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检索条件"机构=Image Processing Pattern Recognition Laboratory"

共 516 条记录，以下是141-150 订阅

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ECoG Analysis with Affinity Propagation Algorithm

ECoG Analysis with Affinity Propagation Algorithm

引用

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...

引用

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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Fast Multiple Scattering in Participating Media with Beamlet Decomposition

Fast Multiple Scattering in Participating Media with Beamlet...

引用

International Conference on Computational Intelligence and Security

作者： Yuanlong Wang Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

We propose a fast algorithm which is based on the beam let decomposition for real-time rendering of scenes in participating media with multiple scattering. Firstly, the light source radiation is considered as composed by all particles in the media and each particle radiation is decomposed along different forward directions using the plane decomposition method. Then the multiple scattering radiation of one particle is calculated by the decomposition radiations from its adjacent particles and the light source. Finally, according to the multiple scattering radiation value of each particle, the radiation of the ray which is from viewpoint is calculated using ray marching method, which can be implemented on the graphics processing unit (GPU), and rendering process is highly parallel. The experimental results show that the algorithm can achieve real-time rendering efficiency and enhance the practicality of multiple scattering.

关键词： Scattering Media Rendering (computer graphics) Light sources Absorption Algorithm design and analysis Graphics processing units

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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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Combining the contrast information with LPQ for texture classification

Combining the contrast information with LPQ for texture clas...

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International Conference on Sciences of Electronics, Technologies of Information and Telecommunications (SETIT)

作者： Hussain Dawood Hassan Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Texture classification is an important problem in image analysis. A considerable amount of research work has been done for local or global rotation invariant feature extraction for texture classification. Local invariant features contain the spatial information, but usually do not have the contrast information. A new hybrid approach is proposed which considers the contrast information in spatial domain and the phase information in frequency domain of the image. It uses the joint histogram of the two complementary features, local phase quantization (LPQ) and the contrast of the image. Support vector machine is used for classification. The experimental results on standard benchmark datasets for texture classification Brodatz and KTH-TIPS2-a show that the proposed method can achieve significant improvement compared to the LPQ, Gabor filer or local Binary pattern methods.

关键词： Histograms Quantization (signal) Support vector machines Transforms Feature extraction Standards Gabor filters

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Scene recognition via Combining Information of Neighbors

Scene Recognition via Combining Information of Neighbors

引用

International Conference on Computational Intelligence and Security

作者： Minguang Song Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Recently, spatial principal component analysis of census transform histograms (PACT) was proposed to recognize instance and categories of places or scenes in an image. When combining PACT with Local difference Magnitude Binary pattern (LMBP), a new representation called Local Difference Binary pattern (LDBP) was proposed and performed better. LDBP is based on the comparisons between center pixel and its neighboring pixels. However, the relationship among neighbor pixels is not considered. In this paper we proposed Local Neighbor Binary pattern (LNBP) to utilize the relationship among neighboring pixels. LNBP provides complementary information regarding neighboring pixels for LDBP. We propose to combine LDBP with LNBP, and used a spatial representation for scene recognition. Experiments on two widely used dataset demonstrate the proposed method can improve the performance of recognition.

关键词： Histograms pattern recognition Transforms Computer vision Principal component analysis Conferences image recognition

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Comparison of linear dimensionality reduction methods in image annotation

Comparison of linear dimensionality reduction methods in ima...

引用

International Workshop on Advanced Computational Intelligence (IWACI)

作者： Shiqiang Li Hussain Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Dimension reduction methods are often used to analyzing high dimensional data, linear dimension methods are commonly used due to their simple geometric interpretations and for effective computational cost. Dimension reduction plays an important role for feature selection. In this paper, we have given a detailed comparison of state-of-the-art linear dimension reduction methods like principal component analysis (PCA), random projections (RP), and locality preserving projections (LPP). We have determined which dimension reduction method performs better under the FastTag image annotation framework. Experiments are conducted on three standard bench mark image datasets such as CorelSk, IAPRTC-12 and ESP game to compare the efficiency, effectiveness and also memory usage. A detailed comparison among the aforementioned dimension reduction method is given.

关键词： Principal component analysis Benchmark testing Kernel Random access memory Customer relationship management

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Removal of random-valued impulse noise by Khalimsky grid

Removal of random-valued impulse noise by Khalimsky grid

引用

Asia Pacific Conference on Multimedia and Broadcasting (APMediaCast)

作者： Hussain Dawood Hassan Dawood Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

In this paper, based on Khalimsky grid, a new Random-valued Impulse noise identification and removal method is proposed. Khalimsky grid can presents the neighborhood relationship among the pixels in the sliding window, effectively. The local statistics of Khalimsky grid is used to define an adaptive threshold range to identify the central pixel in current sliding window as noisy or noise free in an iterative way. The identified noisy pixel is replaced by local statistics of propose vertical direction based noise removal method. The performance of the propose method is evaluated on different test images and compared with state-of-the-art methods. Experimental results show that the propose method can identify the impulse noise, as well as can preserve the detailed information of an image, efficiently.

关键词： Noise Noise measurement Tin Multimedia communication Filtering algorithms Asia Digital multimedia broadcasting

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Cattle's fur detection in complex background based on Graph Cuts

Cattle's fur detection in complex background based on Graph ...

引用

International Conference on Advanced Computer Science and Information System (ICACSIS)

作者： Hisyam Fahmi Ary Noviyanto Aniati Murni Arymurthy Image Processing and Pattern Recognition Laboratory Faculty of Computer Science Universitas Indonesia

Segmentation becomes a difficult task if the objects are not homogeneous and have overlapping characteristics. The Graph Cuts methods combined with Gaussian Mixture Model (GMM) for initialization label has been adopted to detect cattle object in an image with complex background. The RGB colors and Gray Level Co-occurrence Matrix (GLCM) textures are used as the features set. This method can robustly segment the cattle beef image from its background. This segmentation method produces the average of accuracy value up to 90%.

关键词： image segmentation Cows Accuracy image color analysis Equations Mathematical model Gaussian mixture model

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Complete two-dimensional principal component analysis for image registration

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

引用

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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