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2013 26th Conference on Graphics, Patterns and images, SIBGRAPI 2013

作者： Wang, Quan Boyer, Kim L. Signal Analysis and Machine Perception Laboratory Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute Troy NY 12180 United States

ISBN: (纸本)9780769550992

We present the MDS feature learning framework, in which multidimensional scaling (MDS) is applied on high-level pair wise image distances to learn fixed-length vector representations of images. The aspects of the images that are captured by the learned features, which we call MDS features, completely depend on what kind of image distance measurement is employed. With properly selected semantics-sensitive image distances, the MDS features provide rich semantic information about the images that is not captured by other feature extraction techniques. In our work, we introduce the iterated Levenberg-Marquardt algorithm for solving MDS, and study the MDS feature learning with image Euclidean Distance (IMED) and Spatial Pyramid Matching (SPM) distance. We present experiments on both synthetic data and real images - the publicly accessible UIUC car image dataset. The MDS features based on SPM distance achieve exceptional performance for the car recognition task. © 2013 IEEE.

关键词： Object recognition

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Covariance shrinking in active shape models with application to gyral labeling of the cerebral cortex

Covariance shrinking in active shape models with application...

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IEEE International Symposium on Biomedical Imaging

作者： Zhen Yang Aaron Carass Jerry. L. Prince Image Analysis and Communications Laboratory Department of Electrical and Computer Engineering Johns Hopkins University USA

ISBN: (纸本)9781467364560

Active shape models (ASMs) have been widely used in segmentation tasks in medical image analysis. Complex structures and a limited number of training samples can, however, result in the failure to capture the complete range of shape variations. Various modifications to the point distribution model (PDM) have been proposed to increase the flexibility of the model. Still model parameters are often determined empirically without respect to the underlying data structure. We explore shrinkage covariance estimation in building a PDM by combining the sample covariance matrix with a target covariance matrix estimated from a low-dimensional constrained model. Instead of using a global shrinkage intensity, we apply a spatially varying shrinkage intensity field to better adapt to the spatially varying characteristic of a complex shape. The parameters of the constrained model and the amount of shrinkage are determined in a data-driven fashion, so that the resulting distribution is optimized in representing the underlying data. The PDM, which we call SC-PDM, shows an increased flexibility in fitting new shapes and at the same time, is robust to noise. We demonstrate the effectiveness of using SC-PDM to label gyral regions on the human cerebral cortex.

关键词： Shape Brain models Computational modeling Adaptation models Covariance matrices Data models

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IMART software for correction of motion artifacts in images collected in intravital microscopy

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Intravital 2014年第1期3卷 e28210页

作者： Kenneth W Dunn Kevin S Lorenz Paul Salama Edward J Delp Division of Nephrology School of Medicine Indiana University Indianapolis IN USA. Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA. Department of Electrical and Computer Engineering

Intravital microscopy is a uniquely powerful tool, providing the ability to characterize cell and organ physiology in the natural context of the intact, living animal. With the recent development of high-resolution microscopy techniques such as confocal and multiphoton microscopy, intravital microscopy can now characterize structures at subcellular resolution and capture events at sub-second temporal resolution. However, realizing the potential for high resolution requires remarkable stability in the tissue. Whereas the rigid structure of the skull facilitates high-resolution imaging of the brain, organs of the viscera are free to move with respiration and heartbeat, requiring additional apparatus for immobilization. In our experience, these methods are variably effective, so that many studies are compromised by residual motion artifacts. Here we demonstrate the use of IMART, a software tool for removing motion artifacts from intravital microscopy images collected in time series or in three dimensions.

关键词： fluorescence microscopy image analysis image processing image registration in vivo imaging intravital microscopy motion artifact multiphoton microscopy

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Relationship exploration among PPI, ATGP and VCA via theoretical analysis

Relationship exploration among PPI, ATGP and VCA via theoret...

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作者： Chang, Chein-I. Wen, Chia-Hsien Wu, Chao-Cheng Department of Computer Science and Electrical Engineering Remote Sensing Signal and Image Processing Laboratory University of Maryland 1000 Hilltop Circle Baltimore MD 21250 United States Department of Computer Science and Information Engineering Providence University Taichung Taiwan Department of Computer Science and Information Management Providence University 200 Taiwan Boulevard Shalu Dist. Taichung City 43301 Taiwan Department of Electrical Engineering National Taipei University of Technology 1 Zhongxiao E. Rd. Taipei 106 Taiwan

The principle of orthogonality using orthogonal projection (OP) is the key concept to develop mean squared error estimation theory in signal processing and communications. It has also found its way in a wide variety of applications in hyperspectral imaging. This paper explores close relationships among three OP-based algorithms, pixel purity index (PPI), vertex component analysis (VCA) in endmember extraction, automatic target generation process (ATGP) in mixed pixel classification and target detection, etc. and further shows that they indeed can be interpreted one way or another. Specifically, VCA and ATGP are essentially the same algorithm in the sense that they both extract targets via maximal OP from a nested sequence of successive orthogonal subspace projection spaces. Copyright © 2013 Inderscience Enterprises Ltd. Copyright © 2013 Inderscience Enterprises Ltd.

