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Conference on Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI

作者： Manandhar, A. Morton, K. D. Collins, L. M. Torrione, P. A. Duke Univ Dept Elect & Comp Engn Durham NC 27705 USA

ISBN: (纸本)9780819485915

Ground Penetrating Radar (GPR) data provides a powerful technique to identify subsurface buried threats. Although GPR data contains a three-dimensional representation of the subsurface, object truth (i.e. labels and positions of true threat objects in training lanes) is often provided in only two dimensions (GPS coordinates along the earth's surface). To mitigate uncertainty in an object's location in depth, many successful feature extraction/object recognition techniques in GPR extract feature vectors from several depth regions, and attempt to combine information across these feature vectors to make final decisions. However, many machine learning techniques are not well suited for learning under these conditions. Multiple Instance Learning (MIL) is a type of supervised learning method in which labels are available for sets of samples, but not for individual samples. The goal of learning in MIL is to classify new sets of samples as they become available. This set-based framework is useful in processing GPR responses since features are often extracted independently from multiple un-labeled depth bins, and thus a set of features is produced at each potential threat location. In this work, a comparison of several previous approaches to MlL applied to landmine detection in GPR data is presented. One recent algorithm, the p-Posterior Mixture Model approach (pPMM) is given special attention, and several slight modifications to the pPMM approach are presented and compared.

关键词： Land mines General packet radio service Feature extraction expectation maximization algorithms Molecules Ground penetrating radar Machine learning Sensors Data acquisition Target detection

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Fast 4D Segmentation of Large Datasets using Graph Cuts

Fast 4D Segmentation of Large Datasets using Graph Cuts

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Conference on Medical Imaging 2011 - Image Processing

作者： Lombaert, Herve Sun, Yiyong Cheriet, Farida Ecole Polytech Montreal PQ H3C 3A7 Canada Siemens Corp Res Princeton NJ 08540 USA

ISBN: (纸本)9780819485045

In this paper, we propose to use 4D graph cuts for the segmentation of large spatio-temporal (4D) datasets. Indeed, as 4D datasets grow in popularity in many clinical areas, so will the demand for efficient general segmentation algorithms. The graph cuts method(1) has become a leading method for complex 2D and 3D image segmentation in many applications. Despite a few attempts(2-5) in 4D, the use of graph cuts on typical medical volume quickly exceeds today's computer capacities. Among all existing graph cuts based methods(6-10) the multilevel banded graph cuts(9) is the fastest and uses the least amount of memory. Nevertheless, this method has its limitation. Memory becomes an issue when using large 4D volume sequences, and small structures become hardly recoverable when using narrow bands. We thus improve the boundary refinement efficiency by using a 4D competitive region growing. First, we construct a coarse graph at a low resolution with strong temporal links to prevent the shrink bias inherent to the graph cuts method. Second, we use a competitive region growing using a priority queue to capture all fine details. Leaks are prevented by constraining the competitive region growing within a banded region and by adding a viscosity term. This strategy yields results comparable to the multilevel banded graph cuts but is faster and allows its application to large 4D datasets. We applied our method on both cardiac 4D MRI and 4D CT datasets with promising results.

关键词： Image segmentation Image processing algorithms and systems 3D image processing 4D CT imaging 3D modeling expectation maximization algorithms Image processing Magnetic resonance imaging Medical imaging Temporal resolution

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A Comparative Evaluation of Anomaly Detection algorithms for Maritime Video Surveillance

A Comparative Evaluation of Anomaly Detection Algorithms for...

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Conference on Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense X

作者： Auslander, Bryan Gupta, Kalyan Moy Aha, David W. Knexus Res Corp Springfield VA 22153 USA Navy Ctr Appl Res Artificial Intelligence Naval Res Lab Washington DC 20375 USA

ISBN: (纸本)9780819485939

A variety of anomaly detection algorithms have been applied to surveillance tasks for detecting threats with some success. However, it is not clear which anomaly detection algorithms should be used for domains such as ground-based maritime video surveillance. For example, recently introduced algorithms that use local density techniques have performed well for some tasks, but they have not been applied to ground-based maritime video surveillance. Also, the reasons for the performance differences of anomaly detection algorithms on problems of varying difficulty are not well understood. We address these two issues by comparing families of global and local anomaly detection algorithms on tracks extracted from ground-based maritime surveillance videos. Obtaining maritime anomaly data can be difficult or even impractical. Therefore, we use a generative approach to vary and control the difficulty of anomaly detection tasks and to focus on borderline and difficult situations in our empirical comparison studies. We report that global algorithms outperform local algorithms when tracks have large and unstructured variations, while they perform equally well when the tracks have only minor variations.

