@inproceedings {24, title = {Illumination Robust Facial Feature Detection via Decoupled Illumination and Texture Features}, booktitle = {International Conference on Image Analysis and Recognition 2015}, year = {2015}, abstract = {

A method for illumination robust facial feature detection on frontal images of the human face is proposed. Illumination robust features are produced from weighted contributions of the texture and illumination components of an image where the illumination is estimated via Bayesian least-squares minimization with the required posterior probability inferred using an adaptive Monte-Carlo sampling approach. This estimate is used to decouple the illumination and texture components, from which Haar-like features are extracted. A weighted aggregate of each component\&$\#$39;s features is then compared with a cascade of pre-trained classifiers for the face, eyes, nose, and mouth. Experimental results against the Yale Face Database B suggest higher sensitivity and F1 score values than current methods while maintaining comparable specificity and accuracy in the presence of non-ideal illumination conditions.

}, keywords = {illumination robust, image processing, object detection}, author = {B Chwyl and A Wong and D A. Clausi} } @inproceedings {199, title = {Despeckling vs averaging of retinal UHROCT tomograms: advantages and limitations}, booktitle = {Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XV}, year = {2011}, publisher = {SPIE}, organization = {SPIE}, abstract = {

Imaging time can be reduced using despeckled tomograms, which have similar image metrics to those obtained by averaging several low speed tomograms or many high speed tomograms. Quantitative analysis was used to compare the performance of two speckle denoising approaches, algorithmic despeckling and frame averaging, as applied to retinal OCT images. Human retinal tomograms were acquired from healthy subjects with a research grade 1060nm spectral domain UHROCT system with 5μm axial resolution in the retina. Single cross-sectional retinal tomograms were processed with a novel speckle denoising algorithm and compared with frame averaged retinal images acquired at the same location. Image quality metrics such as the image SNR and contrast-to-noise ratio (CNR) were evaluated for both cases.

}, keywords = {biomedical optics, image processing, medical imaging, optical coherence tomography, speckle}, doi = {http://dx.doi.org/10.1117/12.875867}, url = {http://link.aip.org/link/?PSI/7889/78893C/1}, author = {D D. Lee and K Bizheva and A Wong and J A. Eichel and P Fieguth and D A. Clausi}, editor = {J A. Izatt and J G. Fujimoto and V V. Tuchin} } @article {173, title = {Enhanced seam carving via integration of energy gradient functionals}, journal = {IEEE Signal Processing Letters}, volume = {18}, year = {2011}, pages = {375 - 378}, abstract = {

This letter proposes an improved seam carving approach for content-aware image retargeting. The proposed algorithm extends upon the backward and forward energy cost functionals used in previous seam carving methods by incorporating an energy gradient cost functional in the optimization process. This combined absolute energy cost functional penalizes seam candidates that cross areas of local extrema, which characterizes regions with high concentrations of $\#$x201C;important $\#$x201D; image detail. Experiments show superior visual quality results using the proposed absolute energy cost functional over existing methods on a variety of images characterized by high image detail concentration.

}, keywords = {backward energy cost functionals, content-aware image retargeting, energy gradient functional integration, enhanced seam carving approach, forward energy cost functionals, image processing, local extrema, optimization, optimization process, visual quality}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2011.2140396} } @article {174, title = {Enhanced seam carving via integration of energy gradient functionals}, journal = {IEEE Signal Processing Letters}, volume = {18}, year = {2011}, pages = {375 - 378}, abstract = {

This letter proposes an improved seam carving approach for content-aware image retargeting. The proposed algorithm extends upon the backward and forward energy cost functionals used in previous seam carving methods by incorporating an energy gradient cost functional in the optimization process. This combined absolute energy cost functional penalizes seam candidates that cross areas of local extrema, which characterizes regions with high concentrations of $\#$x201C;important $\#$x201D; image detail. Experiments show superior visual quality results using the proposed absolute energy cost functional over existing methods on a variety of images characterized by high image detail concentration.

