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Efficient word-level sequential coding scheme of bit plane coding for EBCOT in JPEG2000

Efficient word-level sequential coding scheme of bit plane c...

引用

9th International Symposium on Consumer Electronics 2005, ISCE 2005

作者： Xiong, Cheng-Yi Tian, Jin-Wen Liu, Jian Institute of Pattern Recognition and Artificial Intelligence South-Center University for Nationalities Huazhong University of Science and Technology Wuhan China Key Laboratory of Education Ministry for Image Processing and Intelligent Control Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China HUST

The embedded block coding with optimized truncation (EBCOT) is the state-of-the-art coding technique for image compression, which is the heart of the latest still image compression standard JPEG2000. EBCOT can be partitioned into three steps: bit plane coding (BPC), context adaptive binary arithmetic coding (CABAC) and postcompression rate-distortion (PCRD) optimization. The bit plane coding in JPEG2000 is a fractional bit-plane coding method, in which the transformed coefficients of a code block are coded bit plane by bit plane with 3 passes in each bit-plane, and context-decision (CX-D) pair of the coded symbol is generated for CABAC. In this paper, a new word-level sequential (WLS) coding scheme for BPC is presented, which makes all the passes coding be completed in one scan, with the context formation in all bit-planes being performed in parallel and all the samples being coded in sequential. The status variables used in context formation are produced on-the-fly. This new algorithm can efficiently implement both the regular mode and vertical causal one recommended by JPEG2000. The proposed algorithm reduces efficiently the size of memory and the amount of memory accessing, and increases significantly the speed of coding. It is well suited for both software and hardware implementation.1 © IEEE.

关键词： image coding

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Object tracking within the framework of concept drift

Object tracking within the framework of concept drift

引用

11th Asian Conference on Computer Vision, ACCV 2012

作者： Chen, Li Zhou, Yue Yang, Jie Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9783642374302

It is well known that the backgrounds or the targets always change in real scenes, which weakens the effectiveness of classical tracking algorithms because of frequent model mismatches. In this paper, an object tracking algorithm within the framework of concept drift is proposed to solve this problem. We detect the driftpoints using a simple message-passing algorithm based on Bayesian Approach. The analyzed probability distribution lays the foundation for the self-adaption of our new model. Our tracking algorithm within the framework of concept drift improves the tracking robustness and accuracy which is illustrated by the two experiments on two real-world changing scenes. © 2013 Springer-Verlag.

关键词： Probability distributions

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Multi-Level Kernel Machine for Scene image Classification

Multi-Level Kernel Machine for Scene Image Classification

引用

International Conference on Computational Intelligence and Security

作者： Junlin Hu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

Recently, a new representation for recognizing instances and categories of scenes called spatial Principal component analysis of Census Transform histograms (PACT) has shown its excellent performance in the scene image classification task. PACT captures local structures of an image through the Census Transform (CT), meanwhile, large scale structures are captured by the strong correlation between neighboring CT values and the histogram. However, the original spatial PACT only simply concatenates all levels compact histograms together, and discards the difference between various levels. In order to improve this problem, we propose a multi-level kernel machine method, which computes a set of base kernels at each level of pyramid of PACT, and finds optimal weights for best fusing all these base kernels for scene recognition. Experiments on two popular benchmark datasets demonstrate that our proposed multi-level kernel machine method outperforms the spatial PACT on scene recognition. Besides, our method is easy to be implemented comparing with spatial PACT.

关键词： Kernel Computed tomography Transforms Computer vision Histograms Conferences Support vector machines

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image fusion algorithm using nonsubsampled contourlet transform

Image fusion algorithm using nonsubsampled contourlet transf...

引用

MIPPR 2007: Multispectral image processing

作者： Yang, Xiao Zhiguo, Cao Kai, Wang Zhengxiang, Xu Institute of Pattern Recognition and Artificial Intelligence State Education Commission Key Laboratory for Image Processing and Intelligent Control Huazhong Uni. of Sci. and Tech. Wuhan 430074 China

ISBN: (纸本)9780819469519

In this paper, a pixel-level image fusion algorithm based on Nonsubsampled Contourlet Transform (NSCT) has been proposed. Compared with Contourlet Transform, NSCT is redundant, shift-invariant and more suitable for image fusion. Each image from different sensors could be decomposed into a low frequency image and a series of high frequency images of different directions by multi-sacle NSCT. For low and high frequency images, they are fused based on local-contrast enhancement and definition respectively. Finally, fused image is reconstructed from low and high frequency fused images. Experiment demonstrates that NSCT could preserve edge significantly and the fusion rule based on region segmentation performances well in local-contrast enhancement.

关键词： image fusion

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Edge detection using image feature detector

Edge detection using image feature detector

引用

International Conference on Signal processing Proceedings (ICSP)

作者： Yang Xuan Liang Dequn Yang Haijun Laboratory of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China

A new edge detection operator based on image features is proposed, which analyzes edges in images for edge features in two dimensions. The local extreme of the operator is created at the edge location and a low value is created at the smooth region. Edges can be located by obtaining the local extreme and a threshold of the operator response. The detection operator is shown to be better than the Canny operator in terms of signal-to-noise ratio and edge location accuracy.

