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Hidden Markov models for silhouette classification

Hidden Markov models for silhouette classification

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作者： Abd-Almageed, Wael Smith, Christopher Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico United States

ISBN: (纸本)1889335185

In this paper, a new technique for object classification from silhouettes is presented. Hidden Markov Models are used as a classification mechanism. Through a set of experiments, we show the validity of our approach and show its invariance under severe rotation conditions. Also, a comparison with other techniques that use Hidden Markov Models for object classification from silhouettes is presented.

关键词： Hidden Markov models

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ViRoom - Low cost synchronized multicamera system and its self-calibration

ViRoom - Low cost synchronized multicamera system and its se...

引用

24th Symposium of the German Pattern Recognition Association, DAGM 2002

作者： Svoboda, Tomáš Hug, Hanspeter Van Gool, Luc Computer Vision Laboratory Department of Information Technology and Electrical Engineering Swiss Federal Institute of Technology Gloriastrasse 35 8092 Zürich Switzerland

ISBN: (纸本)354044209X

This paper presents a multicamera Visual Room (ViRoom). It is constructed from low-cost digital cameras and standard computers running on Linux. Software based synchronized image capture is introduced. A fully automatic self-calibration method for multiple cameras and without any known calibration object is proposed and verified by 3D reconstruction experiments. This handy calibration allows an easy reconfiguration of the setup. Aside from the computers which are usually already available, such a synchronized multicamera setup with six or seven cameras costs less than 1000 USD. © Springer-Verlag Berlin Heidelberg 2002.

关键词： Calibration

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A phantom validation of the FastHARP pulse sequence

A phantom validation of the FastHARP pulse sequence

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IEEE International Symposium on Biomedical Imaging

作者： S. Sampath V. Parthasarathy J.L. Prince Image Analysis and Communications Laboratory Electrical and Computer Engineering Department Johns Hopkins University Baltimore MD USA

The FastHARP magnetic resonance imaging pulse sequence, coupled with fast processing, has the potential to measure and display cardiac function in real-time during clinical dobutamine stress tests. In this paper, we present a preliminary validation of the tracking capability of material points using this imaging sequence. Points lying on a mesh embedded in a moving gel phantom are tracked manually using high-resolution images and are simultaneously tracked using HARP tools. The experiment proves that for this particular type of motion actuation, the points tracked in images using the FastHARP sequence followed by fast and automated HARP tracking tools are comparable to results obtained from images acquired in much longer times with very tedious analysis involving manual intervention.

关键词： Imaging phantoms Magnetic resonance imaging Pulse measurements Tracking Couplings Stress measurement Displays Testing Magnetic materials High-resolution imaging

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No-reference perceptual quality assessment of JPEG compressed images

No-reference perceptual quality assessment of JPEG compresse...

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IEEE International Conference on image Processing

作者： Zhou Wang H.R. Sheikh A.C. Bovik Laboratory for Image and Video Engineering (LIVE) Department of Electrical and Computer Engineering University of Texas Austin Austin TX USA

Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance.

关键词： Quality assessment Transform coding image coding PSNR Distortion measurement image databases Humans Algorithm design and analysis Testing Predictive models

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Resolving ambiguity in depth extraction for motion capture using genetic algorithm

Resolving ambiguity in depth extraction for motion capture u...

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Congress on Evolutionary Computation

作者： Yin Yee Wai Chi Kin Chow Tong Lee Computer Vision and Image Processing Laboratory Department of Electronic Engineering Chinese University of Hong Kong Sha Tin Hong Kong China

There is considerable interest in motion capture from an image sequence taken from a video camera. However, since the images only consist of 2D information, the distance of an object from the image plane cannot be determined uniquely unless some constraints are imposed. Coplanar motion is a popular constraint adopted for this purpose. If the captured motion is not coplanar, only two possible solutions can be obtained at the best even the actual size of the object is given. Therefore, the motion cannot be captured without resolving the distances of each control point from the image plane. By assuming smooth transition, the problem is formulated as one minimizing the transition between image frames and the application of GA to solve this optimization problem is proposed.

关键词： Genetic algorithms Humans Cameras image resolution image sequences Animation Motion analysis Optical filters Optical recording Optical sensors

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Mixture models for dynamic statistical pressure snakes

Mixture models for dynamic statistical pressure snakes

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International Conference on Pattern Recognition

作者： W. Abd-Almageed C.E. Smith Robotics Artificial Intelligence and Vision Laboratory Electrical and Computer Engineering Department University of New Mexico Albuquerque NM USA

This paper introduces a new approach to statistical pressure snakes. It uses statistical modeling for both object and background to obtain a more robust pressure model. The Expectation Maximization (EM) algorithm is used to model the data into a Mixture of Gaussians (MoG). Bayesian theory is then employed as a decision making mechanism. Experimental results using the traditional pressure model and the new mixture pressure model demonstrate the effectiveness of the new models.