}, keywords = {backward energy cost functionals, content-aware image retargeting, energy gradient functional integration, enhanced seam carving approach, forward energy cost functionals, image processing, local extrema, optimization, optimization process, visual quality}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2011.2140396} } @article {175, title = {Enhanced seam carving via integration of energy gradient functionals}, journal = {IEEE Signal Processing Letters}, volume = {18}, year = {2011}, pages = {375 - 378}, abstract = {

This letter proposes an improved seam carving approach for content-aware image retargeting. The proposed algorithm extends upon the backward and forward energy cost functionals used in previous seam carving methods by incorporating an energy gradient cost functional in the optimization process. This combined absolute energy cost functional penalizes seam candidates that cross areas of local extrema, which characterizes regions with high concentrations of $\#$x201C;important $\#$x201D; image detail. Experiments show superior visual quality results using the proposed absolute energy cost functional over existing methods on a variety of images characterized by high image detail concentration.

}, keywords = {backward energy cost functionals, content-aware image retargeting, energy gradient functional integration, enhanced seam carving approach, forward energy cost functionals, image processing, local extrema, optimization, optimization process, visual quality}, issn = {1070-9908}, doi = {http://dx.doi.org/10.1109/LSP.2011.2140396} } @article {218, title = {An adaptive Monte Carlo approach to phase-based multimodal image registration}, journal = {IEEE Transactions on Information Technology in Biomedicine}, volume = {14}, year = {2010}, pages = {173 - 179}, abstract = {

In this paper, a novel multiresolution algorithm for registering multimodal images, using an adaptive Monte Carlo scheme is presented. At each iteration, random solution candidates are generated from a multidimensional solution space of possible geometric transformations, using an adaptive sampling approach. The generated solution candidates are evaluated based on the Pearson type-VII error between the phase moments of the images to determine the solution candidate with the lowest error residual. The multidimensional sampling distribution is refined with each iteration to produce increasingly more plausible solution candidates for the optimal alignment between the images. The proposed algorithm is efficient, robust to local optima, and does not require manual initialization or prior information about the images. Experimental results based on various real-world medical images show that the proposed method is capable of achieving higher registration accuracy than existing multimodal registration algorithms for situations, where little to no overlapping regions exist.

}, keywords = {adaptive Monte Carlo scheme, adaptive sampling approach, Algorithms, Computer-Assisted, Diagnostic Imaging, geometric transformation, Humans, image interpretation, image phase moments, image processing, image registration, iterative methods, Least-Squares Analysis, medical image processing, Monte Carlo Method, Monte Carlo methods, multidimensional sampling distribution, multidimensional solution space, multiresolution algorithm, optimal image alignment, Pearson type VII error, phase based multimodal image registration, random solution candidate generation, sampling methods}, issn = {1089-7771}, doi = {http://dx.doi.org/10.1109/TITB.2009.2035693}, author = {A Wong} } @article {269, title = {Alignment of confocal scanning laser ophthalmoscopy photoreceptor images at different polarizations using complex phase relationships}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {56}, year = {2010}, abstract = {

A polarimetric technique for enhancing fundus images was recently introduced , where confocal scanning laser ophthalmoscopy (CSLO) images are acquired under differing incoming polarization states, and spatially resolved Mueller images are constructed based on the images. An important stage in this technique is the alignment of CSLO images acquired under differing polarization states. This has proven to be particularly difficult when dealing with photoreceptor images, which are characterized by poor SNRs and intensity inhomogeneities due to polarization properties. In this paper, an automated approach to aligning CSLO photoreceptor images acquired under differing polarization states is presented. A novel energy functional based on complex phase relationships is introduced that is invariant to polarization and scale, as well as robust to noise and highly sensitive to photoreceptor structural characteristics. A sequential quadratic programming approach is employed to determine the optimal alignment between the photoreceptor images by minimizing the proposed energy functional. The method has been tested on CSLO fish photoreceptor images acquired under differing polarization states and evaluated based on alignment accuracy. The results demonstrate that the proposed method outperforms existing techniques used for aligning CSLO images, with lower mean alignment error for all test cases.

