@article {125, title = {Unsupervised operational SAR sea ice image classification}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {50}, year = {2012}, pages = {4397-4408}, abstract = {

Thousands of spaceborne synthetic aperture radar (SAR) sea-ice images are systematically processed every year in support of operational activities such as ship navigation and environmental monitoring. An automated approach that generates a pixel-level sea-ice image classification is required since manual pixel-level classification is not feasible. Currently, using a standardized approach, trained ice analysts manually segment full SAR scenes into smaller polygons to record ice types and concentrations. Using this data, pixel-level classification can be achieved by initial unsupervised segmentation of each polygon followed by automatic sea-ice labeling of the full scene. A fully automated Markov random field model that is used to assign labels to all segmented regions in the full scene has been designed and implemented. This approach is the first known successful end-to-end process for operational SAR sea-ice image classification. In addition, a novel performance evaluation framework has been developed to validate the segmentation and labeling of SAR sea-ice images. A trained sea-ice expert has conducted an arms length evaluation using this framework to generate a set of full scene reference images used for testing. Testing demonstrates operational success of the labeling approach.

}, keywords = {image classification, Markov random field (MRF), sea-ice, Synthetic, unsupervised segmentation}, author = {S Ochilov and D A. Clausi} } @article {179, title = {Synthesis of remote sensing label fields using a tree-structured hierarchical model}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {49}, year = {2011}, pages = {2060-2070}, abstract = {

The systematic evaluation of synthetic aperture radar (SAR) data analysis tools, such as segmentation and classification algorithms for geographic information systems, is difficult given the unavailability of ground-truth data in most cases. Therefore, testing is typically limited to small sets of pseudoground-truth data collected manually by trained experts, or primitive synthetic sets composed of simple geometries. To address this issue, we investigate the potential of employing an alternative approach, which involves the synthesis of SAR data and corresponding label fields from real SAR data for use as a reliable evaluation testbed. Given the scale-dependent nonstationary nature of SAR data, a new modeling approach that combines a resolution-oriented hierarchical method with a region-oriented binary tree structure is introduced to synthesize such complex data in a realistic manner. Experimental results using operational RADARSAT SAR sea-ice data and SIR-C/X-SAR land-mass data show that the proposed hierarchical approach can better model complex nonstationary scale structures than local MRF approaches and existing nonparametric methods, thus making it well suited for synthesizing SAR data and the corresponding label fields for potential use in the systematic evaluation of SAR data analysis tools.

}, keywords = {complex nonstationary scale structures, geographic information systems, geophysical image processing, image classification, image segmentation, Markov processes, operational RADARSAT SAR sea ice data, pseudoground truth data, region-oriented binary tree structure, remote sensing by radar, remote sensing label fields, resolution-oriented hierarchical method, SAR data analysis tools, sea ice, SIR-C/X-SAR land mass data, synthetic aperture radar, tree data structures, tree-structured hierarchical model}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2010.2102765}, author = {Y Liu and A Wong and P Fieguth} } @article {271, title = {Fusing AMSR-E and QuikSCAT imagery for improved sea ice recognition}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {47}, year = {2010}, pages = {1980 - 1989}, abstract = {

The benefits of augmenting Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) image data with Quick Scatterometer (QuikSCAT) image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E-only data set against using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal component analysis (PCA) can be used to reduce the dimensionality of the data. The reliability of training data over time was also investigated. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreases it significantly when a sufficient number of bands are used. Combining these data sets is beneficial for sea ice mapping. Using all available bands is recommended, data fusion with PCA does not offer any benefit for these data, and training data from a specific date remains reliable within 30 days.

