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检索条件"机构=Image Processing and Pattern Recognition Research Center"

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Generalized stochastic tube model: Tracking 3D blood vessels in MR images 12

Generalized stochastic tube model: Tracking 3D blood vessels...

引用

12th IAPR International Conference on pattern recognition - Conference B: pattern recognition and Neural Networks, ICPR 1994

作者： Huang, Qian Stockman, George C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

ISBN: (纸本)0818662700

This paper addresses the issue of tracking tubular objects, particularly blood vessels from MR images. A model-based approach is adopted. The generalized stochastic tube (GST) model is developed which is an extension of our previously proposed Generalized Tube (GT) model. Transitions among adjacent tubes are explicitly parameterized. Integrated with a bivariate Gaussian density function adopted to model the blood flow within cross sections, the GST model is applied to tracking blood vessels in MRA volumetric data. Experimental results on both synthetic data with different degrees of Gaussian noise and real MRA data demonstrated that simultaneously utilizing both models yields robust performance under noisy conditions. © 1994 Institute of Electrical and Electronics Engineers Inc.. All rights reserved.

关键词： Blood vessels

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Real-time multi-resolution digital elevation map creation using the sensor model of a stereovision sensor

Real-time multi-resolution digital elevation map creation us...

引用

International Conference on Intelligent Transportation

作者： Szilárd Mandici Sergiu Nedevschi Image Processing and Pattern Recognition Research Group Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781509018901

This paper presents an approach for constructing high quality Digital Elevation Maps (DEMs) from dense stereo data. As opposed to classical DEM creation algorithms, the proposed solution uses the sensor models of the stereovision sensor to correct the raw data. The direct and inverse sensor models are found from experiments. The algorithm decomposes the 2D cells containing vertical objects or object boundaries in order to model the objects more precisely. The approach distinguishes between 2 types of cells: cells containing horizontal and vertical objects. For the vertical object cells, connected components in the histogram of heights are found while in the horizontal object cells, the most representative height is determined using a mode-seeking approach similar to mean shift. The 3D reconstructed points are assigned to the cells around it by considering the probability of correspondence based on the 2D Gaussian component of the direct sensor model. In order to eliminate the noise, a similarity measure is computed between each 3D point and cell based on the calculated DEM cell descriptor and is discarded if the similarity score is smaller than a predefined threshold. The algorithm is implemented on the GPU in order to achieve a real-time execution speed and it is evaluated extensively in comparison with other DEM creation algorithms using the latest KITTI stereo benchmark data. The obtained DEM is denser, more accurate and the object boundaries are modeled more precisely.

关键词： Three-dimensional displays Robot sensing systems image reconstruction Two dimensional displays Histograms Solid modeling Mathematical model

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Model-based automatic recognition of blood vessels from MR images and its 3D visualization

Model-based automatic recognition of blood vessels from MR i...

引用

The 1994 1st IEEE International Conference on image processing

作者： Huang, Qian Stockman, G.C. IBM Research Division Almaden Research Center 650 Harry Road San JoseCA95120-6099 United States Pattern Recognition and Image Processing Laboratory Department of Computer Science Michigan State University East LansingMI48824 United States

A model-based approach is used for recognizing arterial blood vessels from MRA volumetric data. The modeling includes (1) a generalized stochastic tube model characterizing the structural properties of the vessels, and (2) a bivariate Gaussian function, modeling the expected cross sectional blood flow. This integrated model renders the recognition problem as a parameter estimation problem which is subsequently solved in a hierarchical fashion. Due to the descriptive representation for objects, a new visualization scheme for blood vessels is proposed that allows the observation of the blood flow of each cross section along a recognized vessel. Some experimental results from both synthetic data and real MRA volumes are given. The visualization of interior blood flow patterns for some vessels are also shown. © 1994 IEEE.

