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检索条件"机构=Computer Vision and Image Processing Laboratory School of Electrical Engineering"

共 374 条记录，以下是161-170 订阅

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Varying pose face recognition using combination of discrete cosine & wavelet transforms

Varying pose face recognition using combination of discrete ...

引用

International Conference on Intelligent and Advanced Systems, ICIAS

作者： Seyed Omid Shahdi S. A. R. Abu-Bakar Computer Vision Video and Image Processing (CvviP) Research Laboratory Faculty of Electrical Engineering Universiti Teknologi Malaysia Skudai Johor Malaysia

Requirement for a person to face a camera for face identification process may no longer be necessary if the face recognition system is robust against variation of facial pose. In this paper, we proposed a face recognition method which remains reliable even in very large head pose variations. In this method, feature from local regions of face are extracted after employing both discrete cosine transform and discrete wavelet transform. Learning strategy is then applied to infer the relationship between face in a given pose and its frontal view. Results we obtained are very promising considering that our proposed method solely relies on a single gallery image. We also demonstrated the high performance of our method in a condition whereby the face images are of low-resolution quality.

关键词： Face Face recognition Vectors Discrete cosine transforms Discrete wavelet transforms Feature extraction Databases

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What Can an image of a Smooth Lambertian Surface Tell About Camera Nonlinearity?

What Can an Image of a Smooth Lambertian Surface Tell About ...

引用

Canadian Conference on computer and Robot vision, CRV

作者： Moumen T. El-Melegy Aly A. Farag Electrical Engineering Department Assiut University Asyut Egypt Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA

This paper investigates the use of a single image of a smooth Lambertian surface to calibrate and remove some image nonlinearities due to the imaging device. To the best of our knowledge, this has not been addressed before in the literature. We show that this is possible, both theoretically and practically, taking advantage of some local shading measures that vary nonlinearly as a function of luminance and geometric nonlinearities (e.g., gamma correction and lens distortion). This can work as a basis for developing a simple method to estimate these nonlinearities from a single image. Several experiments are reported to validate the proposed method.

关键词： Cameras Distortion measurement Lenses Calibration Shape Lighting Surface treatment

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DIRECT METHOD FOR SHAPE RECOVERY FROM POLARIZATION AND SHADING

DIRECT METHOD FOR SHAPE RECOVERY FROM POLARIZATION AND SHADI...

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IEEE International Conference on image processing

作者： Ali H. Mahmoud Moumen T. El-Melegy Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Electrical Engineering Department Assiut University Assiut Egypt

Polarization imaging can give information about surface shape, and roughness. Polarization has been used for shape recovery, but with convex/concave reconstruction ambiguity. In this paper, we present a direct method to shape recovery using both polarization and shading that resolves this ambiguity, without the need for nonlinear optimization routines. Several experiments on synthetic and real datasets are reported to evaluate the proposed method. The method consistently outperforms some well-known methods based on polarization information alone.

关键词： Shape Surface reconstruction Azimuth image reconstruction Surface treatment Cameras

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Heat Kernels for Non-Rigid Shape Retrieval: Sparse Representation and Efficient Classification

Heat Kernels for Non-Rigid Shape Retrieval: Sparse Represent...

引用

Canadian Conference on computer and Robot vision, CRV

作者： Mostafa Abdelrahman Moumen El-Melegy Aly Farag Computer Vision and Image Processing Laboratory University of Louisville Louisville KY USA Electrical Engineering Department Assiut University Asyut Egypt

One of the major goals of computer vision and machine intelligence is the development of flexible and efficient methods for shape representation. This paper presents an approach for shape retrieval based on sparse representation of scale-invariant heat kernel. We use the Laplace-Beltrami eigen functions to detect a small number of critical points on the shape surface. Then a shape descriptor is formed based on the heat kernels at the detected critical points for different scales, combined with the normalized eigen values of the Lap lace-Beltrami operator. Sparse representation is used to reduce the dimensionality of the calculated descriptor. The proposed descriptor is used for classification via the collaborative representation-based classification with regularized least square algorithm. We compare our approach to two well-known approaches on two different data sets: the nonrigid world data set and the SHREC 2011. The results have indeed confirmed the improved performance of the proposed approach, yet reducing the time and space complicity of the shape retrieval problem.