}, keywords = {Algorithms, Animals, bio-optics, biomedical optical imaging, complex phase relationship, Computer-Assisted, Confocal, confocal scanning laser ophthalmoscopy, enhancing fundus image, eye, Fishes, Fundus Oculi, image enhancement, image processing, incoming polarization states, laser applications in medicine, light polarisation, medical image processing, Microscopy, Ophthalmoscopy, Photoreceptor Cells, photoreceptor image, polarimetry, Polarization, quadratic programming, sequential quadratic programming, spatially resolved Mueller image construction, Thermodynamics, Vertebrate}, issn = {0018-9294}, doi = {http://dx.doi.org/10.1109/TBME.2009.2017510}, author = {A Wong} } @article {221, title = {Generalized probabilistic scale space for image restoration}, journal = {IEEE Transactions on Image Processing}, volume = {19}, year = {2010}, pages = {2774 - 2780}, abstract = {

A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition in the context of image restoration. Experimental results using 2-D images show that generalized sampling-based probabilistic scale-space theory can be used to produce more accurate restored images when compared with state-of-the-art scale-space formulations, particularly under situations characterized by low signal-to-noise ratios and image degradation.

}, keywords = {2D image, Bayes Theorem, Computer-Assisted, generalized sampling-based probabilistic scale space theory, Humans, image degradation, image processing, image restoration, image sampling, Male, noise model, nonlinear dynamics, Normal Distribution, Photography, probability, Prostate, signal-to-noise ratio, Ultrasonography}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/TIP.2010.2048973}, author = {A Wong and A Mishra} } @inproceedings {286, title = {Efficient target recovery using STAGE for mean-shift tracking}, booktitle = {6th Canadian Conference on Computer and Robot Vision}, year = {2009}, month = {05/2009}, abstract = {

Robust visual tracking is a challenging problem, especially when a target undergoes complete occlusion or leaves and later re-enters the camera view. The mean-shift tracker is an efficient appearance-based tracking algorithm that has become very popular in recent years. Many researchers have developed extensions to the algorithm that improve the appearance model used in target localization. We approach the problem from a slightly different angle and seek to improve the robustness of the mean-shift tracker by integrating an efficient failure recovery mechanism. The proposed method uses a novel application of the STAGE algorithm to efficiently recover a target in the event of tracking failure. The STAGE algorithm boosts the performance of a local search algorithm by iteratively learning an evaluation function to predict good states for initiating searches. STAGE can be viewed as a random-restart algorithm that chooses promising restart states based on the shape of the state space, as estimated using the search trajectories from previous iterations. In the proposed method, an adapted version of STAGE is applied to the mean-shift target localization algorithm (Bhattacharyya coefficient maximization using the mean-shift procedure) to efficiently recover the lost target. Experiments indicate that the proposed method is viable as a technique for recovering from failure caused by complete occlusion or departure from the camera view.

}, keywords = {appearance-based tracking algorithm, camera view departure, failure recovery mechanism, image processing, iterative learning, iterative methods, learning systems, local search algorithm, mean-shift target localization algorithm, mean-shift tracking, occlusion, random-restart algorithm, robust visual tracking, STAGE, target recovery, target tracking}, doi = {http://dx.doi.org/10.1109/CRV.2009.23}, author = {F Tung and J S. Zelek and D A. Clausi} } @article {278, title = {Intra-retinal layer segmentation in optical coherence tomography images}, journal = {Optics Express}, volume = {17}, year = {2009}, pages = {23719{\textendash}23728}, abstract = {

Retinal layer thickness, evaluated as a function of spatial position from optical coherence tomography (OCT) images is an important diagnostics marker for many retinal diseases. However, due to factors such as speckle noise, low image contrast, irregularly shaped morphological features such as retinal detachments, macular holes, and drusen, accurate segmentation of individual retinal layers is difficult. To address this issue, a computer method for retinal layer segmentation from OCT images is presented. An efficient two-step kernel-based optimization scheme is employed to first identify the approximate locations of the individual layers, which are then refined to obtain accurate segmentation results for the individual layers. The performance of the algorithm was tested on a set of retinal images acquired in-vivo from healthy and diseased rodent models with a high speed, high resolution OCT system. Experimental results show that the proposed approach provides accurate segmentation for OCT images affected by speckle noise, even in sub-optimal conditions of low image contrast and presence of irregularly shaped structural features in the OCT images.