}, keywords = {Advanced Microwave Scanning Radiometer for the Earth Observing System, AMSR-E-QuikSCAT imagery fusion, data dimensionality reduction, geophysics computing, image classification, image fusion, maximum likelihood classifier, oceanographic regions, PCA, principal component analysis, Quick Scatterometer, Remote Sensing, sea ice, sea ice mapping, sea ice recognition, supervised sea ice classification, training data reliability, Western Arctic region}, doi = {http://dx.doi.org/10.1109/TGRS.2009.2013632}, author = {P Yu and D A. Clausi and S Howell} } @inproceedings {253, title = {Image synthesis using conditional random fields}, booktitle = {17th IEEE International Conference on Image Processing (ICIP)}, year = {2010}, abstract = {

Methods of scientific imaging and image analysis have become pervasive in a great variety of fields, including the properties of porous media. To study the large-scale morphological properties of porous media, high resolution random (Monte Carlo) samples are required. The purpose of this paper is to propose a novel approach for the statistical synthesis of scientific images, based on the concept of Conditional Random Fields. We explore two different sets of potential functions are used to model the pore-structure characteristics, and Monte Carlo Markov chain methods are also used to sample the high resolution images from the trained model. The resulting images are of high quality, and show the performance of the proposed framework.

}, keywords = {Conditional Random Fields, image analysis, image classification, image sampling, image synthesis, large-scale morphological properties, Monte Carlo methods, porous media, Scientific Imaging}, doi = {http://dx.doi.org/10.1109/ICIP.2010.5653134}, author = {E Ahmadi and Z Azimifar and P Fieguth and S Ayatollahi} } @article {224, title = {MAGIC: MAp-Guided Ice Classification system for operational analysis}, journal = {Canadian Journal of Remote Sensing}, volume = {36}, year = {2010}, pages = {S13 - S25}, abstract = {

A map-guided ice classification (MAGIC) system that aims at effectively interpreting SAR sea ice images using the associated ice charts in an operational environment is presented. As an ongoing project, MAGIC version 1.0 has been developed using operational SAR image data from the Canadian ice service (CIS). MAGIC is intended to not only be used for SAR sea ice classification research and development, but also used for classification research of generic digital imagery using the available fundamental and advanced algorithms. At some point, we hope to make the system freely available.

}, keywords = {Canadian Ice Service, image classification, MAGIC, map-guided ice classification, operational analysis, radar imaging, SAR sea ice images, sea ice, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783172}, author = {D A. Clausi and K Qin and M S. Chowdhury and P Yu and P Maillard} } @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 {287, title = {IceSynth: An image synthesis system for sea-ice segmentation evaluation}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, address = {Kelowna, British Columbia, Canada}, abstract = {

An ongoing challenge in automatic sea-ice monitoring using synthetic aperture radar (SAR) is the automatic segmentation of SAR sea-ice images based on the underlying ice type. Given the intractability of obtaining ground-truth segmentation data from polar regions, the evaluation of automatic SAR sea-ice image segmentation algorithms is generally limited to tests using real SAR imagery based on pseudo-ground truth data (e.g., manual segmentations) and simple synthetic tests using basic shape primitives. As such, it is difficult to evaluate automatic segmentation algorithms in a systematic and reliable manner using realistic scenarios. To tackle this issue, a novel image synthesis system named IceSynth is presented, which is capable of generating a variety of synthetic sea-ice images that are representative of real SAR sea-ice imagery. In IceSynth, SAR sea-ice textures for each ice type are synthesized via stochastic sampling based on non-parametric local conditional texture probability distribution estimates. A stochastic sampling approach based on non-parametric local class probability distribution estimates is used to generate large-scale sea-ice structures of various ice types based on ice classification priors extracted from real SAR sea-ice imagery. Experimental results show that IceSynth is capable of generating realistic-looking SAR sea-ice images that are well-suited for performing objective evaluation of SAR sea-ice image segmentation algorithms.