关键词： Shape Heating Eigenvalues and eigenfunctions Kernel Vectors Manifolds Heat transfer

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ADAPTIVE ERROR CONCEALMENT FOR MULTIPLE DESCRIPTION VIDEO CODING USING MOTION VECTOR ANALYSIS

ADAPTIVE ERROR CONCEALMENT FOR MULTIPLE DESCRIPTION VIDEO CO...

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IEEE International Conference on image processing

作者： Neeraj Gadgil Meilin Yang Mary L. Comer Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Multiple Description Coding (MDC) is an efficient error resilient video coding method especially for real-time applications when retransmission is unacceptable. For applications with scalable, multicast and P2P environments, it is advantageous to use more than two descriptions. In this paper, we present an adaptive temporal-spatial error concealment method using motion vector analysis to improve the performance of MDC. Experimental results demonstrate the efficacy of the proposed method.

关键词： Packet loss Vectors Indexes image coding Decoding Correlation

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Macroblock-level adaptive error concealment methods for MDC

Macroblock-level adaptive error concealment methods for MDC

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Picture Coding Symposium, PCS

作者： Meilin Yang Mary L. Comer Edward J. Delp Video and Image Processing Laboratory (VIPER) School of Electrical and Computer Engineering Purdue University West Lafayette IN USA

Multiple Description Coding (MDC) is one of the most efficient error resilient video coding methods especially when retransmissions are not acceptable. For applications in scalable, multicast and P2P environments, it is advantageous to use more than two descriptions. In this paper, we present two macroblock-level adaptive error concealment methods to improve the performance of MDC with four descriptions. A Gilbert model is used as the channel model to simulate burst packet loss. Experimental results demonstrate the efficacy of the two proposed methods.

关键词： Encoding image analysis image coding PSNR Video coding Decoding

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Thermographic image analysis method in detection of canine bone cancer (osteosarcoma)

Thermographic image analysis method in detection of canine b...

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Congress on image and Signal processing, CISP

作者： Maryamsadat Amini Peng Liu Scott E Umbaugh Dominic J. Marino Catherine A. Loughin Computer Vision and Image Processing Laboratory Electrical and Computer Engineering Southern Illinois University Edwardsville IL USA Long Island Veterinary Specialists Plainview NY USA

Bone cancer is a pathologic condition which may occur for both humans and canines. This tumor develops quickly from within the bone tissue and become painful as it grows outward. A metastasized bone tumor may be cured by amputation, otherwise it will be fatal. The current diagnostic imaging methods for bone cancer are X-rays, Computed Tomography scan (CT scan), and Magnetic Resonance Imaging (MRI). The disadvantages of these methods include not enough detail in X-ray images, high dose of radiation from CT scans, and high expense of the time-consuming MRI method. In most of the bone cancer cases, when this tumor is detected by these imaging methods, it has already metastasized. The study is to investigate whether it is possible to detect canine bone cancer by thermography imaging. This alternative imaging method will decrease diagnostic time, expenses and prevent radiation exposure. The best classification success rate obtained in this study is 80.77%.

关键词： Feature extraction image color analysis Cancer Pattern classification Bones Software Histograms

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Iterative data adaptive anisotropic image filtering

Iterative data adaptive anisotropic image filtering

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2012 International Conference on Machine Learning and Cybernetics, ICMLC 2012

作者： Yang, Chang-Cai Zheng, Xin-Yi Tian, Jin-Wen Shang, Ke Tian, Xin Hao, Wei Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-Spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan 430074 China Electrical Engineering and Renewable Energy School China Three Gorges University Yichang 443002 China Institute of Intelligent Vision and Image Information College of Computer and Information Technology China Three Gorges University Yichang 443002 China