}, keywords = {algorithms and filters, image processing, image recognition, optical coherence tomography, Optical diagnostics for medicine}, doi = {http://dx.doi.org/10.1364/OE.17.023719}, url = {http://www.opticsexpress.org/abstract.cfm?URI=oe-17-26-23719}, author = {K Bizheva and A Mishra and A Wong and D A. Clausi} } @article {358, title = {Estimation of cellular fabric in embryonic epithelia}, journal = {Computer Methods in Biomechanics and Biomedical Engineering}, volume = {10}, year = {2007}, pages = {75 - 84}, abstract = {

Recent computational and analytical studies have shown that cellular fabric-as embodied by average cell size, aspect ratio and orientation-is a key indicator of the stresses acting in an embryonic epithelium. Cellular fabric in real embryonic tissues could not previously be measured automatically because the cell boundaries tend to be poorly defined, significant lighting and cell pigmentation differences occur and tissues contain a variety of cell geometries. To overcome these difficulties, four algorithms were developed: least squares ellipse fitting (LSEF), area moments (AM), correlation and axes search (CAS) and Gabor filters (GF). The AM method was found to be the most reliable of these methods, giving typical cell size, aspect ratio and orientation errors of 18\%, 0.10 and 7.4 degrees, respectively, when evaluated against manually segmented images. The power of the AM algorithm to provide new insights into the mechanics of morphogenesis is demonstrated through a brief investigation of gastrulation, where fabric data suggest that key gastrulation movements are driven by epidermal tensions circumferential to the blastopore.

}, keywords = {Cellular fabric, embryogenesis, image processing, Tissue mechanics}, author = {D A. Clausi and P Iles and G W Brodland and S Puddister} } @article {385, title = {Automated detection of crack defects in buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {58 - 72}, abstract = {

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

}, keywords = {Automated inspection, Crack detection, image processing, Pipeline assessment, Pipeline Infrastructure, Segmentation, Statistical properties}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.006}, author = {S K. Sinha and P Fieguth} } @article {388, title = {Neuro-fuzzy network for the classification of buried pipe defects}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {73 - 83}, abstract = {

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

}, keywords = {Automated inspection, Backpropagation network, Featuresm Neural Network, image processing, Neuro-fuzzy projection network, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.005}, author = {S K. Sinha and P Fieguth} } @article {391, title = {Segmentation of buried concrete pipe images}, journal = {Automation in Construction}, volume = {15}, year = {2006}, pages = {47 - 57}, abstract = {

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

}, keywords = {Automated inspection, image processing, Mathematical morphology, Pipeline assessment, Pipeline Infrastructure, Segmentation}, issn = {0926-5805}, doi = {http://dx.doi.org/10.1016/j.autcon.2005.02.007}, author = {S K. Sinha and P Fieguth} } @inproceedings {757, title = {Hierarchical Markov models for wavelet-domain statistics}, booktitle = {2003 IEEE Workshop on Statistical Signal Processing}, year = {2003}, abstract = {

There is a growing realization that modeling wavelet coefficients as statistically independent may be a poor assumption. Thus, this paper investigates two efficient models for wavelet coefficient coupling. Spatial statistics which are Markov (commonly used for textures and other random imagery) do not preserve their Markov properties in the wavelet domain; that is, the wavelet-domain covariance Pw does not have a sparse inverse. The main theme of this work is to investigate the approximation of Pw by hierarchical Markov and non-Markov models.

