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Formal Photograph Compression Algorithm Based on Object Segmentation

International Journal of Automation and computing 2008年第3期5卷 276-283页

作者： Li Zhu Guo-You Wang Chen Wang Laboratory. for Biomedical Image Processing Institute for Pattern Recognition ＆ Artificial Intelligence Huazhong University of Science ＆ Technology Wuhan 430074 PRC School of Mechanical and Aeropace Engineering Nanyang Technological Vnlversity Sigapore 639798 Sigapore

Small storage space for photographs in formal documents is increasingly necessary in today＇s needs for huge amounts of data communication and storage. Traditional compression algorithms do not sufficiently utilize the distinctness of formal photographs. That is, the object is an image of the human head, and the background is in unicolor. Therefore, the compression is of low efficiency and the image after compression is still space-consuming. This paper presents an image compression algorithm based on object segmentation for practical high-efficiency applications. To achieve high coding efficiency, shape-adaptive discrete wavelet transforms are used to transformation arbitrarily shaped objects. The areas of the human head and its background are compressed separately to reduce the coding redundancy of the background. Two methods, lossless image contour coding based on differential chain, and modified set partitioning in hierarchical trees （SPIHT） algorithm of arbitrary shape, are discussed in detail. The results of experiments show that when bit per pixel （bpp）is equal to 0.078, peak signal-to-noise ratio （PSNR） of reconstructed photograph will exceed the standard of SPIHT by nearly 4dB.

关键词： image compression object segmentation lossless image contour coding differential chain set partitioning in hierarchical trees （SPIHT） coding of arbitrarily shaped object.

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ANT colony optimization algorithm for feature selection and classification of multispectral remote sensing image

ANT colony optimization algorithm for feature selection and ...

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2008 IEEE International Geoscience and Remote Sensing Symposium - Proceedings

作者： Wen, Lintao Yin, Qian Guo, Ping Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China School of Computer Science and Technology Beijing Institute of Technology Beijing 100081 China

ISBN: (纸本)9781424428083

In classification of multispectral remote sensing image, it is usually difficult to obtain higher classification accuracy if only consider image's spectral feature or texture feature alone. In this paper ,we presents a new approach by applying Ant Colony Optimization (ACO) algorithm to find a multi-feature vector composed of spectral and texture features in order to get a better result in the classification. The experimental results show that ACO algorithm is helpful in subset searching of the features used to multispectral remote sense image's classification. Using the combination of the spectral and texture features obtained by ACO in classification always get a better accuracy. ©2008 IEEE.

关键词： Ant colony optimization

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ECoG Analysis with Affinity Propagation Algorithm

ECoG Analysis with Affinity Propagation Algorithm

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International Conference on Natural Computation (ICNC)

作者： Yuan Yuan An-bang Xu Ping Guo Jia-cai Zhang Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Analyzing notor imagery electrocardiogram (ECoG) signal is very challenging for it is hard to set up a classifier based on the labeled ECoG obtained in the first session and apply it to the unlabeled test data obtained in the second session. Here we propose a new approach to analyze ECoG trails in the case of session-to-session transfer exists. In our approach, firstly, dimension reduction is performed with independent component analysis (ICA) decomposition. Secondly, ECoG trials are clustered by an unsupervised learning algorithm called affinity propagation. Primary experimental results show that the proposed approach gives the reasonable result than that using the classical K-means clustering algorithm.

关键词： Algorithm design and analysis Independent component analysis Clustering algorithms Feature extraction Electroencephalography pattern analysis Signal analysis image analysis pattern recognition Signal processing

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Dual-cache Structure Based Large Scale Texture Mapping for Real-time Terrain Rendering

Dual-cache Structure Based Large Scale Texture Mapping for R...

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IEEE Conference on Robotics, Automation and Mechatronics

作者： Dong Tian Xiaodong Wang Xin Zheng Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

There are two key problems in efficient large scale texture mapping for terrain rendering-efficient data organization and real time data updating in memory. In order to solve these problems, in this paper we propose a quadtree based indexing method to organize multi-resolution images and to fast retrieve data from disk; For memory updating, we present a real time dual-cache structure based updating method, which effectively reduces the frequency of data refresh. We also innovatively use a wavelet image enhancement algorithm to enhance original terrain texture, which obtain richer edge information and give us a more realistic effect in terrain rendering. Through the analysis of storage efficiency and rendering speed of our experiment, this dual-cache structure based method solves rendering speed and memory limit problems perfectly.

关键词： Large-scale systems Terrain mapping Rendering (computer graphics) Indexing Information retrieval image processing pattern recognition Frequency image enhancement image retrieval

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Complete two-dimensional principal component analysis for image registration

Complete two-dimensional principal component analysis for im...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Anbang Xu Xinyu Chen Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

We present a new feature extraction method, which called the complete two-dimensional principal component analysis (Complete 2DPCA), for image registration. Complete 2DPCA is based on 2D image matrices. Two image covariance matrices are constructed directly using the original image matrix and their eigenvectors are derived for image feature extraction. In the 2D image registration scheme, we propose complete 2DPCA to extract features from the image sets, and these features are input vectors of feedforward neural networks (FNN). Neural network outputs are registration parameters with respect to reference and observed image sets. Comparative experiments are performed between complete 2DPCA based method and other feature based methods. The results show that the proposed method has an encouraging performance.