关键词： Detection and tracking algorithms expectation maximization algorithms Video surveillance Video Surveillance Computer simulations Evolutionary algorithms Analytical research Maritime surveillance Sensors

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Constrain static target kinetic iterative image reconstruction for 4D cardiac CT imaging

Constrain static target kinetic iterative image reconstructi...

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Conference on Computational Imaging IX

作者： Alessio, Adam M. La Riviere, Patrick J. Univ Washington Seattle WA 98195 USA Univ Chicago Chicago IL 60637 USA

ISBN: (纸本)9780819484109

Iterative image reconstruction offers improved signal to noise properties for CT imaging. A primary challenge with iterative methods is the substantial computation time. This computation time is even more prohibitive in 4D imaging applications, such as cardiac gated or dynamic acquisition sequences. In this work, we propose only updating the time-varying elements of a 4D image sequence while constraining the static elements to be fixed or slowly varying in time. We test the method with simulations of 4D acquisitions based on measured cardiac patient data from a) a retrospective cardiac-gated CT acquisition and b) a dynamic perfusion CT acquisition. We target the kinetic elements with one of two methods: 1) position a circular ROI on the heart, assuming area outside ROI is essentially static throughout imaging time;and 2) select varying elements from the coefficient of variation image formed from fast analytic reconstruction of all time frames. Targeted kinetic elements are updated with each iteration, while static elements remain fixed at initial image values formed from the reconstruction of data from all time frames. Results confirm that the computation time is proportional to the number of targeted elements;our simulations suggest that <30% of elements need to be updated in each frame leading to >3 times reductions in reconstruction time. The images reconstructed with the proposed method have matched mean square error with full 4D reconstruction. The proposed method is amenable to most optimization algorithms and offers the potential for significant computation improvements, which could be traded off for more sophisticated system models or penalty terms.

关键词： Computed tomography Image restoration Reconstruction algorithms Signal to noise ratio Data modeling Heart Composites CT reconstruction Data acquisition expectation maximization algorithms

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Conference on Imaging and Printing in a Web 2.0 World II

作者： Ahmadullin, Ildus Fan, Jian Damera-Venkata, Niranjan Lim, Suk Hwan Lin, Qian Liu, Jerry Liu, Sam O'Brien-Strain, Eamonn Allebach, Jan Purdue Univ W Lafayette IN 47907 USA Hewlett Packard Labs Palo Alto CA 94304 USA

ISBN: (纸本)9780819484161

Managing large document databases is an important task today. Being able to automatically compare document layouts and classify and search documents with respect to their visual appearance proves to be desirable in many applications. We measure single page documents' similarity with respect to distance functions between three document components: background, text, and saliency. Each document component is represented as a Gaussian mixture distribution;and distances between different documents' components are calculated as probabilistic similarities between corresponding distributions. The similarity measure between documents is represented as a weighted sum of the components' distances. Using this document similarity measure, we propose a browsing mechanism operating on a document dataset. For these purposes, we use a hierarchical browsing environment which we call the document similarity pyramid. It allows the user to browse a large document dataset and to search for documents in the dataset that are similar to the query. The user can browse the dataset on different levels of the pyramid, and zoom into the documents that are of interest.

关键词： Visualization Databases expectation maximization algorithms Image segmentation Zoom lenses Darmstadtium Dubnium Feature extraction Detection and tracking algorithms RGB color model

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Topologically Correct Cortical Segmentation using Khalimsky's Cubic Complex framework

Topologically Correct Cortical Segmentation using Khalimsky'...