关键词： image edge detection computer vision Equations Robot vision systems Robustness Bayesian methods Active contours Gaussian distribution Intelligent robots Artificial intelligence

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ARMA modeling for estimation of permeability from perfusion MRI

ARMA modeling for estimation of permeability from perfusion ...

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IEEE International Symposium on Biomedical Imaging

作者： M.M. Khalighi H. Soltanian-Zadeh J.R. Ewing Radiology Image Analysis Laboratory Henry Ford Health System Detroit MI USA Electrical and Computer Engineering Department University of Tehran Tehran Iran Neurology Department Henry Ford Health System Detroit MI USA

We develop noninvasive MRI techniques that quantify the permeability of the Blood-Brain Barrier (BBB). Using such gadolinium compounds as Gd-DTPA and gadomer-17, changes in R1 (R1=1/T1) can be measured and used as estimates of tissue concentration versus time, thus permitting estimates of BBB permeability parameters. Estimating BBB permeability parameters requires deconvolution in a linear system, a task that has been accomplished heretofore by nonlinear least-squares procedures, although these techniques tend to instability in the presence of noise. We introduce a method using Z-transform and Autoregressive Moving Average (ARMA) modeling to perform this deconvolution. This method has the advantage that it linearizes the optimization procedure by which parametric estimates are formed, and it stabilizes the deconvolution. in the presence of noise.

关键词： Magnetic resonance imaging Autoregressive processes Deconvolution Iron Equations Proposals image analysis Nervous system Permeability measurement Time measurement

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Automatic thresholding abundance fractional images for mixed pixel classification

Automatic thresholding abundance fractional images for mixed...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Shao-Shan Chiang Chein-I Chang Department of Electrical Engineering Lunghwa University of Science and Technology Taoyuan Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Mixed pixel classification is different from spatial-based image classification in the sense that the former deals with abundance fractional images resulting from mixed pixels as opposed to classification maps produced by the latter. As a result, mixed pixel classification is generally carried out by visual inspection on the generated abundance fractional images. Consequently, it can be very subjective and vary with different human interpretations. Under such circumstance, it is difficult to substantiate an algorithm and conducting a comparative analysis is impossible. This paper presents one histogram-based approach to thresholding abundance fractional images. It thresholds an abundance fractional image into a binary image using a probability of confidence as a threshold value.

关键词： Pixel Humans image analysis Inspection image segmentation Signal processing image processing Laboratories computer science image classification

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A study between orthogonal subspace projection and generalized likelihood ratio test in hyperspectral image analysis

A study between orthogonal subspace projection and generaliz...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Qian Du Hsuan Ren Chein-I Chang Department of Electrical Engineering and Computer Science Texas A and M University Kingsville TX USA Edgewood Chemical and Biological Center US Army MD USA Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

Orthogonal subspace projection (OSP) and generalized likelihood ratio test (GLRT) have shown success in hyperspectral image classification. The OSP is derived by maximizing signal-to-noise ratio (SNR) resulting from a linear mixture model in which the noise is assumed to be white. On the other hand, the GLRT is formulated based on a signal detection model that can be described by a binary hypothesis testing problem. In order for the GLRT to derive an analytical form, the noise in the signal detection model is generally assumed to be white Gaussian noise. However, Gaussianity is generally not true in remotely sensed imagery. Interestingly, such assumption has not been investigated. This paper presents a comparative study between OSP and GLRT based on their assumptions. In particular, a detailed analysis of assumptions made on these two approaches is conducted through a series of computer simulations. Experimental results show that the OSP does not depend on Gaussian noise. By the contrast, the GLRT is affected by the Gaussian noise assumption. If it is violated, its performance is degraded.

关键词： Testing Hyperspectral imaging image analysis Gaussian noise Signal to noise ratio Hyperspectral sensors Signal detection image classification Signal analysis Gaussian processes

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Normal mammogram classification based on regional analysis

Normal mammogram classification based on regional analysis

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Midwest Symposium on Circuits and Systems (MWSCAS)

作者： Yajie Sun C.F. Babbs E.J. Delp Video and Image Processing Laboratory School of Electrical and Computer Engineering Purdue University West Lafayette IN USA Department of Basic Medical Sciences School of Veterinary Medicine Purdue University West Lafayette IN USA

The majority of screening mammograms are normal. It will be beneficial if a detection system is designed to help radiologists readily identify normal regions of mammograms. In this paper, we will present a binary tree classifier based on the use of global features extracted from different levels of a 2-D Quincunx wavelet decomposition of normal and abnormal regional images. This classifier is then used to classify whether an entire whole-field mammogram is normal. This approach is fundamentally different from other approaches that identify a particular abnormality in that is independent of the particular type of abnormality.

关键词： Feature extraction Cancer detection Classification tree analysis Mammography Lesions Biomedical imaging Decision trees image databases Sun image processing

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