}, keywords = {automatic sea-ice monitoring, ice classification, ice types, IceSynth, image classification, image segmentation, image synthesis system, image texture, oceanographic techniques, polar regions, probability, probability distribution estimation, pseudo-ground truth data, remote sensing by radar, SAR imagery, sea ice, sea-ice segmentation evaluation, sea-ice structures, sea-ice textures, stochastic processes, stochastic sampling, synthetic aperture radar}, doi = {http://dx.doi.org/10.1109/CRV.2009.27}, author = {A Wong and W Zhang and D A. Clausi} } @inproceedings {328, title = {Combining AMSR-E and QuikSCAT image data to improve sea ice classification}, booktitle = {5th Workshop on Pattern Recognition for Remote Sensing}, year = {2008}, month = {12/2008}, address = {Tampa, Florida, USA}, abstract = {

The benefits of augmenting AMSR-E image data with QuikSCAT image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E only data set against the combined data and examined the preferred number of features to use as well as the reliability of training data over time. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreased it significantly when enough features are used. Combining these data sets is beneficial for sea ice mapping. Using all available features is recommended and training data from a specific date remains reliable within 30 days.

}, keywords = {AMSR-E image data, classifier accuracy, hydrological techniques, image classification, maximum likelihood classifier, maximum likelihood estimation, QuikSCAT image data, sea ice, supervised sea ice classification, terrain mapping, Western Arctic region}, doi = {http://dx.doi.org/10.1109/PRRS.2008.4783170}, author = {P Yu and D A. Clausi and R De Abreu and T Agnew} } @inproceedings {366, title = {A medical texture local binary pattern for TRUS prostate segmentation}, booktitle = {29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society}, year = {2007}, abstract = {

Prostate cancer diagnosis and treatment rely on segmentation of Transrectal Ultrasound (TRUS) prostate images. This is a challenging and difficult task dut to weak prostate boundaries, speckle noise and the short range of gray levels. Advances in digital imaging techniques have made it possible the acquisition of large volumes of TRUS prostate images so that there is considerable demand for automated segmentation systems. Local binary pattern (LBP) has been used for texture segmentation and analysis. Despite its promising performance for texture classification it has not yet been considered for TRUS prostate segmentation. In this paper we introduce a medical texture local binary pattern operator designed for applications of medical imaging where different tissues or micro organisms might maintain extremely weak underlying textures that make it impossible or very difficult for ordinary texture analysis approaches to classify them. In the proposed method the deformations of a level set contour are controlled based on the medical texture local binary pattern operator.

}, keywords = {automated segmentation systems, biomedical ultrasonics, cancer, digital imaging techniques, image classification, image segmentation, image texture, LBP, level set contour deformations, medical image processing, medical texture local binary pattern, prostate cancer diagnosis, prostate cancer treatment, short gray level range, speckle noise, texture analysis, Texture classification, texture segmentation, transrectal ultrasound prostate image segmentation, TRUS prostate segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2007.4353617}, author = {N Nezamoddin Kachouie and P Fieguth} } @inproceedings {749, title = {An elliptical level set method for automatic TRUS prostate image segmentation}, booktitle = {IEEE International Symposium on Signal Processing and Information Technology}, year = {2006}, abstract = {

One of the most important tasks in prostate cancer diagnosis and treatment is segmentation of transrectal ultrasound (TRUS) prostate images. Due to the large volumes of TRUS prostate images, automatic segmentation systems are mandatory. Weak prostate boundaries, speckle noise and the short range of gray levels make the task more challenging and difficult. Deformable models have been considered as an effective approach for semi-automatic prostate segmentation. However the main problem toward a fully automatic segmentation system using deformable models is initialization of seed or control points. In this paper an automatic level set prostate segmentation is presented. A classification method is employed to locate the approximate location of the prostate which is used to initiate the proposed elliptical level set contour. The deformations of the level set are guided by a velocity function which is derived using the TRUS prostate image histogram