关键词： Blood vessels

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Fast Pedestrian Detection for Mobile Devices

Fast Pedestrian Detection for Mobile Devices

引用

International IEEE Conference on Intelligent Transportation Systems

作者： Arthur Daniel Costea Andreea Valeria Vesa Sergiu Nedevschi Image Processing and Pattern Recognition Research Center Computer Science Department Technical University of Cluj-Napoca Romania

ISBN: (纸本)9781467365970

In this paper we present a fast and robust solution for pedestrian detection that can run in real time conditions even on mobile devices with limited computational power. An optimization of the channel features based multiscale detection schemes is proposed by using 8 detection models for each half octave scales. The image features have to be computed only once each half octave and there is no need for feature approximation. We use multiscale square features for training the multiresolution pedestrian classifiers. The proposed solution achieves state of art detection results on Caltech pedestrian benchmark at over 100 FPS using a CPU implementation, being the fastest detection approach on the benchmark. The solution is fast enough to perform under real time conditions on mobile platforms, yet preserving its robustness. The full detection process can run at over 20 FPS on a quad-core ARM CPU based smartphone or tablet, being a suitable solution for limited computational power mobile devices or embedded platforms.

关键词： pedestrian detection image features solution image feature Mobile devices Pedestrians Solutions Fasting Robust solution FES gene Smartphone Solution computational power

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Motion constraint patterns

Motion constraint patterns

引用

1993 IEEE Workshop on Qualitative Vision, WQV 1993

作者： Fermüller, Cornelia Computer Vision Laboratory Center for Automation Research University of Maryland College ParkMD20742-3275 United States Department for Pattern Recognition and Image Processing Institute for Automation Technical University Vienna Treitlstraße 3 ViennaA-1040 Austria

ISBN: (纸本)0818636920

The problem of egomotion recovery has been treated by using as input local image motion, with the published algorithms utilizing the geometric constraint relating 2-D local image motion (optical flow, correspondence, derivatives of the image flow) to 3-D motion and structure. Since it has proved very difficult to achieve accurate input (local image motion), a lot of effort has been devoted to the development of robust techniques. A new approach to the problem of egomotion estimation is taken, based on constraints of a global nature. It is proved that local normal flow measurements form global patterns in the image plane. The position of these patterns is related to the three dimensional motion parameters. By locating some of these patterns, which depend only on subsets of the motion parameters, through a simple search technique, the 3-D motion parameters can be found. The proposed algorithmic procedure is very robust, since it is not affected by small perturbations in the normal flow measurements. As a matter of fact, since only the sign of the normal flow measurement is employed, the direction of translation and the axis of rotation can be estimated with up to 100% error in the image measurements. © 1993 IEEE.

关键词： Flow measurement

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Accurate moving object detection in thermal imagery

Accurate moving object detection in thermal imagery

引用

IEEE International Conference on Computer Science and Automation Engineering (CSAE)

作者： Zhang, Yan Zhao, Ting Gu, Jian Yu, Shengyang Testing Center of Security Products for Computer Information System Ministry of Public Security Quality Supervision Ministry of Public Security Shanghai China Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

ISBN: (纸本)9781424487257

Accurate detection of moving object provides a fundamental capability that drives numerous high-level computer vision applications. In this paper, a novel algorithm is proposed to detect objects in widely varying thermal imagery. On the basis of properties of thermal imagery, effective foreground and background model are first presented, and then these models are competitively used in an unified MAP-MRF framework to detect objects. Experimental results on challenging dataset show the robustness and effectivity of the proposed algorithm. © 2011 IEEE.

关键词： Object detection

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A framework for nuclear image enhancement based on the Anscomb transform and the bayesian thresholding

A framework for nuclear image enhancement based on the Ansco...

引用

IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Imen Mbazaa Najla Gribaa Nawres Khlifa Kamel Hamrouni Research Unit of Signal Processing Image Processing and Pattern Recognition National Engineering School of Sfax (ENIS) Tunisia

Scintigraphic imagery is an important data source since it provides morphological and functional clinical informations. However, scintigraphic images present very bad quality because of several degradation factors. In fact, the recorded data are embedded in noise modelled as the realisation of Poisson process. The aim of this paper is to provide a framework able to enhance nuclear images quality by Poisson intensity estimation. This framework consists, in a first step, of performing variance-stabilizing step for the Poisson process thanks to the Anscombe transformation. So the obtained data can be considered as contaminated by a white Gaussian noise. In a second step, it uses a Bayesian technique inspired of Pizurica approach, known in the literature for exhibiting good results as for white Gaussian noise. In fact, the complex wavelet packets were exploited regarding to Pizurica algorithm.

