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检索条件"机构=Computer Vision & Image Analysis Laboratory Department of Electrical and Computer Engineering"

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Automated generation of an efficient MPEG-4 Reconfigurable Video Coding decoder implementation

Automated generation of an efficient MPEG-4 Reconfigurable V...

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Conference on Design and Architectures for Signal and image Processing

作者： Ruirui Gu Jonathan Piat Mickael Raulet Jorn W. Janneck Shuvra S. Bhattacharyya Department of Electrical and Computer Engineering University of Maryland College Park MD USA IETR Laboratory UMR CNRS 6164 Image and Remote Sensing Group Rennes France

This paper proposes an automatic design flow from user-friendly design to efficient implementation of video processing systems. This design flow starts with the use of coarse-grain dataflow representations based on the CAL language, which is a complete language for dataflow programming of embedded systems. Our approach integrates previously developed techniques for detecting synchronous dataflow (SDF) regions within larger CAL networks, and exploiting the static structure of such regions using analysis tools in The Dataflow interchange format Package (TDP). Using a new XML format that we have developed to exchange dataflow information between different dataflow tools, we explore systematic implementation of signal processing systems using CAL, SDF-like region detection, TDP-based static scheduling, and CAL-to-C (CAL2C) translation. Our approach, which is a novel integration of three complementary dataflow tools - the CAL parser, TDP, and CAL2C - is demonstrated on an MPEG Reconfigurable Video Coding (RVC) decoder.

关键词： Transform coding XML Video coding Decoding Digital signal processing Computational modeling Libraries

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Using colour local binary pattern features for face recognition

Using colour local binary pattern features for face recognit...

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IEEE International Conference on image Processing

作者： Jae Young Choi Konstantinos N. Plataniotis Yong Man Ro Image and Video System Laboratory Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Electrical and Computer Engineering University of Toronto Toronto ONT Canada

In this paper, we propose a novel feature representation based on color-based Local Binary Pattern (LBP) texture analysis for face recognition (FR). The proposed method exploits both color and texture discriminative features of a face image for FR purpose. We evaluate the proposed feature using three public face databases: CMU-PIE, Color FERET, and XM2VTSDB. Experimental results show that the results of the proposed feature impressively better than the results of grayscale LBP and color features. In particular, it is shown that the proposed feature is highly robust against severe variations in illumination and spatial resolution.

关键词： image color analysis Face Feature extraction Gray-scale Histograms Lighting Pixel

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Loop transformations for interface-based hierarchies IN SDF graphs

Loop transformations for interface-based hierarchies IN SDF ...

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International Conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Jonathan Piat Shuvra S. Bhattacharyya Michael Raulet IETR/INSA Image and Remote Sensing laboratory UMR CNRS 6164 Rennes France Department of Electrical and Computer Engineering University of Maryland College Park MD USA

Data-flow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of data-flow, termed synchronous data-flow (SDF), offers strong compile-time predictability properties, but has limited expressive power. A new type of hierarchy (Interface-based SDF) has been proposed allowing more expressivity while maintaining its predictability. One of the main problems with this hierarchical SDF model is the lack of trade-off between parallelism and network clustering. This paper presents a systematic method for applying an important class of loop transformation techniques in the context of interface-based SDF semantics. The resulting approach provides novel capabilities for integrating parallelism extraction properties of the targeted loop transformations with the useful modeling, analysis, and code reuse properties provided by SDF.

关键词： Application software computer interfaces Parallel processing System recovery Power engineering computing Digital signal processing Power system modeling Processor scheduling computer architecture Programming profession

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Spatio-temporal localization of focal epileptic sources from intracranial electrocortical recordings using an independent component analysis (ICA) algorithm

Spatio-temporal localization of focal epileptic sources from...

