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IEEE International Conference on image processing

作者： Marc Bosch Fengqing Zhu Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

In this paper we integrate several spatial texture tools into a texture-based video coding scheme. We implemented texture techniques and segmentation strategies in order to detect texture regions in video sequences. These textures are analyzed using temporal motion techniques and are labeled as skipped areas that are not encoded. After the decoding process, frame reconstruction is performed by inserting the skipped texture areas into the decoded frames. We are able to show an improvement over previous texture-based implementations in terms of compression efficiency.

关键词： Video compression Decoding image reconstruction image coding image segmentation image texture analysis Video coding Feature extraction Synthesizers Video sequences

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A comparision of Model-based methods for knee cartilage segmentation

A comparision of Model-based methods for knee cartilage segm...

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2nd International Conference on computer vision Theory and Applications, VISAPP 2007

作者： Cheong, J. Faggian, N. Langs, G. Suter, D. Cicuttini, F. Dept. of Electrical Dept. of Computer Systems Engineering Monash University Australia Clayton School of Information Technology Monash University Australia Institute for Computer Graphics and Vision Graz University of Technology Austria Pattern Recognition and Image Processing Group Vienna University of Technology Austria Dept. of Epidemiology Dept. of Preventive Medicine Monash University Australia

Osteoarthritis is a chronic and crippling disease affecting an increasing number of people each year. With no known cure, it is expected to reach epidemic proportions in the near future. Accurate segmentation of knee cartilage from magnetic resonance imaging (MRI) scans facilitates the measurement of cartilage volume present in a patient's knee, thus enabling medical clinicians to detect the onset of osteoarthritis and also crucially, to study its effects. This paper compares four model-based segmentation methods popular for medical data segmentation, namely Active Shape Models (ASM) (Cootes et al., 1995), Active Appearance Models (AAM) (Cootes et al., 2001), Patch-based Active Appearance Models (PAAM) (Faggian et al., 2006), and Active Feature Models (AFM) (Langs et al., 2006). A comprehensive analysis of how accurately these methods segment human tibial cartilage is presented. The results obtained were benchmarked against the current "gold standard" (cartilage segmented manually by trained clinicians) and indicate that modeling local texture features around each landmark provides the best results for segmenting human tibial cartilage.

关键词： Medical imaging

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Editorial: Knowledge engineering, semantics, and signal processing in audio - visual information retrieval

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IEEE Transactions on Circuits and Systems for Video Technology 2007年第3期17卷 257-258页

作者： Izquierdo, Ebroul Zhang, Jian Sikora, Thomas Huang, Thomas S. Department of Electrical Engineering Queen Mary University of London London El 4NS United Kingdom Sydney Australia School of Computer Science and Engineering University of New South Wales Australia Communication Systems Group Technical University Berlin Berlin Germany ITG University of Illinois at Urbana-Champaign United States Department of Electrical and Computer Engineering Coordinated Science Laboratory United States Image Formation and Processing Group Beckman Institute for Advanced Science and Technology United States Institute's Major Research Theme Human Computer Intelligent Interaction National Academy of Engineering Chinese Academies of Engineering and Sciences China International Association of Pattern Recognition Optical Society of American United States

No abstract available

关键词： Special issues and sections Knowledge engineering Information retrieval Audio-visual systems

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A Geometry-Based Local Descriptor for Range Data

A Geometry-Based Local Descriptor for Range Data

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Proceedings of the Digital image Computing: Technqiues and Applications (DICTA)

作者： Fredrik Viksten Klas Nordberg Image Coding Group Department of Electrical Engineering Linköping university Sweden Computer Vision Laboratory Department of Electrical Engineering Linköping university Sweden

ISBN: (纸本)9781424431618;9780769530673

We present a novel local descriptor for range data that can describe one or more planes or lines in a local region. It is possible to recover the geometry of the described local region and extract the size, position and orientation of each local plane or line-like structure from the descriptor. This gives the descriptor a property that other popular local descriptors for range data, such as spin images or point signatures, does not have. The estimation of the descriptor is dependant on estimation of surface normals but does not depend on the specific normal estimation method used. It is shown that is possible to extract how many planar surface regions the descriptor represents and that this could be used as a point-of-interest detector.