关键词： Hyperspectral imaging

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Graph-Based Optimization with Tubularity Markov Tree for 3D Vessel Segmentation

Graph-Based Optimization with Tubularity Markov Tree for 3D ...

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IEEE Conference on computer vision and Pattern Recognition

作者： Ning Zhu Albert C. S. CHUNG Lo Kwee-Seong Medical Image Analysis Laboratory Department of Computer Science and Engineering The Hong Kong University of Science and Technology

ISBN: (纸本)9781467364102

In this paper, we propose a graph-based method for 3D vessel tree structure segmentation based on a new tubularity Markov tree model (TMT), which works as both new energy function and graph construction method. With the help of power-watershed implementation [7], a global optimal segmentation can be obtained with low computational cost. Different with other graph-based vessel segmentation methods, the proposed method does not depend on any skeleton and ROI extraction method. The classical issues of the graph-based methods, such as shrinking bias and sensitivity to seed point location, can be solved with the proposed method thanks to vessel data fidelity obtained with TMT. The proposed method is compared with some classical graph-based image segmentation methods and two up-to-date 3D vessel segmentation methods, and is demonstrated to be more accurate than these methods for 3D vessel tree segmentation. Although the segmentation is done without ROI extraction, the computational cost for the proposed method is low (within 20 seconds for 256*256*144 image).

关键词： segmentation Segmentation 3-glucosaminoglycan sulfate Blood Vessels pressure vessels Construction methods image segmentation point location Learning-based method empirical approach alternative energy Skeletons bone structure Markov TPSG1 gene

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Pedestrian detection based on LIDAR-driven sliding window and relational parts-based detection

Pedestrian detection based on LIDAR-driven sliding window an...

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IEEE Symposium on Intelligent Vehicle

作者： Luciano Oliveira Urbano Nunes Intelligent Vision Research Laboratory Federal University Bahia Brazil Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Portugal

The most standard image object detectors are usually comprised of one or multiple feature extractors or classifiers within a sliding window framework. Nevertheless, this type of approach has demonstrated a very limited performance under datasets of cluttered scenes and real life situations. To tackle these issues, LIDAR space is exploited here in order to detect 2D objects in 3D space, avoiding all the inherent problems of regular sliding window techniques. Additionally, we propose a relational parts-based pedestrian detection in a probabilistic non-iid framework. With the proposed framework, we have achieved state-of-the-art performance in a pedestrian dataset gathered in a challenging urban scenario. The proposed system demonstrated superior performance in comparison with pure sliding-window-based image detectors.

关键词： Detectors Laser radar Feature extraction Support vector machines Training Probabilistic logic Vectors

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A generalized directional Laplacian distribution: Estimation, mixture models and audio source separation

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IEEE Transactions on Audio, Speech and Language Processing 2012年第9期20卷 2397-2408页

作者： Mitianoudis, Nikolaos Image Processing and Multimedia Laboratory Department of Electrical and Computer Engineering Democritus University of Thrace 67100 Xanthi Greece

Directional or Circular statistics are pertaining to the analysis and interpretation of directions or rotations. In this work, a novel probability distribution is proposed to model multidimensional sparse directional data. The Generalized Directional Laplacian Distribution (DLD) is a hybrid between the Laplacian distribution and the von Mises-Fisher distribution. The distribution's parameters are estimated using Maximum-Likelihood Estimation over a set of training data points. Mixtures of Directional Laplacian Distributions (MDLD) are also introduced in order to model multiple concentrations of sparse directional data. The author explores the application of the derived DLD mixture model to cluster sound sources that exist in an underdetermined instantaneous sound mixture. The proposed model can solve the general K × L (K

关键词： Source separation

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A laboratory-BASED X-RAY PHASE CONTRAST IMAGING SCANNER WITH APPLICATIONS IN BIOMEDICAL AND NON-MEDICAL DISCIPLINES

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International Journal of Modern Physics: Conference Series 2014年第27期ijmpcs卷

