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Canadian Conference on Computer and Robot vision, CRV

作者： Wen Zhang Alexander Wong David A. Clausi Vision and Image Processing Group Systems Design Engineering University of Waterloo Waterloo ONT Canada

Synthetic aperture radar (SAR) images are degraded by a form of multiplicative noise known as speckle. Current methods for despeckling are limited in that they either do not perform enough noise attenuation, or do not adequately preserve or enhance image detail. We propose a novel adaptive stochastic method for joint enhancement and despecking of images (JEDI) for SAR. The proposed method utilizes an adaptive importance sampling scheme based on local statistics to generate random samples while reducing estimation variance. A Monte Carlo estimate is computed based on the generated samples, wherein the samples are aggregated to form a despeckled and detail-enhanced result. The advantage of JEDI is the ability to efficiently take advantage of information redundancy in speckled images to reduce the effects of speckle while simultaneously enhancing detail visualization. Testing with both simulated and real speckled images shows that JEDI typically outperforms popular despeckling algorithms such as Frost filtering, anisotropic diffusion, median filtering, Gamma-MAP and GenLik in terms of quantitative and qualitative visual quality. On average, JEDI provides a 2-15% improvement in PSNR and a 5-14% improvement in image quality index measures over the tested methods.

关键词： Stochastic processes Stochastic resonance Speckle Monte Carlo methods Testing Filtering Synthetic aperture radar Degradation Attenuation Statistics

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Towards a Robust Spatio-Temporal Interest Point Detection for Human Action Recognition

Towards a Robust Spatio-Temporal Interest Point Detection fo...

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Canadian Conference on Computer and Robot vision, CRV

作者： Hossein Shabani David A. Clausi John S. Zelek Vision and Image Processing Laboratory Department of Systems Design Engineering University of Waterloo Waterloo ONT Canada

Spatio-temporal salient features are widely being used for compact representation of objects and motions in video, especially for event and action recognition. The existing feature extraction methods have two main problems: First, they work in batch mode and mostly use Gaussian (linear) scale-space filtering for multi-scale feature extraction. This linear filtering causes the blurring of the edges and salient motions which should be preserved for robust feature extraction. Second, the environmental motion and ego disturbances (e.g., camera shake) are not usually differentiated. These problems result in the detection of false features no matter which saliency criteria is used. To address these problems, we developed a non-linear (scale-space) filtering approach which prevents both spatial and temporal dislocations. This model can provide a non-linear counterpart of the Laplacian of Gaussian to form the conceptual structure maps from which multi-scale spatio-temporal salient features are extracted. Preliminary evaluation shows promising result with false detection being removed.

关键词： Robustness Humans Filtering Feature extraction Nonlinear filters Computer vision Cameras image recognition Laplace equations Spatiotemporal phenomena

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To possess or not to possess [President's Corner]

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IEEE Solid-State Circuits Magazine 2009年第4期1卷 4-4页

作者： Willy Sansen Vision and Image Processing Research Group Department of Systems Design Engineering University of Waterloo Waterloo ON Canada

I love the IEEE Journal of Solid-State Circuits (JSSC), not only because its cover is flashy red but especially because it contains all the information that I need to survive and beat the world. I have all my copies saved on a shelf, which has become quite impressive. It looks as though I have mastered all the information contained in these volumes, as if I know it all.

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Robust snake convergence based on dynamic programming

Robust snake convergence based on dynamic programming

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IEEE International Conference on image processing

作者： A.K. Mishra P. Fieguth D.A. Clausi Vision and Image Processing Research Group Systems Design Engineering University of Waterloo Waterloo ONT Canada

The extraction of contours using deformable models, such as snakes, is a problem of great interest in computer vision, particular in areas of medical imaging and tracking. Snakes have been widely studied and many methods are available. In most cases, the snake converges towards the optimal contour by minimizing a sum of internal (prior) and external (image measurement) energy terms. This approach is elegant, but frequently mis-converges in the presence of noise or complex contours. To address these limitations, a novel discrete snake is proposed which treats the two energy terms separately. Essentially, the proposed method is a deterministic iterative statistical data fusion approach, in which the visual boundaries of the object are extracted, ignoring any prior, employing a Hidden Markov Model (HMM) and Viterbi search, and then applying importance sampling to the boundary points, on which the shape prior is asserted. The proposed implementation is straightforward and achieves dramatic speed and accuracy improvement compared to other methods.

关键词： Robustness Convergence Dynamic programming Hidden Markov models Deformable models Computer vision Biomedical imaging image converters Energy measurement Iterative methods

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A generic shape matching with anchoring of knowledge primitives of object ontology

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2nd International Conference on image Analysis and Recognition, ICIAR 2005

作者： Han, Dongil You, Bum-Jae Kim, Yong Se Suh, Il Hong Vision and Image Processing Lab. Sejong University Seoul Korea Republic of Intelligent Robotics Research Center Korea Institute of Science and Technology Seoul Korea Republic of Creative Design and Intelligent Tutoring Systems Research Center Sungkyunkwan University Suwon Korea Republic of Intelligence and Interaction Lab. Hanyang University Seoul Korea Republic of

ISBN: (纸本)3540290699

We have developed a generic ontology of objects, and a knowledge base of everyday physical objects. Objects are represented as assemblies of functional features and their spatial relations. Generic shape information of objects and features is stored using a partial boundary representation. Form-function reasoning is applied to deduce geometric shape elements from a feature's functions. We have also developed a generic geometric shape based object recognition method which uses many local features. The proposed recognition method considers the concept of ontology for representation of generic functions of objects. And the use of a general shape-function reasoning with context understanding enhances the performance of object recognition. © Springer-Verlag Berlin Heidelberg 2005.