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Conference on Medical Imaging 2011 - Image Processing

作者： Cardoso, Manuel Jorge Clarkson, Matthew J. Modat, Marc Talbot, Hugues Couprie, Michel Ourselin, Sebastien UCL Ctr Med Image Comp London WC1E 6BT England Univ Paris Est CNRS UMR 8049 A2SI ESIIEE Champs Sur Marne France

ISBN: (纸本)9780819485045

Automatic segmentation of the cerebral cortex from magnetic resonance brain images is a valuable tool for neuroscience research. Due to the presence of noise, intensity non-uniformity, partial volume effects, the limited resolution of MRI and the highly convoluted shape of the cerebral cortex, segmenting the brain in a robust, accurate and topologically correct way still poses a challenge. In this paper we describe a topologically correct expectation Maximisation based Maximum a Posteriori segmentation algorithm formulated within the Khalimsky cubic complex framework, where both the solution of the EM algorithm and the information derived from a geodesic distance function are used to locally modify the weighting of a Markov Random Field and drive the topology correction operations. Experiments performed on 20 Brainweb datasets show that the proposed method obtains a topologically correct segmentation without significant loss in accuracy when compared to two well established techniques.

关键词： expectation maximization algorithms Image segmentation Brain Algorithm development Independent component analysis Tissues Magnetorheological finishing Cerebral cortex Magnetic resonance imaging Surgery

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A Monte Carlo Implementation of the SAGE Algorithm for Joint Soft-Multiuser Decoding, Channel Parameter Estimation, and Code Acquisition

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2010年第11期58卷 5756-5766页

作者： Kocian, Alexander Panayirci, Erdal Poor, H. Vincent Ruggieri, Marina Univ Roma Tor Vergata Dept Elect Engn Ctr TeleInFrastruct CTiF Italy Rome Italy Princeton Univ Dept Elect Engn Princeton NJ 08544 USA

This paper presents an iterative scheme for joint timing acquisition, multi-channel parameter estimation, and multiuser soft-data decoding. As an example, an asynchronous convolutionally coded direct-sequence code-division multiple-access system is considered. The proposed receiver is derived within the space-alternating generalized expectation-maximization framework, implying that convergence in likelihood is guaranteed under appropriate conditions in contrast to many other iterative receiver architectures. The proposed receiver iterates between joint posterior data estimation, interference cancellation, and single-user channel estimation and timing acquisition. A Markov Chain Monte Carlo technique, namely Gibbs sampling, is employed to compute the a posteriori probabilities of data symbols in a computationally efficient way. Computer simulations in flat Rayleigh fading show that the proposed algorithm is able to handle high system loads unlike many other iterative receivers.

关键词： expectation maximization algorithms multiaccess communication Monte Carlo mthods

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A Weighted Mean Shift, Normalized Cuts Initialized Color Gradient Based Geodesic Active Contour Model: Applications to Histopathology Image Segmentation

A Weighted Mean Shift, Normalized Cuts Initialized Color Gra...

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Conference on Medical Imaging 2010 - Image Processing

作者： Xu, Jun Janowczyk, Andrew Chandran, Sharat Madabhushi, Anant Rutgers State Univ Dept Biomed Engn Piscataway NJ 08855 USA Indian Inst Technol IIT Dept Comp Sci & Engn Bombay Maharashtra India

ISBN: (纸本)9780819480248

While geodesic active contours (GAC) have become very popular tools for image segmentation, they are sensitive to model initialization. In order to get an accurate segmentation, the model typically needs to be initialized very close to the true object boundary. Apart from accuracy, automated initialization of the objects of interest is an important pre-requisite to being able to run the active contour model on very large images (such as those found in digitized histopathology). A second limitation of GAC model is that the edge detector function is based on gray scale gradients;color images typically being converted to gray scale prior to computing the gradient. For color images, however, the gray scale gradient results in broken edges and weak boundaries, since the other channels are not exploited for the gradient determination. In this paper we present a new geodesic active contour model that is driven by an accurate and rapid object initialization scheme-weighted mean shift normalized cuts (WNCut). WNCut draws its strength from the integration of two powerful segmentation strategies-mean shift clustering and normalized cuts. WNCut involves first defining a color swatch (typically a few pixels) from the object of interest. A multi-scale mean shift coupled normalized cuts algorithm then rapidly yields an initial accurate detection of all objects in the scene corresponding to the colors in the swatch. This detection result provides the initial boundary for GAC model. The edge-detector function of the GAC model employs a local structure tensor based color gradient, obtained by calculating the local min/max variations contributed from each color channel (e. g. R, G, B or H, S, V). Our color gradient based edge-detector function results in more prominent boundaries compared to classical gray scale gradient based function. We evaluate segmentation results of our new WNCut initialized color gradient based GAC (WNCut-CGAC) model against a popular region-based model (Chan &

关键词： Image segmentation Data modeling Prostate Tumor growth modeling Biopsy RGB color model Breast Cancer expectation maximization algorithms Sensors

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Differentiation of uric acid versus non-uric acid kidney stones in the presence of iodine using dual-energy CT

Differentiation of uric acid versus non-uric acid kidney sto...