}, keywords = {automatic level set prostate segmentation, automatic TRUS prostate image segmentation, biomedical ultrasonics, cancer, classification method, elliptical level set method, image classification, image segmentation, medical image processing, prostate cancer diagnosis, speckle noise, transrectal ultrasound, velocity function, weak prostate boundaries}, doi = {http://dx.doi.org/10.1109/ISSPIT.2006.270795}, author = {N Kachouie and N Nezamoddin and P Fieguth and S Rahnamayan} } @article {383, title = {Filament preserving model (FPM) segmentation applied to SAR sea-ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {44}, year = {2006}, pages = {3687 - 3694}, abstract = {

Modeling spatial context constraints using a Markov random field (MRF) has been widely used in the segmentation of noisy images. Its applicability to synthetic aperture radar (SAR) sea-ice segmentation has also been demonstrated recently. 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 necessary for identifying certain ice types. In this paper, a new statistical context model is proposed that, within the same scene, can simultaneously preserve narrow elongated features while producing similar smooth segmentation results comparable to typical MRF-based approaches. Tested on one synthetic image and two SAR sea-ice scenes, this filament preserving model substantially improves classification accuracies when compared to standard Gaussian mixture and MRF-based segmentation algorithms

}, keywords = {egg code, filament preserving model, image classification, image segmentation, Markov processes, Markov random field, MRF-based segmentation algorithm, noisy image segmentation, oceanographic techniques, remote sensing by radar, SAR sea-ice imagery, sea ice, ship navigation, standard Gaussian mixture, synthetic aperture radar, synthetic image}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2006.885046}, author = {Q Yu and D A. Clausi} } @inproceedings {394, title = {Joint image segmentation and interpretation using iterative semantic region growing on SAR sea ice imagery}, booktitle = {18th International Conference on Pattern Recognition (ICPR)}, year = {2006}, month = {08/2006}, address = {Hong Kong}, abstract = {

Segmentation of images into disjoint regions and interpretation of the regions for semantic meanings are two central tasks in an image analysis system. Typically, the segmentation and interpretation are performed separately with the interpretation as a post processing of segmentation. In this paper, we use an iterative method that keeps refining the segmentation and producing semantic class labels at the same time. The segmentation algorithm is based on a region growing technique and the interpretation is a Markov random field (MRF) based classification. The two processes are integrated under the Bayesian framework, with both aiming at reducing a defined energy. The interactions between the two are bidirectional by letting the interpretation result have some degree of control on the region growing process. Various features can hence be efficiently combined, and accurate classifications are obtained for operational synthetic aperture radar (SAR) sea ice applications

}, keywords = {Bayes methods, Bayesian framework, image analysis, image classification, image interpretation, image segmentation, iterative methods, iterative semantic region growing, Markov processes, Markov random field based classification, radar imaging, SAR sea ice imagery, sea ice, semantic class labels, synthetic aperture radar, terrain mapping}, doi = {http://dx.doi.org/10.1109/ICPR.2006.734}, author = {Q Yu and D A. Clausi} } @inproceedings {423, title = {Adaboost and support vector machines for white matter lesion segmentation in MR images}, booktitle = {27th Annual International Conference of the Engineering in Medicine and Biology Society}, year = {2005}, abstract = {

The use of two powerful classification techniques (boosting and SVM) is explored for the segmentation of white-matter lesions in the MRI scans of human brain. Simple features are generated from proton density (PD) scans. Radial basis function (RBF) based Adaboost technique and support vector machines (SVM) are employed for this task. The classifiers are trained on severe, moderate and mild cases. The segmentation is performed in T1 acquisition space rather than standard space (with more slices). Hence, the proposed approach requires less time for manual verification. The results indicate that the proposed approach can handle MR field inhomogeneities quite well and is completely independent from manual selection process so that it can be run under batch mode. Segmentation performance comparison with manual detection is also provided