关键词： image edge detection Computer vision Detectors image analysis Laboratories image processing pattern recognition pattern analysis Signal to noise ratio Filters

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Parallelization and Optimization of Molecular Dynamics Simulation on Many Integrated Core

Parallelization and Optimization of Molecular Dynamics Simul...

引用

International Conference on Computational Intelligence and Security

作者： Qian Yin Ruiyi Luo Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Molecular dynamics (MD) simulations are useful in various areas. In this paper, we parallelize and optimize the grid-based MD algorithm on Many Integrated Core (MIC) Architecture. To get full play of the hardware and accelerate computation of MD simulation, we design the parallel structure using multi-threads with OpenMP. Also, various or method such as Array Notification, intrinsic and so on are used to vectorize the application according to the character of MIC for a higher performance. Due that multi-core is also a trendy of CPU and High Performance Computing, our method can be followed by other similar applications and provide a more choice.

关键词： Microwave integrated circuits Instruction sets Force Graphics processing units Algorithm design and analysis Arrays

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Impulse noise removal in two-dimensional electrophoresis images based on dome recognition 8

Impulse noise removal in two-dimensional electrophoresis ima...

引用

8th International Conference on BioMedical Engineering and Informatics, BMEI 2015

作者： Ou, Qiaofeng Zhang, Huisheng Li, Lixin Xiong, Bangshu School of Electronic Information Northwestern Polytechnical University Xi'an710072 Hong Kong Key Laboratory of Image Processing and Pattern Recognition of Jiangxi Province Nanchang Hangkong University Nanchang330063 China

ISBN: (纸本)9781509000227

Two-dimensional gel electrophoresis (2DE) images are often corrupted by impulse noise in broad sense (including various artifacts, such as fingerprints, hairs, gel cracks, strips, water stains, dust and so on). In this paper, we propose a novel adaptive directional filter (ADF) to remove impulse noise completely and accurately. Firstly, we detect the impulse noise based on the observation that the directional derivative distributions around the protein spot centers and noise pixels are very different. Then, according to the bell-shaped three-dimensional appearance of protein spots, the greatest and smallest gradient directions were used to restore the noised pixels. Lastly, the pixels adjacent to the modified pixels are iteratively detected and restored until no impulse noise is detected. Experimental results on the synthetic and real 2DE images show that the proposed method performs much better than the previous approaches. © 2015 IEEE.

关键词： Pixels

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A Study of Deep Belief Network Based Chinese Speech Emotion recognition

A Study of Deep Belief Network Based Chinese Speech Emotion ...

引用

International Conference on Computational Intelligence and Security

作者： Bu Chen Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781479974351

This paper presents a deep learning method application to the extraction of emotions included in Chinese speech with a deep belief network (DBN) structure. Eight proper features such as pitch, mel frequency cepstrum coefficient (MFCC) are chosen from Mandarin speech used as network inputs, and a DBN classifier is used instead of traditional shallow learning methods to recognition of emotions. Experiment studies have proven that its recognition rate is higher than that of the traditional back propagation (BP) method and support vector machine (SVM) classifier.

关键词： Speech Feature extraction Speech recognition Emotion recognition Support vector machines Training Speech processing

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Building Extraction Method in Remote Sensing image

Building Extraction Method in Remote Sensing Image

引用

International Conference on Data processing Techniques and Applications for Cyber-Physical Systems, DPTA 2019

作者： Han, Qinzhe Zheng, Xin Yin, Qian Chen, Ziyi Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China College of Information Science and Technology University parkPA United States Beijing Normal University Beijing China

ISBN: (纸本)9789811514678

Identifying buildings in disaster areas quickly and conveniently plays an important role in post-disaster reconstruction and disaster assessment. Aiming at the technical requirements of earthquake relief projects, this paper proposed a building extraction method in remote sensing images by combining traditional digital image processing methods and convolution neural network. First, In the pretreatment process, remote sensing images are transformed from RGB space to HSV space. Then, shadows in the images are weakened by using the Retinex model de-shadowing algorithm. Finally, buildings in the image are classified and recognized by using a convolution neural network. Experiments prove that compared with the fast R-CNN algorithm, the detection accuracy is improved. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Complete Two-Dimensional PCA for Face recognition

Complete Two-Dimensional PCA for Face Recognition

引用

International Conference on pattern recognition

作者： Anbang Xu Xin Jin Yugang Jiang Ping Guo Image Processing & Pattern Recognition Laboratory Beijing Normal University Beijing China

We propose a novel method, the complete two-dimensional principal component analysis (complete 2DPCA), for image features extraction. Compared to the original 2DPCA, complete 2DPCA not only gain a higher recognition rate, but also reduce the feature coefficients needed for face recognition. Complete 2DPCA is based on 2D image matrices. Two image covariance matrices are constructed directly using the original image matrix and theirs eigenvectors are derived for image feature extraction. Our experiments were performed on ORL face database, and experimental results show that the proposed method has an encouraging performance

关键词： Principal component analysis Face recognition Covariance matrix Independent component analysis Kernel Feature extraction pattern recognition Lighting image databases Scattering

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