关键词： Tensile stress Layout computer vision Geometry Data mining Digital images computer applications Application software image coding Laboratories

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Does an endmember set really yield maximum simplex volume?

Does an endmember set really yield maximum simplex volume?

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Chao-Cheng Wu Chein-I Chang Remote Sensing Signal and Image Processing Laboratory the Department of Computer Science and Electrical Engineering Department University of Maryland Baltimore MD USA

One of commonly used criteria for finding an endmember set is to assume that for a given number of endmembers, p, a p-vertex simplex with its vertices specified by p endmembers always yields the maximum volume. Since there are also other criteria which have been widely used for endmember extraction, the issue of interest is "does an endmember set really produce a simplex with maximum volume?" In other words, using the criterion of a simplex with maximum volume is a better and more effective measure than other criteria currently being used by endmember extraction such as orthogonal projection-based pixel purity index (PPI), fully constrained least squares-based spectral unmixing, etc. This paper explores this issue by investigating a number of popular endmember extraction algorithms which are designed by different criteria. An extensive experiment-based study is also conducted for comparative analysis.

关键词： Algorithm design and analysis Least squares methods Hyperspectral imaging Indexes Volume measurement Chaos Data mining Subspace constraints Computational efficiency Current measurement

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On Cluster Validity Indexes in Fuzzy and Hard Clustering Algorithms for image Segmentation

On Cluster Validity Indexes in Fuzzy and Hard Clustering Alg...

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IEEE International Conference on image processing

作者： Moumen El-Melegy E.A. Zanaty Walaa M. Abd-Elhafiez Aly Farag Electrical Department Assiut University Asyut Egypt Computer Science Department Taif University Saudi Arabia Mathematics Department Sohag University Sohag Egypt Computer Vision and Image Processing Laboratory University of Louisville KY USA

This paper addresses the issue of assessing the quality of the clusters found by fuzzy and hard clustering algorithms. In particular, it seeks an answer to the question on how well cluster validity indexes can automatically determine the appropriate number of clusters that represent the data. The paper surveys several key existing solutions for cluster validity in the domain of image segmentation. In addition, it suggests two new indexes. The first one is based on Akaike's information criterion (AIC). While AIC was devoted to other domains such as statistical estimation of model fitting, it is implemented here for the first time as a validation index. The second index is developed from the well-established idea of cross-validation. The existing and new indexes are evaluated and compared on several synthetic images corrupted with noise of varying levels and volumetric MR data.

关键词： Clustering algorithms image segmentation Partitioning algorithms Biomedical imaging Clustering methods computer science Information systems computer vision image processing Fuzzy systems

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CHESAPEAKE BAY WATER QUALITY MONITORING USING SATELLITE imageRY

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International Journal of High Speed Electronics and Systems 2007年第4期17卷 681-688页

作者： BHARATH RAMAKRISHNA CHEIN-I CHANG BRUCE TROU JERRY HENQEMIHLE Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore County Baltimore MD 21250 USA Microtel LLC 111 Centerway Greenbelt MD 20770 USA

The Chesapeake Bay is a valued ecological, economic, recreational, cultural and scenic resource. The Bay watershed States and the District of Columbia, in conjunction with the EPA Chesapeake Bay Program, have worked and teamed together over the past 20 years to protect and restore the Bay ecosystem. A key component of this effort is water quality and habitat monitoring to assess the impact of management actions and natural processes, and evaluate habitat parameters on living resources such as submerged aquatic vegetation (SAV), oysters, and fisheries. Using aerial and satellite remote sensing imagery has become a practical and effective means of monitoring water quality in a timely manner. Of particular interest in evaluation of water clarity are several initiative measures. Specifically, Secchi-Disk Transparency (SDT) and Chlorophyll a (Chl- a ) have been widely accepted as critical indicators of water quality and their reliable estimation using satellite imagery provides a cost effective and speedy means for water quality monitoring. Work done at Water Resources Center, University of Minnesota has demonstrated the feasibility of performing regional assessment of lake water quality using LANDSAT image data. This paper investigates an approach similar to their work but uses a different type of satellite imagery, EO-1 ALI imagery where the SDT and Chl- a are also used as indicators to estimate water quality for the Chesapeake Bay and DC area (Potomac River). In doing so, three major issues are investigated, which are (1) the study site that is an open Bay area, not a self-contained lake; (2) investigation of applicability of equations that are used to specify the SDT and Chl- a to our Bay area study; (3) the use of a different type of satellite imagery for water quality monitoring. This paper develops techniques to address these three issues and presents preliminary experiments which show encouraging results.