}, keywords = {Adaboost, biomedical MRI, brain, human brain, image classification, image segmentation, medical image processing, MR images, proton density scans, radial basis function, radial basis function networks, support vector machines, T1 acquisition, white matter lesion segmentation}, doi = {http://dx.doi.org/10.1109/IEMBS.2005.1616447}, author = {A Quddus and P Fieguth and O Basir} } @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 {643, title = {Comparing classification metrics for labeling segmented remote sensing images}, booktitle = {2nd Annual Canadian Conference on Computer and Robot Vision}, year = {2005}, month = {05/2005}, address = {Victoria, B.C., Canada}, abstract = {

Image segmentation and labelling are the two conceptual operations in image classification. As the remote sensing community uses more powerful segmentation procedures with spatial constraint, new possibilities can be explored for labelling. Instead of assigning a label to a single observation (pixel), whole segments of image are labelled at once implying the use of multivariate samples rather than pixel vectors. This approach to image classification also offers new possibilities for using a priori information about the classes such as existing maps or object signature libraries. The present paper addresses the two issues. First a labelling scheme is presented that gathers evidence about the classes from incomplete a priori information using a \"cognitive reasoning\" approach. Then, five different metrics are compared for the label assignment and are combined through a voting scheme. The results show that very different results can be obtained depending on the metric chosen. The metric combination through voting, being a suboptimal approach does not necessarily provide the best results but could be a safe alternative to choosing only one metric.

}, keywords = {a priori information, classification metrics, cognitive reasoning, image classification, image labelling, image segmentation, label assignment, multivariate samples, Remote Sensing, segmented remote sensing images, voting scheme}, doi = {http://dx.doi.org/10.1109/CRV.2005.28}, author = {P Maillard and D A. Clausi} } @article {415, title = {Design-based texture feature fusion using Gabor filters and co-occurrence probabilities}, volume = {14}, year = {2005}, pages = {925 - 936}, abstract = {

A design-based method to fuse Gabor filter and grey level co-occurrence probability (GLCP) features for improved texture recognition is presented. The fused feature set utilizes both the Gabor filter\&$\#$39;s capability of accurately capturing lower and mid-frequency texture information and the GLCP\&$\#$39;s capability in texture information relevant to higher frequency components. Evaluation methods include comparing feature space separability and comparing image segmentation classification rates. The fused feature sets are demonstrated to produce higher feature space separations, as well as higher segmentation accuracies relative to the individual feature sets. Fused feature sets also outperform individual feature sets for noisy images, across different noise magnitudes. The curse of dimensionality is demonstrated not to affect segmentation using the proposed the 48-dimensional fused feature set. Gabor magnitude responses produce higher segmentation accuracies than linearly normalized Gabor magnitude responses. Feature reduction using principal component analysis is acceptable for maintaining the segmentation performance, but feature reduction using the feature contrast method dramatically reduced the segmentation accuracy. Overall, the designed fused feature set is advocated as a means for improving texture segmentation performance.

}, keywords = {Algorithms, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, design-based texture feature fusion, feature contrast, feature reduction, Gabor filter, grey level cooccurrence probability, image classification, image enhancement, image interpretation, image segmentation, image segmentation classification, image texture, Information Storage and Retrieval, Models, Pattern Recognition, principal component analysis, probability, Statistical, Subtraction Technique, texture recognition}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2005.849319}, author = {D A. Clausi and H Deng} } @article {417, title = {Operational map-guided classification of SAR sea ice imagery}, journal = {IEEE Transactions on Geoscience and Remote Sensing}, volume = {43}, year = {2005}, pages = {2940 - 2951}, abstract = {