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Conference on Medical Imaging - Physics of Medical Imaging

作者： Wang, J. Qu, M. Leng, S. McCollough, C. H. Mayo Clin Dept Radiol Rochester MN 55905 USA

ISBN: (纸本)9780819480231

In this study, the feasibility of differentiating uric acid from non-uric acid kidney stones in the presence of iodinated contrast material was evaluated using dual-energy CT (DECT). Iodine subtraction was accomplished with a commercial three material decomposition algorithm to create a virtual non-contrast (VNC) image set. VNC images were then used to segment stone regions from tissue background. The DE ratio of each stone was calculated using the CT images acquired at two different energies with DECT using the stone map generated from the VNC images. The performance of DE ratio-based stone differentiation was evaluated at five different iodine concentrations (21, 42, 63, 84 and 105 mg/ml). The DE ratio of stones in iodine solution was found larger than those obtained in non-iodine cases. This is mainly caused by the partial volume effect around the boundary between the stone and iodine solution. The overestimation of the DE ratio leads to substantial overlap between different stone types. To address the partial volume effect, an expectation-maximization (EM) approach was implemented to estimate the contribution of iodine and stone within each image pixel in their mixture area. The DE ratio of each stone was corrected to maximally remove the influence of iodine solutions. The separation of uric-acid and non-uric-acid stone was improved in the presence of iodine solution.

关键词： Iodine Renal calculi Image segmentation expectation maximization algorithms Computed tomography Tissues Photovoltaics Dual energy imaging Medicine Tin

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Hyperspectral Unmixing: Geometrical, Statistical, and Sparse Regression-Based Approaches

Hyperspectral Unmixing: Geometrical, Statistical, and Sparse...

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Conference on Image and Signal Processing for Remote Sensing XVI

作者： Bioucas-Dias, Jose M. Plaza, Antonio Univ Lisbon Inst Telecomunicacoes Inst Super Tecn P-1699 Lisbon Portugal

ISBN: (纸本)9780819483478

Hyperspectral instruments acquire electromagnetic energy scattered within their ground instantaneous field view in hundreds of spectral channels with high spectral resolution. Very often, however, owing to low spatial resolution of the scanner or to the presence of intimate mixtures (mixing of the materials at a very small scale) in the scene, the spectral vectors (collection of signals acquired at different spectral bands from a given pixel) acquired by the hyperspectral scanners are actually mixtures of the spectral signatures of the materials present in the scene. Given a set of mixed spectral vectors, spectral mixture analysis (or spectral unmixing) aims at estimating the number of reference materials, also called endmembers, their spectral signatures, and their fractional abundances. Spectral unmixing is, thus, a source separation problem where, under a linear mixing model, the sources are the fractional abundances and the endmember spectral signatures are the columns of the mixing matrix. As such, the independent component analysis (ICA) framework came naturally to mind to unmix spectral data. However, the ICA crux assumption of source statistical independence is not satisfied in spectral applications, since the sources are fractions and, thus, non-negative and sum to one. As a consequence, ICA-based algorithms have severe limitations in the area of spectral unmixing, and this has fostered new unmixing research directions taking into account geometric and statistical characteristics of hyperspectral sources. This paper presents an overview of the principal research directions in hyperspectral unmixing. The presentations is organized into four main topics: i) mixing models, ii) signal subspace identification, iii) geometrical-based spectral unmixing, (iv) statistical-based spectral unmixing, and (v) sparse regression-based unmixing. In each topic, we describe what physical or mathematical problems are involved and summarize state-of-the-art algorithms to addres

关键词： Independent component analysis Algorithm development Sensors Signal to noise ratio Light scattering Statistical modeling expectation maximization algorithms Reflectivity Data modeling Mathematical modeling

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