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Adaptive image compression using sparse dictionaries

Adaptive image compression using sparse dictionaries

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Inbal Horev Ori Bryt Ron Rubinstein Department of Electrical Engineering Signal and Image Processing Laboratory Haifa Israel Department of Computer Science Geometric Image Processing Laboratory Haifa Israel

Transform coding is a widely used image compression technique, where entropy reduction can be achieved by decomposing the image over a dictionary which provides compaction. Existing algorithms, such as JPEG and JPEG2000, utilize fixed dictionaries which are shared by the encoder and decoder. Recently, works utilizing content-specific dictionaries show promising results by focusing on specific classes of images and using highly specialized dictionaries. However, such approaches lose the ability to compress arbitrary images. In this paper we propose an input-adaptive compression approach, which encodes each input image over a dictionary specifically trained for it. The scheme is based on the sparse dictionary structure, whose compact representation allows relatively low-cost transmission of the dictionary along with the compressed data. In this way, the process achieves both adaptivity and generality. Our results show that although this method involves transmitting the dictionary, it remains competitive with the JPEG and JPEG2000 algorithms.

关键词： Dictionaries image coding Transform coding PSNR Sparse matrices Encoding Bit rate

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A design method for Malaysian car plates character segmentation based on skew correction

A design method for Malaysian car plates character segmentat...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Jinn-Li Tan S. A. R Abu-Bakar Computer Vision Video and Image Processing Department of Microelectronics and Computer Engineering Faculty of Electrical Engineering Universiti Teknologi Malaysia Malaysia

This paper presents a license plate character segmentation method in the context of Malaysian cars. First of all, pre-processing steps will enhance the image before Laplacian pyramid takes place. For a proper binarization, Laplacian pyramid which up-sampled the image from an image lower in the pyramid when the image is captured under low resolution. By using Sobel edge detector and then median filtering, circumscribe rectangle of minimum area is formed and the angle is calculated. At the same time, the area of characters is focussed. The characters are then selected based on connected component analysis after applying Niblack's threshold. Our goal is to segment the characters properly from the steps mentioned. Therefore, our algorithm tries to find the best point to segment the characters using little prior knowledge. Experimental shows promising results on the flexibility of the proposed design method.

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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 ...

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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...

引用

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...

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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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Large Pose Invariant Face Recognition Using Feature-based Recurrent Neural Network

Large Pose Invariant Face Recognition Using Feature-based Re...

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International Joint Conference on Neural Networks

作者： Teddy Salan Khan M. Iftekharuddin Intelligent Systems and Image Processing Lab Department of Electrical and Computer Engineering University of Memphis Vision Lab Department of Electrical and Computer Engineering Old Dominion University

ISBN: (纸本)9781467314886

Cellular Simultaneous Recurrent Network (CSRN) is a novel bio-inspired recurrent neural network that mimics reinforcement learning in the brain. CSRN has been proven to be a powerful tool for learning and predicting temporal information in face image sequences. In this work, we propose a novel implementation of feature-based CSRN for large-scale pose invariant face recognition. We also report systematic evaluation and performance comparison of our feature-based CSRN method with other well-known standard algorithms (PCA, LDA, Bayesian Classifier and EBGM) using face recognition technology standards for large-scale pose invariant face recognition.

关键词： Large pose invariant face recognition Cellular Simultaneous Neural Network Feature extraction Motion unit Face Recognition Vendor Test Face Recognition Technology (FERET) program

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

Varying pose face recognition using combination of discrete ...

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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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Human action concentric video retrieval system using features weight updating method as relevance feedback

Human action concentric video retrieval system using feature...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Munaf Rashid S. A. R. Abu-Bakar Musa Mokji Aliyu Abdu Micro Electronics and Computer Engineering Department Computer Vision Videos and image processing Laboratory (CVVIP) Universiti Teknologi Malaysia Johor Bahru Malaysia

Retrieving videos based on its contents is becoming an increasingly popular area of research, because of enormous growth in the availability of multimedia information on public databases like Google and YouTube. Usually videos contain large variety of data but majority of online videos contain human as a subject of interest. In this paper, a human action based video retrieval system is presented which can be used to retrieve videos based on the contents of the query. The proposed system can search videos containing particular action on large databases efficiently. Furthermore, it is also shown that by using features weight updating approach as a Relevance feedback (RF), it is possible to involve user concepts interactively so that complex human action queries can be searched quickly to achieve useful results. Three popular Human action datasets namely Weizmann, KTH and UCF (sports) have been utilized in order to validate the performance of the proposed system. Experimental results and simulations show the efficacy of the proposed system. Even with number of visual challenges proposed approach will manage to get better accuracy as compare to other existing methods.

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2D PCA-based shape prior for level sets segmentation framework of the vertebral body

2D PCA-based shape prior for level sets segmentation framewo...

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Cairo International Biomedical engineering Conference, CIBEC

作者： Ahmed Shalaby Melih Aslan Hossam Abdelmunim Aly A. Farag Computer Vision and Image Processing Laboratory University of Louisville KY USA Computer & Systems Engineering Department Ain Shams University Cairo Egypt

In this paper, a novel statistical shape modeling method is developed for the vertebral body (VB) segmentation framework. Two-dimensional principle component analysis (2D-PCA) technique is exploited to extract the shape prior. The obtained shape prior is then embedded into the image domain to develop a new shape-based segmentation approach. Our framework consists of four main steps: i) shape model construction using 2D-PCA, ii) Detection of the VB region using the Matched filter, iii) Initial segmentation using the graph cuts which integrates the intensity and spatial interaction models, and iv) Registration of the shape prior and initially segmented region to obtain the final segmentation. The proposed method is validated on a Phantom as well as clinical CT images with various Gaussian noise levels. The experimental results show that the noise immunity and the segmentation accuracy of 2D-PCA based approach are much higher than conventional PCA approach.

关键词： Shape image segmentation Training Principal component analysis Accuracy Computed tomography Vectors

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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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