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Iranian Conference of Biomedical engineering (ICBME)

作者： Mohamed Amin Latif Abbas Babajani-Feremi Daniel Barkmeier Jeffery A. Leob Hamid Soltanian-Zadeh Image Analysis Laboratory Radiology Department Henry Ford Health System Detroit MI USA The Center of Molecular Medicine and Genetics Wayne State University School of Medicine Detroit MI USA CIPCE Electrical and Computer Engineering Department University of Tehran Tehran Iran

The gold standard for the localization of epileptic activities in the cerebral cortex is intracranial electrocorticography (ECoG) electrodes placed directly on the brain surface. However, it has limitations in being able to localize deep brain epileptic sources. As a means to improve the localization of epileptic activities from these subdural electrical recordings, we developed a simple source monitoring system to detect and localize these deep sources. The method is based on an independent component analysis (ICA) algorithm known as joint approximate diagonalization of eigen-matrices (JADE). This method was tested on patients with neocortical epileptic foci focusing on the interictal spikes from ECoG signals. The proposed method localizes the interictal epileptic discharges to both superficial as well as deep regions of the human neocortex and has the potential to improve the localization and surgical outcomes of patients with medically refractory epilepsy undergoing surgical resections.

关键词： Biological system modeling Epilepsy Surgery Electrodes Magnetic resonance imaging Signal to noise ratio Brain modeling

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Color component feature selection in feature-level fusion based color face recognition

Color component feature selection in feature-level fusion ba...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)

作者： Seung Ho Lee Jae Young Choi Konstantinos N. Plataniotis Yong Man Ro Image and Video System Laboratory Korea Advanced Institute of Science and Technology Daejeon South Korea School of Computer Science Ryerson University Toronto ONT Canada Department of Electrical and computer Engineering University of Toronto Toronto ONT Canada

In this paper, we propose a new color face recognition (FR) method which effectively employs feature selection algorithm in order to find the set of optimal color components (from various color models) for FR purpose. The proposed FR method is also designed to improve FR accuracy by combining the selected color components at the feature level. The effectiveness of the proposed color FR method has been successfully demonstrated using two public CMU-PIE and Color FERET face databases (DB). In our comparative experiments, traditional grayscale-based FR, previous color-based FR, and popular local binary pattern (LBP) based FR methods were compared with the proposed method. Experimental results show that our color FR method performs better than the aforementioned three different FR approaches. In particular, the proposed method can achieve 7.81% and 18.57% improvement in FR performance on the CMU-PIE and Color FERET DB, respectively, compared to representative color-based FR solutions previously developed.

关键词： image color analysis Face Classification algorithms Lighting Feature extraction Principal component analysis Optimized production technology

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Face annotation for online personal videos using color feature fusion based face recognition

Face annotation for online personal videos using color featu...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Jae Young Choi Konstantinos N. Plataniotis Yong Man Ro Image and Video System Laboratory Korea Advanced Institute of Science and Technology Daejeon South Korea The Edward S Rogers Sr Department of Electrical and Computer Engineering University of Toronto Toronto ONT Canada

This paper proposes a novel weighted feature fusion in color face recognition (FR) to automatically annotate faces in personal videos. In the proposed FR method, multiple face images (belonging to the same subject) are clustered from a sequence of video frames. To facilitate a complementary effect on improving annotation performance, the grouped faces are combined using the proposed weighted feature fusion. In addition, we make effective use of facial color feature to cope with decrease in annotation performance due to a low-resolution face in personal videos. To evaluate the effectiveness of proposed FR method, more than 40,000 video frames for 10 real-world personal videos are collected from an existing online video sharing website. Experimental results show that the proposed FR method significantly improves annotation performance obtained using conventional grayscale image based FR methods.

关键词： Face Videos image color analysis Feature extraction Training Power capacitors Accuracy

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Multiple-atlas-based automatic sementation of hippocampus for lateralization in temporal lobe epilepsy

Multiple-atlas-based automatic sementation of hippocampus fo...

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IEEE International Symposium on Biomedical Imaging

作者： Alireza Akhondi-Asl Kourosh Jafari-Khouzani Hamid Soltanian-Zadeh Image Analysis Laboratory Department of Diagnostic Radiology Henry Ford Health System Detroit MI USA School of Cognitive Sciences Institute of Studies in Theoretical Physics and Mathematics Tehran Iran Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran Iran

ISBN: (纸本)9781424441259

We introduce a 3D segmentation framework which uses principal shapes. The probabilistic energy function of the method is defined based on intensity, tissue type, and location information of the structures using a multiple atlas method. For intensity information, nonparametric probability density function is used which considers intensity relation of different structures. To find a local minimum of the energy function, a two-step optimization strategy is used. In the first step, shape parameters are optimized based on the analytic derivatives of the energy function. In the second step, shapes of the structures are fine-tuned using a level set method. The proposed method is shown to be superior to some popular methods in the literature using a dataset of 64 patients with mesial temporal lobe epilepsy. In addition, the method can be used for lateralization with accuracy close to that of manual segmentation.