关键词： Principal component analysis image registration Feature extraction Neural networks Covariance matrix Feedforward neural networks Intelligent robots Registers Discrete cosine transforms image processing

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Automatic needle segmentation using 3D Gray-level Hough Transform in 3D ultrasound images

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Journal of Computational Information Systems 2008年第5期4卷 2021-2026页

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Image Processing and Intelligence Control Key Lab. of Education Ministry of China Institute of Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

This paper describes a novel 3D needle segmentation algorithm for 3DUS data. The algorithm includes the 3D Gray-level Hough Transform (3DGHT), which is based on the representation (ψ, θ, ρ, α) of straight lines in 3D space and Local Dimensionality Estimator (GST). Different from the traditional Hough transformation the process of binarization is not needed, because the proposed algorithm adopts the original gray level image directly. We tested it with the synthetic data and water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1° and position deviation less than 1 mm. © 2008 Binary Information Press.

关键词： Hough transforms

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Large Scale Terrain Compression and Real-Time Rendering Based on Wavelet Transform

Large Scale Terrain Compression and Real-Time Rendering Base...

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International Conference on Computational Intelligence and Security

作者： Xiaodong Wang Xin Zheng Qian Yin Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781424441969

By taking advantage of 3 attributes of integer wavelet transform (IWT): efficient computing, multi-resolution and partially reconstruction, we propose one lossless large scale terrain compression method with high compression rate in this paper. By this way, we can decompress terrain data with multi-resolution efficiently at any viewpoint and render the terrain in real time by fast updating strategy. Experiments prove that by our method we can compress the data efficiently and render the terrain scene smoothly in real time.

关键词： Large-scale systems Wavelet transforms image coding Rendering (computer graphics) image reconstruction Discrete wavelet transforms image resolution Multiresolution analysis Virtual reality Computational intelligence

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New design for 9/7-tap wavelet filter and its VLSI implementation

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第3期31卷 411-418页

作者： Tian, Xin Tan, Yi-Hua Tian, Jin-Wen State Key Laboratory for Multi-Spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Wuhan 430074 China

This paper designs a new kind of lifting 9-7-tap wavelet filter with binary characteristics and presents a high speed VLSI structure for the wavelet filter. The coefficients of the lifting filters are binary numbers, so it can simplify the VLSI design. The test results for compression performances have shown that the new filter is good almost as CDF97 under PSNR. when the data has a finite accuracy, it may have a better performance than CDF97. The filter can be implemented with using adders and shifters. Therefore the hardware resources can be saved and the critical path can be shortened. According to using the folding technology and the retiming technology, the architecture of the design can be transformed into a kind of embedded folding architecture which leads to the parallel computation of addition operations. So the critical path can be shortened to nearly the time of a addition operation and the utilization of the hardware resources also has a good performance. Simulation results show that the max working frequency could almost reach 250 MHz which is the four times by the CDF97;The occupied logic cell is reduced by 66.7 compared with the CDF97+4 stages pipeline. So it is especially suitable for the high speed VLSI design.

关键词： VLSI circuits

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An Improved Random Sampling LDA for Face recognition

An Improved Random Sampling LDA for Face Recognition

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Congress on image and Signal processing, CISP

作者： Yunfei Jiang Xinyu Chen Ping Guo Hanqing Lu Laboratory of Image Processing and Pattern Recognition Beijing Normal University Beijing China National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

Linear Discriminant Analysis (LDA) is one of the most used feature extraction techniques for face recognition. However, it often suffers from the small sample size problem with high dimension setting. Random Subspace Method (RSM) is a popular combining technique to improve weak classifier. Nevertheless, it remains a problem how to construct an optimal random subspace for discriminant analysis. In this paper, we propose an improved random sampling LDA for face recognition. Firstly, AdaBoost is adopted to select Gabor feature and remove redundant information. Secondly, in the selected Gabor feature space, we combine principal component analysis and RSM approaches to construct optimal random subspaces for LDA. After that, direct LDA (D-LDA) and R-LDA is applied in each subspace, respectively. Final results are obtained by combining all the LDA classifiers using a fusion rule. Experiments with both the ORL and FERET face databases demonstrate the effectiveness of our proposed method, and it shows promising results compared with previous approaches.

关键词： Sampling methods Linear discriminant analysis Face recognition Principal component analysis pattern recognition Laboratories Feature extraction Scattering Signal processing image sampling

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Iris recognition using Steerable Pyramids

Iris Recognition using Steerable Pyramids

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Workshops on image processing Theory, Tools and Applications, IPTA

作者： Nefissa Khiari Hela Mahersia Kamel Hamrouni Laboratory of Signal Processing Image Processing and Pattern Recognition National Engineering School of Tunis University of Tunis Tunis Tunisia

This work presents a new iris recognition method based on steerable pyramid transform. This method consists of four steps: localization, normalization, features extraction and matching. After locating the iris boundaries by Hough Transform, normalization is operated by unwrapping the circular ring and isolating the noisy regions. Steerable pyramid filters are then used to capture orientation details from the iris texture. The features are extracted on each filtered sub-image to form a fixed length feature vector which will be compared to other vectors in the matching step. This technique has been tested on infrared light iris images. It has been compared, in both identification and verification modes, to known methods.

关键词： Iris recognition Feature extraction Wavelet transforms image processing Matched filters Gabor filters Filtering Biometrics Eyelids Eyelashes

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