This paper presents a map-guided sea ice classification system built to work in parallel with the Canadian Ice Service (CIS) operations to produce pixel-based ice maps that complement actual \"egg code\" maps produced by CIS. The system uses the CIS maps as input to guide classification by providing information on the number of ice types and their final label for specific regions. Segmentation is based on a modified adaptive Markov random field (MRF) model that uses synthetic aperture radar (SAR) intensities and texture features as input. The ice type labeling is performed automatically by gathering evidences based on a priori information on one or two classes and deducing the other labels iteratively by comparing distributions of segments. Three methods for comparing the segment distributions (Fisher criterion, Mahalanobis distance, and Kolmogorov-Smirnov test) were implemented and compared. The system is fully described with special attention to the labeling procedure. Examples are presented in the form of two CIS SAR-based ice maps from the Gulf of Saint Lawrence region and one example from the Beaufort Sea. The results indicate that when the segmentation is good, the labeling attains best results (between 71\% and 89\%) based on evaluation by a sea ice analyst. Some problems remain to be assessed which are primarily attributable to discrepancies in the information provided by the egg code and what is actually visible in the SAR image. Subscale information on floe size and shape available to human analysts, but not in this classification system, also appear to be a critical information for separating some ice types.

}, keywords = {Beaufort Sea, Canadian Ice Service, Fisher criterion, geophysical signal processing, gray-level cooccurrence matrix, Gulf of Saint Lawrence, image classification, image segmentation, image texture, Kolmogorov-Smirnov test, Mahalanobis distance, map-guided classification, Markov processes, Markov random field, oceanographic regions, oceanographic techniques, pixel-based ice maps, radar imaging, remote sensing by laser beam, remote sensing by radar, SAR sea ice imagery, sea ice, sea ice classification, segment distribution comparison, synthetic aperture radar}, issn = {0196-2892}, doi = {http://dx.doi.org/10.1109/TGRS.2005.857897}, author = {P Maillard and D A. Clausi and H Deng} } @article {690, title = {Gaussian MRF rotation-invariant features for image classification}, journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence}, volume = {26}, year = {2004}, pages = {951 - 955}, abstract = {

Features based on Markov random field (MRF) models are sensitive to texture rotation. This paper develops an anisotropic circular Gaussian MRF (ACGMRF) model for retrieving rotation-invariant texture features. To overcome the singularity problem of the least squares estimate method, an approximate least squares estimate method is designed and implemented. Rotation-invariant features are obtained from the ACGMRF model parameters using the discrete Fourier transform. The ACGMRF model is demonstrated to be a statistical improvement over three published methods. The three methods include a Laplacian pyramid, an isotropic circular GMRF (ICGMRF), and gray level cooccurrence probability features.

}, keywords = {Algorithms, anisotropic circular Gaussian MRF model, Artificial Intelligence, Automated, Computer Simulation, Computer-Assisted, discrete Fourier transform, discrete Fourier transforms, Gaussian MRF rotation-invariant features, gray level cooccurrence probability features, image classification, image enhancement, image interpretation, image texture, Laplacian pyramid, Markov Chains, Markov processes, Markov Random Field models, Models, Normal Distribution, Pattern Recognition, Rotation, rotation-invariant texture features, Statistical, statistical improvement, texture rotation}, issn = {0162-8828}, doi = {http://dx.doi.org/10.1109/TPAMI.2004.30}, author = {H Deng and D A. Clausi} } @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 {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} } @inproceedings {501, title = {Fast retrieval methods for images with significant variations}, booktitle = {International Conference on Image Processing}, year = {2000}, abstract = {

Fast image retrieval is the key to success for operations on large image databases, and a great many techniques have been developed for efficient retrieval. However, most of these methods are tailored to visual scenes or to images having limited variations. We investigate the searching of enormous databases (of up to 10 7 images) for the matching and identification of precious stones (principally diamonds). Because of the size of the database, we propose a hierarchy of classifiers, which successively prune candidate images such that the more complex classifiers are required to test only tiny portions of the data. The new classifier developed here applies a wavelet transform to image histograms and is capable of rejecting 99.9\% of bad matches.

}, keywords = {classifiers hierarchy, database searching, diamond, diamonds, fast image retrieval methods, image classification, image histograms, image matching, image retrieval, large image databases, precious stones identification, precious stones matching, visual databases, visual scenes, wavelet transform, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2000.899470}, author = {P Fieguth and R Wan} } @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} }