关键词： Hippocampus Temporal lobe Epilepsy image segmentation Principal component analysis Shape control Probability density function Level set Skull Algorithm design and analysis

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Live demonstration: Spatial-temporal color video reproduction from noisy CFA sequence track: Digital signal processing

Live demonstration: Spatial-temporal color video reproductio...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Lei Zhang Weisheng Dong Chiu-Wai Hui Xiaolin Wu Guangming Shi Department of computing Hong Kong Polytechnic University Hong Kong China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China Department of Electrical and Computer Engineering McMaster University Canada

This demonstration shows a spatial-temporal denoising and demosaicking scheme for noisy CFA videos. This scheme can significantly reduce the noise-caused color artifacts and effectively preserve the image edge structures. The experimental results showed that this scheme achieves promising color video reproduction in terms of both PSNR and visual perception.

关键词： Color Colored noise Digital signal processing Noise reduction PSNR Principal component analysis Digital cameras image processing Laboratories computer science education

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A statistical shape model using 2D-principal component analysis from few medical samples and its evaluation

A statistical shape model using 2D-principal component analy...

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International Conference on Software engineering and Data Mining (SEDM)

作者： Tomoko Tateyama Taishi Tanaka Shinya Kohara Amir Hossein Foruzan Akira Furukawa Yen-Wei Chen Intelligent Image Processing Laboratory College of Information Science and Engineering Ritsumeikan University Shiga Japan Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Department of Radiology Shiga University of Medical Science Shiga Japan

ISBN: (纸本)9781424473243;9788988678220;9788988678213

Since the medical training samples are very limited, it is difficult to construct a statistical shape model with good generalization using few samples. In this paper, we propose a novel statistical shape modeling method using 2D PCA. The 3D shape is represented as a matrix by spherical parameterization. The experiments showed that our proposed method can reconstruct statistical shape model with good generalization even using fewer samples.

关键词： Biomedical imaging Shape control Biomedical engineering Principal component analysis Liver Information analysis Educational institutions Azimuth Covariance matrix image analysis

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Volume-based Magnetic Resonance Brain image Classification

Volume-based Magnetic Resonance Brain Image Classification

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International Conference on Genetic and Evolutionary Computing (ICGEC)

作者： Yaw-Jiunn Chiou Jyh-Wen Chai Clayton Chi-Chang Chen Shih-Yu Chen Hsian-Min Chen Yen-Chieh Ouyang Wu-Chung Su Ching Tsorng Tsai Ching-Wen Yang San-Kan Lee Chein-I Chang Department of Electrical Engineering National Chung Hsing University Taichung Taiwan Department of Radiology Taichung Veterans General Hospital Taichung Taiwan Section of Radiology College of Medicine China Medical University Taichung Taiwan Department of Radiological Technology Central Taiwan University of Science and Technology Taichung Taiwan Department of Biomedical Engineering Hungkuang University Taichung Taiwan Computer Science Department Tunghai University Taichung Taiwan Computer Center Taichung Veterans General Hospital Taichung Taiwan Remote Sensing Signal and Image Processing Laboratory Department of Computer Science and Electrical Engineering University of Maryland Baltimore MD USA

This paper develops a volume-based technique, called Volume Sphering analysis (VSA) which can process all acquired Magnetic Resonance (MR) image slices formed image cube using only one set of training samples obtained from an image slice. So, several significant advantages and benefits can be gained from our proposed VSA. In the past, when MR image classification is performed, each image slice requires its own specific training samples and training samples obtained from one slice are not applied to another slice. The VSA allows users to reduce computational time. In addition, it saves significant effort in selecting training samples for each of image slices. Thirdly, it is robust to all image slices compared to the traditional one-slice MR image classification which is sensitive to each image slice. Experimental results demonstrate that the VSA performs as well as does that using specifically selected training samples for individual image slices.

关键词： Training Support vector machine classification image classification Vectors Volume measurement Magnetic resonance

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