}, keywords = {correlation methods, hierarchical Markov models, image processing, Markov processes, spatial statistics, statistical analysis, wavelet coefficient coupling, wavelet transforms, wavelet-domain covariance, wavelet-domain statistics}, doi = {http://dx.doi.org/10.1109/SSP.2003.1289393}, author = {Z Azimifar and P Fieguth and E Jernigan} } @inproceedings {650, title = {Parametric contour estimation by simulated annealing}, booktitle = {IEEE International Conference on Image Processing}, year = {2003}, address = {Spain}, abstract = {

Virtually all implementations of simulated annealing are simplified by assuming discrete unknowns, however continuous-parameter annealing has many potential applications to image processing. Widely scattered problems such as formant tracking, boundary estimation and phase- unwrapping can all be approached as the annealed minimizations of continuous B-spline parameters. The benefits of simulated annealing are well known, including an insensitivity to initial conditions and the ability to solve problems with many local minima. Discrete variable annealing has seen broad application, however continuous-variable annealing is limited by the computational challenge of Gibbs sampling. In this paper we develop efficient approaches to sampling, illustrated in the context of contour tracking in noisy images.

}, keywords = {boundary estimation, continuous B-spline parameters, continuous-variable annealing, discrete variable annealing, formant tracking, Gibbs sampling, image processing, image sampling, noisy images, parametric contour estimation, phase-unwrapping, simulated annealing, splines (mathematics), tracking}, doi = {http://dx.doi.org/10.1109/ICIP.2003.1247278}, author = {M Jamieson and P Fieguth and L J. Lee} } @inproceedings {598, title = {Towards random field modeling of wavelet statistics}, booktitle = {International Conference on Image Processing}, year = {2002}, address = {Rochester, NY}, abstract = {

The paper investigates the statistical characterization of signals and images in the wavelet domain. In particular, in contrast to common decorrelated-coefficient models, we find that the correlation between wavelet scales can be surprisingly substantial, even across several scales. We investigate possible choices of statistical-interaction models. One efficient and fast strategy which describes the wavelet-based statistical correlations is illustrated. Finally, the effectiveness of the proposed tool towards an efficient hierarchical MRF (Markov random field) modeling of within-scale neighborhoods and across-scale dependencies is demonstrated.

}, keywords = {image characterization, image processing, Markov processes, Markov random field modeling, MRF, signal characterization, statistical analysis, statistical image processing, wavelet statistics, wavelet transforms}, doi = {http://dx.doi.org/10.1109/ICIP.2002.1038035}, author = {Z Azimifar and P Fieguth and E Jernigan} } @inproceedings {527, title = {An automated and rapid defect inspection algorithm for fluorescent PDP patterns}, booktitle = {14th Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

Plasma display panels (PDPs) represent the next generation of visual displays in the new century. Relative to traditional CRTs, PDPs offer advantages such as providing clearer images and occupying less space. The quality control of the PDP in the production line is very important to minimize costs and maximize product quality. Although there are several different kinds of patterns that need to be inspected, this research presents algorithms used for fluorescent pattern inspection of PDPs. Here, a complete design and implementation of a sequence of algorithmic components necessary to identify fluorescent pattern PDP defects is described. A primary criterion is that the maximum time allowed for the entire analysis is only a few seconds, so each component must execute rapidly and efficiently
Plasma display panels (PDPs) represent the next generation of visual displays in the new century. Relative to traditional CRTs, PDPs offer advantages such as providing clearer images and occupying less space. The quality control of the PDP in the production line is very important to minimize costs and maximize product quality. Although there are several different kinds of patterns that need to be inspected, this research presents algorithms used for fluorescent pattern inspection of PDPs. Here, a complete design and implementation of a sequence of algorithmic components necessary to identify fluorescent pattern PDP defects is described. A primary criterion is that the maximum time allowed for the entire analysis is only a few seconds, so each component must execute rapidly and efficiently
}, keywords = {automated rapid defect inspection algorithm, automatic optical inspection, costs, flat panel displays, fluorescence, fluorescent pattern inspection, fluorescent PDP patterns, image processing, plasma display panels, plasma displays, product quality, production line, quality control, visual displays}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933685}, author = {R Ge and D A. Clausi} } @inproceedings {534, title = {Morphological skeleton algorithm for PDP production line inspection}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {2001}, abstract = {

