpropagation algorithm, neuro-fuzzy algorithm, statistical analysis, surface defects, trapezoidal fuzzy membership function, underground pipe cracks classification}, doi = {http://dx.doi.org/10.1109/ISIC.1999.796688}, author = {S K. Sinha and F Karray and P Fieguth} } @article {471, title = {A fast method to determine co-occurrence texture features}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {36}, year = {1998}, pages = {298 - 300}, abstract = {

A critical shortcoming of determining texture features derived from grey-level co-occurrence matrices (GLCM\&$\#$39;s) is the excessive computational burden. This paper describes the implementation of a linked-list algorithm to determine co-occurrence texture features far more efficiently. Behavior of common co-occurrence texture features across difference grey-level quantizations is investigated

}, keywords = {co-occurrence texture feature, fast method, Feature Extraction, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey-level co-occurrence matrices, grey-level quantization, image feature, image processing, image texture, land surface, linked-list algorithm, occurrence matrix, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/36.655338}, author = {D A. Clausi and E Jernigan} }