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

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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...

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

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

引用

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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recognition of Active Deception Jamming for LFM Radar Based on Hyperdimensional Computing 3

Recognition of Active Deception Jamming for LFM Radar Based ...

引用

2022 3rd International Conference on Electrical, Electronic Information and Communication Engineering, EEICE 2022

作者： Dong, Hao Wang, Yan Hu, Wen Key Laboratory of Radar Imaging and Microwave Photonics Ministry of Education Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Hangkong University Nanchang China

Active deception jamming is one of the common means to jam radar signals. How to effectively recognize active deception jamming is a challenge of modern radar technology. To address the accuracy and real-time of radar active jamming recognition in practical applications, we propose a fast recognition method of radar active deception jamming based on hyperdimensional computing. First, to address the high feature dimensions, we use the sparse representation of time-frequency diagrams as the input, which time-frequency diagrams are constructed by the jamming plus echo signals in multiple consecutive pulse periods. Second, to achieve a high accuracy and real time of recognition, we employ the hyperdimensional computing to map the input to hyperdimensional space for training and recognition. Last, in the recognition stage, the Hamming distance is adopted to measure the similarity of samples. We validate our method on nine types of radar active deception jamming. The experimental results show that compared with the existing mainstream machine learning-based methods, the proposed method greatly shortens the training time on the premise of maintaining a high recognition accuracy. © Published under licence by IOP Publishing Ltd.

关键词： Hamming distance

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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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Residual Error based Approach to Classification of Multisource Remote Sensing images

Residual Error based Approach to Classification of Multisour...

引用

International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Dongdong Cao Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Classification of multisource remote sensing images has been studied for decades, and many methods have been proposed. Most of these studies focus on how to improve the classifiers in order to obtain higher classification accuracy. However, as we know, even if the most promising neural network method, its good performance not only depends on the classifier itself, but also has relation to the training pattern (i.e. features). On consideration of this aspect, we propose an approach to feature selection and classification of multisource remote sensing image based on residual error in this paper. In particular, a feature-selection scheme approach is proposed, which is to select effective subsets of features as inputs of a classifier by taking into account the residual error associated with each land-cover class. In addition, a classification technique base on selected features by using a feedforward neural network is investigated. The results of experiments carried out on a multisource data set confirm the validity of the proposed approach

关键词： Remote sensing Neural networks Remote monitoring Earth Feedforward neural networks Computer errors Classification algorithms Bayesian methods Robustness image processing

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Real time implementation of an MRF-based motion detection algorithm on a DSP board

Real time implementation of an MRF-based motion detection al...

引用

IEEE Digital Signal processing Workshop

作者： C. Dumontier F. Luthon J.P. Charras Image Processing and Pattern Recognition Laboratory National Polytechnical Institute Grenoble France

ISBN: (纸本)0780336291

The main concern in image processing is computation cost. Markov random fields (MRF) based algorithms particularly require a significant computation cost. Most of implementations of this kind of algorithms are made on parallel machines. This paper investigates an original solution for real time implementation of a robust MRF-based motion detection algorithm. A PC board, based on a pipeline architecture using a single powerfull DSP and FPGA components, is developed. The algorithm and the board are described. A processing rate of 15 images per second is achieved, showing the validity of this approach.

关键词： Motion detection Computational efficiency image processing Markov random fields Parallel machines Robustness Pipelines Computer architecture Digital signal processing Field programmable gate arrays

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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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