关键词： Chesapeake Bay EO-1 ALI imagery Secchi Depth (SDT) Chl- a

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A Hierarchical Clustering Based on Mutual Information Maximization

A Hierarchical Clustering Based on Mutual Information Maximi...

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IEEE International Conference on image processing

作者： M. Aghagolzadeh H. Soltanian-Zadeh B. Araabi A. Aghagolzadeh Control and Intelligent Processing Center of Excellence Department of Electrical and Computer Engineering University of Tehran Tehran Iran Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran

Mutual information has been used in many clustering algorithms for measuring general dependencies between random data variables, but its difficulties in computing for small size datasets has limited its efficiency for clustering in many applications. A novel clustering method is proposed which estimates mutual information based on information potential computed pair-wise between data points and without any prior assumptions about cluster density function. The proposed algorithm increases the mutual information in each step in an agglomerative hierarchy scheme. We have shown experimentally that maximizing mutual information between data points and their class labels will lead to an efficient clustering. Experiments done on a variety of artificial and real datasets show the superiority of this algorithm, besides its low computational complexity, in comparison to other information based clustering methods and also some ordinary clustering algorithms.

关键词： Mutual information Clustering algorithms Clustering methods Data mining Data structures Density functional theory Computational complexity Entropy Higher order statistics Probability density function

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Face Recognition System Using Ant Colony Optimization-Based Selected Features

Face Recognition System Using Ant Colony Optimization-Based ...

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IEEE Symposium on Computational Intelligence in Security and Defense Applications, CISDA

作者： Hamidreza Rashidy Kanan Karim Faez Mehdi Hosseinzadeh Image Processing and Pattern Recognition Laboratory Electrical Engineering Department Tehran Polytechnic Amirkabir University of Technology슠 Tehran Iran Department of Computer Engineering University of Isfahan Isfahan Iran

Feature selection (FS) is a most important step which can affect the performance of pattern recognition system. This paper presents a novel feature selection method that is based on ant colony optimization (ACO). ACO algorithm is inspired of ant's social behavior in their search for the shortest paths to food sources. In the proposed algorithm, classifier performance and the length of selected feature vector are adopted as heuristic information for ACO. So, we can select the optimal feature subset without the priori knowledge of features. Simulation results on face recognition system and ORL database show the superiority of the proposed algorithm

关键词： Face recognition Ant colony optimization Discrete wavelet transforms Pattern recognition Artificial intelligence Particle swarm optimization Computational intelligence computer security Application software image processing

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A probabilistic framework for content-based diagnosis of retinal disease

A probabilistic framework for content-based diagnosis of ret...

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29th Annual International Conference of IEEE-EMBS, engineering in Medicine and Biology Society, EMBC'07

作者： Tobin, Kenneth W. Abdelrahman, Mohamed Chaum, Edward Govindasamy, V. Priya Karnowski, Thomas P. IEEE Image Science and Machine Vision Group Oak Ridge National Laboratory Oak Ridge TN 37831-6075 United States Electrical and Computer Engineering Dept. Tennessee Tech. University Cookeville TN 38505-0001 United States Department of Retinal Diseases Hamilton Eye Institute University of Tennessee Memphis TN 38163 United States

ISBN: (纸本)1424407885

Diabetic retinopathy is the leading cause of blindness in the working age population in the industrialized world. computer assisted analysis has the potential to assist in the early detection of diabetes by regular screening of large populations. The widespread availability of digital fundus cameras today is leading to the accumulation of large image archives of diagnosed patient data that captures historical knowledge of retinal pathology. Through this research we are developing a content-based image retrieval method to verify our hypothesis that retinal pathology can be identified and quantified from visually similar retinal images in an image archive. We will present diagnostic results for specificity and sensitivity on a population of 395 fundus images representing the normal fundus and 14 stratified disease states. © 2007 IEEE.

关键词： Diseases

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