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

}, keywords = {automated applications, commercial machine vision system, Computer Vision, continuity checking, defects detection, electrode pattern inspection, Feature Extraction, feature point matching, image matching, image processing, image representation, image thinning, Inspection, Mathematical morphology, morphological skeleton algorithm, PDP inspection, PDP production line inspection, plasma display panel, plasma displays, production, redundant image data reduction, semiconductor device testing, semiconductor inspection, shape analysis, shape representation, single pixel lines, thick-lined images}, doi = {http://dx.doi.org/10.1109/CCECE.2001.933598}, author = {R Ge and D A. Clausi} } @article {524, title = {Multiply-rooted multiscale models for large-scale estimation}, journal = {IEEE Transactions on Image Processing}, volume = {10}, year = {2001}, pages = {1676 - 1686}, abstract = {

Divide-and-conquer or multiscale techniques have become popular for solving large statistical estimation problems. The methods rely on defining a state which conditionally decorrelates the large problem into multiple subproblems, each more straightforward than the original. However this step cannot be carried out for asymptotically large problems since the dimension of the state grows without bound, leading to problems of computational complexity and numerical stability. In this paper, we propose a new approach to hierarchical estimation in which the conditional decorrelation of arbitrarily large regions is avoided, and the problem is instead addressed piece-by-piece. The approach possesses promising attributes: it is not a local method-the estimate at every point is based on all measurements; it is numerically stable for problems of arbitrary size; and the approach retains the benefits of the multiscale framework on which it is based: a broad class of statistical models, a stochastic realization theory, an algorithm to calculate statistical likelihoods, and the ability to fuse local and nonlocal measurements

}, keywords = {asymptotically large problems, computational complexity, conditional decorrelation, estimation theory, geophysical signal processing, hierarchical estimation, image processing, large statistical estimation problems, large-scale estimation, local measurements fusion, multiply-rooted multiscale models, multiscale framework, nonlocal measurements fusion, numerical stability, oceanographic techniques, Remote Sensing, statistical analysis, statistical likelihoods, statistical models, stochastic realization theory}, issn = {1057-7149}, doi = {http://dx.doi.org/10.1109/83.967396}, author = {P Fieguth} } @inproceedings {566, title = {Rapid determination of co-occurrence texture features}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {2001}, abstract = {

Typically, the co-occurrence features for image processing are calculated-by using a grey level co-occurrence matrix (GLCM). This method is computationally intensive since the matrix is usually sparse leading to many unnecessary calculations involving zero probabilities. An improvement on the GLCM method is to utilize a grey level co-occurrence linked list (GLCLL) to store only the non-zero co-occurring probabilities. The GLCLL suffers since, to achieve preferred computational speeds, the list should be sorted. This paper presents a grey level co-occurrence hybrid structure (GLCHS) based on an integrated hash table and linked list approach. Texture features obtained using this technique are identical to those obtained using the GLCM and GLCLL. Based on a Brodatz test image, the GLCHS method is demonstrated to be a superior technique when compared across various window sizes and grey level quantizations. The GLCHS method required, on average, 33.4\% of the computational time ( sigma;=3.08\%) required by the GLCLL. Significant computational gains are made using the GLCHS method

}, keywords = {Brodatz test image, co-occurrence features, co-occurrence texture feature, geophysical measurement technique, geophysical signal processing, geophysical techniques, grey level co-occurrence hybrid structure, grey level co-occurrence linked list, grey level co-occurrence matrix, image processing, image texture, integrated hash table, land surface, linked list, Remote Sensing, terrain mapping}, doi = {http://dx.doi.org/10.1109/IGARSS.2001.977103}, author = {D A. Clausi and Y Zhao} } @inproceedings {511, title = {Foveated multiscale models for large-scale estimation}, booktitle = {International Conference on Image Processing}, year = {1999}, abstract = {

Efficient, large-scale estimation methods such as nested dissection or multiscale estimation rely on a divide-and-conquer strategy, in which a statistical problem is conditionally broken into smaller pieces. This conditional decorrelation is not possible for arbitrarily large problems due to issues of computational complexity and numerical stability. Given the growing interest in global-scale-remote sensing problems (or even three-dimensional problems), in this summary we develop a class of estimators with more promising asymptotic computational properties