关键词： image enhancement Bayesian methods Gaussian noise Noise reduction Wavelet packets Wavelet transforms image processing image quality Pixel Cameras

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Robust Parametric and Semi-parametric Spot Fitting for Spot Array images 8

Robust Parametric and Semi-parametric Spot Fitting for Spot ...

引用

8th International Conference on Intelligent Systems for Molecular Biology, ISMB 2000

作者： Brändle, Norbert Chen, Horng-Yang Bischof, Horst Lapp, Hilmar Vienna University of Technology Pattern Recognition and Image Processing Group Treitlstr. 3/1832 ViennaA-1040 Austria Novartis Research Institute Vienna Genetics Brunner Str. 59 ViennaA-1230 Austria

ISBN: (纸本)1577351150

In this paper we address the problem of reliably fitting parametric and semi-parametric models to spots in high density spot array images obtained in gene expression experiments. The goal is to measure the amount of label bound to an array element. A lot of spots can be modelled accurately by a Gaussian shape. In order to deal with highly overlapping spots we use robust M-estimators. When the parametric method fails (which can be detected automatically) we use a novel, robust semi-parametric method which can handle spots of different shapes accurately. The introduced techniques are evaluated experimentally. Copyright © 2000, American Association for Artificial Intelligence (***). All rights reserved.

关键词： Gene expression

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Ethnicity classification based on gait using multi-view fusion

Ethnicity classification based on gait using multi-view fusi...

引用

2010 IEEE Computer Society Conference on Computer Vision and pattern recognition - Workshops, CVPRW 2010

作者： Zhang, De Wang, Yunhong Bhanu, Bir Intelligent Recognition and Image Processing Laboratory Beihang University Beijing 100191 China Center for Research in Intelligent Systems University of California Riverside CA 92521 United States

ISBN: (纸本)9781424470297

The determination of ethnicity of an individual, as a soft biometrics, can be very useful in a video-based surveillance system. Currently, face is commonly used to determine the ethnicity of a person. Up to now, gait has been used for individual recognition and gender classification but not for ethnicity determination. This paper focuses on the ethnicity determination based on fusion of multi-view gait. Gait Energy image (GEI) is used to analyze the recognition power of gait for ethnicity. Feature fusion, score fusion and decision fusion from multiple views of gait are explored. For the feature fusion, GEI images and camera views are put together to render a third-order tensor (x;y;view). A multilinear principal component analysis (MPCA) is used to extract features from tensor objects which integrate all views. For the score fusion, the similarity scores measured from single views are combined with a weighted SUM rule. For the decision fusion, ethnicity classification is realized on each individual view first. The classification results are then combined to make the final determination with a majority vote rule. A database of 36 walking people (East Asian and South American) was acquired from 7 different camera views. The experimental results show that ethnicity can be determined from human gait in video automatically. The classification rate is improved by fusing multiple camera views and a comparison among different fusion schemes shows that the MPCA based feature fusion performs thebest. © 2010 IEEE.

关键词： Principal component analysis

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A robust, real-time voice activity detection algorithm for embedded mobile devices

A robust, real-time voice activity detection algorithm for e...

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作者： Wu, Bian Ren, Xiaolin Liu, Chongqing Zhang, Yaxin Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai 200030 China Motorola Labs. China Research Center 38F CITIC Sq. 1168 Nanjing Rd. W. Shanghai 200041 China

When an Automatic Speech recognition (ASR) system is applied in noisy environments, Voice Activity Detection (VAD) is crucial to the performance of the overall system. The employment of the VAD for ASR on embedded mobile systems will minimize physical distractions and make the system convenient to use. Conventional VAD algorithm is of high complexity, which makes it unsuitable for embedded mobile devices;or of low robustness, which holds back its application in mobile noisy environments. In this paper, we propose a robust VAD algorithm specifically designed for ASR on embedded mobile devices. The architecture of the proposed algorithm is based on a two-level decision making strategy, where there is an interaction between a lower features-based level and subsequent decision logic based on a finite-state machine. Many discriminating features are employed in the lower level to improve the robustness of the VAD. The two-level decision strategy allows different features to be used in different states and reduces the cost of the algorithm, which makes the proposed algorithm suitable for embedded mobile devices. The evaluation experiments show the proposed VAD algorithm is robust and contribute to the overall performance gain of the ASR system in various acoustic environments. © 2005 Springer Science+Business Media, Inc.

关键词： Speech recognition

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