作者： C. K. HAGEN P. C. DIEMOZ M. ENDRIZZI P. R. T. MUNRO M. B. SZAFRANIEC T. P. MILLARD R. SPELLER D A. OLIVO Department of Medical Physics and Bioengineering University College London London WC1E 6BT UK Optical + Biomedical Engineering Laboratory School of Electrical Electronic and Computer Engineering The University of Western Australia Crawley Western Australia 6009 Australia Centre for Microscopy Characterisation and Analysis The University of Western Australia Crawley Western Australia 6009 Australia

X-ray phase contrast imaging (XPCi) provides a much higher visibility of low-absorbing details than conventional, attenuation-based radiography. This is due to the fact that image contrast is determined by the unit decrement of the real part of the complex refractive index of an object rather than by its imaginary part (the absorption coefficient), which can be up to 1000 times larger for energies in the X-ray regime. This finds applications in many areas, including medicine, biology, material testing, and homeland security. Until lately, XPCi has been restricted to synchrotron facilities due to its demanding coherence requirements on the radiation source. However, edge illumination XPCi, first developed by one of the authors at the ELETTRA Synchrotron in Italy, substantially relaxes these requirements and therefore provides options to overcome this problem. Our group has built a prototype scanner that adapts the edge-illumination concept to standard laboratory conditions and extends it to large fields of view. This is based on X-ray sources and detectors available off the shelf, and its use has led to impressive results in mammography, cartilage imaging, testing of composite materials and security inspection. This article presents the method and the scanner prototype, and reviews its applications in selected biomedical and non-medical disciplines.

关键词： X-ray imaging phase contrast phase retrieval

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COMPRESSIVE SENSING-BASED image DENOISING USING ADAPTIVE MULTIPLE SAMPLING AND OPTIMAL ERROR TOLERANCE

COMPRESSIVE SENSING-BASED IMAGE DENOISING USING ADAPTIVE MUL...

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IEEE International Conference on Acoustics, Speech, and Signal Processing

作者： Wonseok Kang Eunsung Lee Eunjung Chea Aggelos K. Katsaggelos Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Seoul Chung-Ang University Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL 60208 USA

ISBN: (纸本)9781479903573

In this paper, we present a compressive sensing-based image denoising algorithm using spatially adaptive image representation and estimation of optimal error tolerance based on sparse signal analysis. The proposed method performs block-based multiple compressive sampling after decomposing the sparse signal into feature and non-feature regions using simple statistical analysis. For minimization of recovery error and number of iterations, the modified OMP method estimates the optimal error tolerance using the average variance in the recovery step. Experimental results demonstrate that the proposed denoising algorithm better removes noise without undesired artifacts than existing state-of-the-art methods in terms of both objective (PSNR/SSIM) and subjective measures. Processing time of the proposed method is 5 to 10 times faster than the standard OMP-based method.

关键词： Tolerances physiologic tolerance Sampling system recovery image denoising image representation images Signal analysis Statistical analysis Statistical methods image denoising algorithm

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Importance Sampling Spherical Harmonics to Improve Probabilistic Tractography

Importance Sampling Spherical Harmonics to Improve Probabili...

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International Workshop on Pattern Recognition in NeuroImaging (PRNI)

作者： H. Ertan Çetingül Laura Dumont Mariappan S. Nadar Paul M. Thompson Guillermo Sapiro Christophe Lenglet Imaging and Computer Vision TF Siemens Corporation Princeton NJ USA Engineering School of Chemistry Physics and Electronics CPE Lyon Villeurbanne France Laboratory of Neuro Imaging School of Medicine University of California Los Angeles Los Angeles CA USA Department of Electrical and Computer Engineering Duke University Durham NC USA Center for Magnetic Resonance Research University of Minnesota Minneapolis MN USA

We consider the problem of improving the accuracy and reliability of probabilistic white matter tractography methods by improving the built-in sampling scheme, which randomly draws, from a diffusion model such as the orientation distribution function (ODF), a direction of propagation. Existing methods employing inverse transform sampling require an ad hoc thresholding step to prevent the less likely directions from being sampled. We herein propose to perform importance sampling of spherical harmonics, which redistributes an input point set on the sphere to match the ODF using hierarchical sample warping. This produces a point set that is more concentrated around the modes, allowing the subsequent inverse transform sampling to generate orientations that are in better accordance with the local fiber configuration. Integrated into a Kalman filter-based framework, our approach is evaluated through experiments on synthetic, phantom, and real datasets.

关键词： Probabilistic logic Harmonic analysis Monte Carlo methods image reconstruction Laplace equations Phantoms Tensile stress

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