}, keywords = {asymptotic computational properties, computational complexity, conditional decorrelation, decorrelation, divide and conquer methods, divide-and-conquer strategy, foveated multiscale models, global-scale-remote sensing problems, image processing, large-scale estimation methods, multiscale estimation, nested dissection, numerical stability, statistical problem, three-dimensional problems}, doi = {http://dx.doi.org/10.1109/ICIP.1999.823022}, author = {P Fieguth} } @inproceedings {512, title = {Large scale dynamic estimation of ocean surface temperature}, booktitle = {IEEE Geoscience and Remote Sensing Symposium}, year = {1999}, abstract = {

This paper addresses the dynamic estimation of the ocean surface temperature based on data from the Along-Track Scanning Radiometer (ATSR) for large (512 times;512) fields. For such huge problems, the conventional dynamic estimation tool (the Kalman filter) is not directly applicable, instead, we develop a recursive estimation algorithm that emulates the Kalman filter. Our approach uses a multiscale estimation algorithm for the update step, and makes simplifying assumptions about the surface dynamics leading to a computationally efficient prediction step

}, keywords = {Along-Track Scanning Radiometer data, ATSR data, geophysical signal processing, image processing, Kalman filter, Kalman filters, large scale dynamic estimation, multiscale estimation algorithm, ocean surface temperature, oceanographic techniques, prediction step, prediction theory, radiometry, recursive estimation, recursive estimation algorithm, recursive filters, update step}, doi = {http://dx.doi.org/10.1109/IGARSS.1999.772108}, author = {P Fieguth and F M. Khellah and M J. Murray and M R. Allen} } @inproceedings {514, title = {Recursive multiscale estimation of space-time random fields}, booktitle = {International Conference on Image Processing}, year = {1999}, abstract = {

We recently developed a multiscale recursive estimation procedure for the estimation of large-scale dynamic systems. The procedure propagates multiscale models for the estimation errors more efficiently than the Kalman filter\&$\#$39;s propagation of the error covariances, with a resulting computational complexity of Oscr;(N) and Oscr;(N3/2 ), where N is the number of variables estimated, for 1-D and 2-D dynamic systems, respectively. To further reduce the computational cost, we introduce in this paper a new class of reduced-order spatially-interpolated multiscale models, and demonstrate their use in remote

}, keywords = {computational complexity, estimation errors, image processing, large-scale dynamic systems, multiscale models, recursive estimation, recursive multiscale estimation, reduced-order spatially-interpolated multiscale models, space-time adaptive processing, space-time random fields}, doi = {http://dx.doi.org/10.1109/ICIP.1999.823021}, author = {T T. Ho and P Fieguth and A S. Wilsky} } @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} } @inproceedings {474, title = {Hierarchical methods for global-scale estimation problems}, booktitle = {IEEE Canadian Conference on Electrical and Computer Engineering}, year = {1998}, address = {Waterloo, Canada}, abstract = {

There is a substantial signal-processing challenge associated with large-scale (especially global-scale) remote-sensing problems: solving the statistical inverse problem (i.e. deducing the properties of the sensed field from measurements) by brute force, that is by covariance matrix inversion, is completely impractical for fields involving millions of pixels. This paper reports on the ongoing development of an alternative technique, in which the statistical problem is modeled on a multiscale tree, applied to estimating sea-surface temperature (SST) based on infrared radiance observations from the along-track scanning radiometers (ATSRs)

}, keywords = {along-track scanning radiometers, covariance matrix inversion, geophysical signal processing, global-scale estimation problems, hierarchical methods, image processing, infrared radiance observations, inverse problems, measurements, multiscale tree, oceanographic techniques, parameter estimation, radiometry, Remote Sensing, remote-sensing, sea-surface temperature, sensed field, signal-processing, statistical analysis, statistical inverse problem, statistical problem, tree data structures}, doi = {http://dx.doi.org/10.1109/CCECE.1998.682707}, author = {P Fieguth and M R. Allen and M J. Murray} }