关键词： Object recognition

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Self-adjusting beat detection and prediction in music

Self-adjusting beat detection and prediction in music

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Proceedings - IEEE International Conference on Acoustics, Speech, and Signal processing

作者： Harper, Robert Jernigan, M.E. Vision and Image Processing Lab. Systems Design Engineering University of Waterloo Waterloo Ont. Canada

This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for selection as the best beat hypothesis. Beat prediction nodes perform period self-adjustment to better represent the detected music beat period. The system is tested using a variety of music from different genres and shows promise, in many cases with high correct beat detection percentages.

关键词： Computer music

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Self-adjusting beat detection and prediction in music

Self-adjusting beat detection and prediction in music

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International Conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： R. Harper M.E. Jernigan Vision and Image Processing Lab Systems Design Engineering University of Waterloo Waterloo ONT Canada

This paper proposes a new approach to beat detection and prediction in music. Recurrent timing networks are used to detect and predict periodicities in an onset stream and are contained within nodes that compete for selection as the best beat hypothesis. Beat prediction nodes perform period self adjustment to better represent the detected music beat period. The system is tested using a variety of music from different genres and shows promise, in many cases with high correct beat detection percentages.

关键词： Multiple signal classification Timing Music Signal processing Delay Humans Intelligent networks image processing design engineering systems engineering and theory

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Interactive tools for signal processing education. The signal processing instructional facility (SPIF lab) at Washington State University

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Computer Applications in engineering Education 1993年第6期1卷 517-517页

作者： Bamberger, R.H. Signal Processing Instructional Facility School of Electrical Engineering and Computer Science Washington State University Pullman WA 99164-2752 Dr. Roberto H. Bamberger:was born on January 30 1965 in Buenos Aires Argentina. He received his B.E.E and PhD from the Georgia Institute of Technology in 1986 and 1990 respectively. Currently Dr. Bamberger is an assistant professor in the School of Electrical Engineering and Computer Science at Washington State University. Dr. Bamberger's research program is focused on the design implementation and application of multidimensional multirate filter banks subband image and video compression analysis of nonstationary data and application of digital signal processing techniques in the design of analog and mixed-mode integrated circuits. Dr. Bamberger is the founder and director of the Signal Processing Instructional Facility (SPIF Lab) at Washington State University. The SPIF Lab features interactive multimedia-based tools for teaching concepts related to linear systems theory. Dr. Bamberger is active in developing multimedia-based instructional laboratories for a variety of other topics as well. In addition to his teaching and research programs Dr. Bamberger is an associate editor for theIEEE Transactions on Signal Processingand a member of the editorial board forComputer Applications in Engineering Education. Dr. Bamberger is also advisor to the WSU Student Branch of the IEEE and assistant director of the WSU Cougar Marching Band. Dr. Bamberger is a member of Phi Eta Sigma Eta Kappa Nu Tau Beta Pi and Kappa Kappa Psi.

The Signal processing Instructional Facility (SPIF lab) is an experiment in using in interactive multimedia for teaching concepts related to linear systems theory and signal processing. The goals of the SPIF lab are to augment, enhance, and interconnect sophomore, junior, and senior level courses with the common thread of linear systems and transforms by unifying the experimentation medium. In this fashion, physical phenomenon is returned to the forefront of engineering education. The laboratory features powerful Mathematica Notebooks (a form of hypertext) and interactive applications that use dedicated DSP microprocessors.

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INGEN: A robot vision system for Generic object recognition

INGEN: A robot vision system for Generic object recognition

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1991 Workshop on Directions in Automated CAD-Based vision, CADVIS 1991

作者： Vayda, A.J. Kak, A.C. Image Processing Systems Division Environmental Research Institute of Michigan Ann ArborMI48107 United States Robot Vision Lab School of Electrical Engineering Purdue University West LafayetteIN47907 United States

ISBN: (纸本)0818621478

Generic shape recognition is the problem of determining the pose and dimensions of objects for which only shape models are available and the object's size is unknown. One application domain for generic object recognition is the handling and sorting of postal objects. Because metrical information relating object features to one another is not available, the more common feature-based approaches are inadequate. Our system, INGEN (INference engine for GENeric object recognition), uses a data-driven approach to determine the pose and size of objects with generic shapes such as parallelepipeds and cylinders. This system successfully recognizes occluded objects in heaps. It also handles scenes which have irregularities in surfaces and edges - such irregularities are common to postal objects - as well as shadows and irregularities in the range data itself. © 1991 Proceedings - Workshop on Directions in Automated CAD-Based vision, CADVIS 1991. All rights reserved.

关键词： Object recognition

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INGEN: a robot vision system for generic object recognition

INGEN: a robot vision system for generic object recognition

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Workshop on Directions in Automated CAD-Based vision

作者： A.J. Vayda A.C. Kak Image Processing Systems Division Environmental Research Institute of Michigan Ann Arbor MI USA Robot Vision Lab School of Elecmcal Engineering Purdue University West Lafayette IN USA

Generic shape recognition is the problem of determining the pose and dimensions of objects for which only shape models are available and the object's size is unknown. One application domain for generic object recognition is the handling and sorting of postal objects. Because metrical information relating object features to one another is not available, the more common feature-based approaches are inadequate. INGEN (inference engine for generic object recognition), uses a data-driven approach to determine the pose and size of objects with generic shapes such as parallelepipeds and cylinders. This system successfully recognizes occluded objects in heaps. It also handles scenes which have irregularities in surfaces and edges-such irregularities are common to postal objects-as well as shadows and irregularities in the range data itself.< >

关键词： Robot vision systems Object recognition Shape Layout Surface fitting image processing image recognition Sorting Engine cylinders Postal services

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