ISBN: (纸本)9781467314855

This paper presents a novel data-adaptive anisotropic filtering technique built on top of an iterative scheme. This new technique can preserve the original significant structures while suppressing noises to the largest extent. To achieve this goal, dominant orientation information of all gradients is used to control the local kernel adaptively. This results in elongated and elliptical contours spread along the directions of the local edge structure. With these locally adapted kernels, the smoothing is mostly effective along the edges, rather than across them. Therefore details in the original input can be preserved. The performance of the proposed data-adaptive anisotropic filter together with its iterative scheme are compared with the conventional Gaussian filter, anisotropic diffusion filter, the structure-adaptive anisotropic filter, and the iterative kernel regression scheme. The experimental results on both simulated Gaussian and Poission noisy images indicate that the proposed method performs the best, and the iterative scheme can significantly improve the performance of the noise suppressing while delivering high computation efficiency. © 2012 IEEE.

关键词： Iterative methods

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Automatic segmentation of brain tumors in magnetic resonance images

Automatic segmentation of brain tumors in magnetic resonance...

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IEEE-EMBS International Conference on Biomedical and Health Informatics, BHI 2012. In Conj. with the 8th Int. Symp.on Medical Devices and Biosensors and the 7th Int. Symp. on Biomedical and Health engineering

作者： Behzadfar, Neda Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence School of Electrical and Computer Engineering University of Tehran Tehran 14395-515 Iran Pooyandegan Rah Saadat Company Tehran 16756-965 Iran Image Analysis Laboratory Radiology Department HenryFord Healh System Detroit MI 48202 United States

ISBN: (纸本)9781457721779

Segmentation of tumors in magnetic resonance images (MRI) is an important task but is quite time consuming when performed manually by experts. Automating this process is challenging due to the high diversity in appearance of tumor tissue in different patients, and in many cases, similarity between tumor and normal tissues. This paper presents an automatic method for segmentation of brain tumors in MRI. We use images of patients with glioblastoma multiform tumors. After pre-processing and removal of the regions that do not have useful information (e.g., eyes and scalp), we create a projection image for determining the primary location of the tumor. This image provides an overall view of the tumor. Then, we grow the primary region to segment the entire tumor. This method is automatic and independent of the operator. It segments low contrast tumors without requiring their exacta tissue boundaries. The segmentation results obtained by the proposed approach are compared with those of an expert radiologist showing excellent correlations among them (R2=0.97). © 2012 IEEE.

关键词： Tumors

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Feature set augmentation for enhancing the performance of a non-intrusive quality predictor

Feature set augmentation for enhancing the performance of a ...

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International Workshop on Quality of Multimedia Experience, QoMEx

作者： Petko N. Petkov Hannes Helgason W. Bastiaan Kleijn Sound and Image Processing Laboratory School of Electrical Engineering KTH Royal Institute of Technology Stockholm Sweden School of Engineering and Computer Science Victoria University of Wellington Wellington New Zealand

A non-intrusive quality predictor constitutes a mapping from signal features to a (typically one dimensional) representation of the perceived quality. Assuming that the regression model performing the mapping is suited to the data, the performance of the predictor largely depends on how well the parameters of this regression model can be inferred from the training data. In situations where the training data is scarce, model performance is degraded due to over-fitting. The effects of over-fitting can be mitigated by feature selection but the model performance remains low due to the insufficiently representative training data. The objective we pursue is to enhance the performance of a quality predictor by augmenting the feature set with the output of a pre-trained quality predictor. This approach introduces an implicit dependence of the regression model parameters on a larger amount of training data. In view of the increasing usage of speech signals with higher bandwidth, and the dearth of training data for such signals, an augmentation of particular interest is that of a wide-band feature set with a narrow-band quality prediction. Experimental results for additive noise and non-linear distortions encountered in hearing aids, using quality labels from an intrusive quality predictor, illustrate the performance enhancement capabilities of the proposed approach.

关键词： Training data Niobium Data models Uncertainty Quality assessment Training Predictive models

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