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检索条件"机构=Key Laboratory of Image Processing and Pattern Recognition"

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Fast Multiple Scattering in Participating Media with Beamlet Decomposition

Fast Multiple Scattering in Participating Media with Beamlet...

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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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Object Detector based on Enhanced Multi-scale Feature Fusion Pyramid Network

Object Detector based on Enhanced Multi-scale Feature Fusion...

引用

IEEE Advanced Information Technology, Electronic and Automation Control Conference (IAEAC)

作者： Luan Zhao Xiaofeng Zhang Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

ISBN: (数字)9781728180281

ISBN: (纸本)9781728180298

Constructing the pyramidal architecture for the feature is currently a very effective way to obtain feature information of objects at different scales. Although the feature pyramid can realize the recognition and detection of multi-scale objects in the object detection task well, it still has some limitations. Since the feature information of different levels is often not from the same layer of the network, it is difficult to obtain the feature of different objects information at a certain scale from a certain level feature map of the pyramid network. To solve this problem, we present a novel object detection architecture, named Enhanced Multi-scale Feature Fusion Pyramid Network (EMFFPNet). Our network consists of Enhanced Multi-scale Feature Fusion Module (EMFFM) and Predictor Optimization Module (POM). In EMFFM, Features at different levels can be fused into the Enhanced features as outputs, which are more representative and deterministic. In order to enable the enhanced features to play their respective roles in the pyramid network, we assign different weights to fusion features of different levels in POM. We perform the experiments on the COCO detection benchmark. The experimental results indicate that the performance of our model is much better than the state-of-the-art model.

关键词： Object detection Predictive models Feature extraction Task analysis Information technology Optimization Standards

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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...

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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

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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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A fusion method for multispectral and panchromatic images based on HSI and Contourlet transformation

A fusion method for multispectral and panchromatic images ba...

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International Workshop on image Analysis for Multimedia Interactive Services, WIAMIS

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

Fusion of multispectral and panchromatic remote sensing images is a procedure to obtain spatial resolution and quality of the panchromatic image as well as preserving spectral information of the multispectral image. In this paper, we present a new fusion method based on HSI (Hue-Saturation-Intensity) and Contourlet transform. First, we convert the multispectral image from the RGB color space into the HSI color space. Then, by applying Contourlet transform to the panchromatic image and the I component of the multispectral image, we utilize an improved fusion rule based on PCA for the low-frequency sub-images, and engage the maximum fusion rule for the high-frequency sub-images. Finally, a fusion image is obtained by the inverse HSI transform. The experimental results show that the proposed fusion method not only enhances the spatial resolution of the fusion image, but also preserves the spectral information of the original multispectral image.

关键词： Spatial resolution Principal component analysis Multispectral imaging Wavelet transforms Remote sensing Frequency Discrete wavelet transforms image resolution Fuses Fourier transforms

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Software Fault Prediction Framework Based on aiNet Algorithm

Software Fault Prediction Framework Based on aiNet Algorithm

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

Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data from the modules that have been tested, then generated multi-centers for each network by Hierarchical Clustering. The proposed framework acquires information along with the testing process timely and adjusts the network generated by aiNet algorithm dynamically. Experimental results show that higher accuracy can be obtained by using the proposed framework.

关键词： Software Prediction algorithms Testing Software algorithms Measurement Clustering algorithms Accuracy

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Text-independent Speaker Identification Using Fisher Discrimination Dictionary Learning Method

Text-independent Speaker Identification Using Fisher Discrim...

引用

International Conference on Computational Intelligence and Security

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

In last decades, text-independent speaker recognition is a hot research topic attracted many researchers. In this paper, we proposed to apply the Fisher discrimination dictionary learning method to identify the text-independent speaker recognition. The feature used in classification is the Gaussian Mixture Model super vector. The proposed method is evaluated with public ally available dataset TIMIT. Experimental results show that the proposed method outperforms the Sparse Representation Classifier used for text-independent speaker recognition in both clean and noisy condition.

关键词： Dictionaries Mathematical model Equations Speaker recognition Noise measurement Learning systems pattern recognition

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