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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

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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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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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Text-independent Speaker Identification Using Fisher Discrimination Dictionary Learning Method

Text-independent Speaker Identification Using Fisher Discrim...

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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...

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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...

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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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Sparse Representation for Multi-Label image Annotation

Sparse Representation for Multi-Label Image Annotation

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International Conference on Computational Intelligence and Security

作者： Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

ISBN: (纸本)9781457720086

image annotation is the process of assigning proper keywords to describe the content of a given image, which can be regarded as a problem of multi-object image classification. In this paper, a general multi-label annotation algorithm is proposed, which is based on sparse representation theory and employs a multi-level decision method to deal with the multi-object classification problem. The experimental results show that the proposed algorithm can provide more promising results compared with the traditional classification based image annotation methods.

关键词： Training Vectors Support vector machines Databases Feature extraction Roads Training data

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Classification Fusion in Wireless Sensor Networks

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自动化学报 2006年第6期32卷 947-955页

作者： LIU Chun-Ting HUO Hong FANG Tao LI De-Ren SHEN Xiao Institute of Image Processing ＆ Pattern Recognition Shanghai Jiaotong University Shanghai 200240 State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan 430079

In wireless sensor networks, target classification differs from that in centralized sensing systems because of the distributed detection, wireless communication and limited resources. We study the classification problem of moving vehicles in wireless sensor networks using acoustic signals emitted from vehicles. Three algorithms including wavelet decomposition, weighted k-nearest-neighbor and Dempster-Shafer theory are combined in this paper. Finally, we use real world experimental data to validate the classification methods. The result shows that wavelet based feature extraction method can extract stable features from acoustic signals. By fusion with Dempster's rule, the classification performance is improved.

关键词： Wireless sensor networks classification fusion wavelet decomposition weighted k-nearest-neighbor Dempster-Shafer theory

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A Hierarchical Model with Pseudoinverse Learning Algorithm Optimazation for Pulsar Candidate Selection

A Hierarchical Model with Pseudoinverse Learning Algorithm O...

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Congress on Evolutionary Computation

作者： Shijia Li Sibo Feng Ping Guo Qian Yin Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

Pulsars search has always been one of the most concerned problem in the field of astronomy. Nowadays, with the development of astronomical instruments and observation technology, the amount of data is getting bigger and bigger. Radio pulsar surveys have generated and will generate vast amounts of data. To handle big data, developing new technologies and frameworks to efficiently and accurately analyze these data become increasing urgent. The number of positive and negative samples in pulsar candidate data set is very unbalanced, if we only use these a few positive samples to train a deep neural network (DNN), the trained DNN is prone because of the problem of overfitting and will affect the generalization ability. Motivated by the mixtures of experts network architecture, we proposed a hierarchical model for pulsar candidate selection which assembles a set of trained base classifiers. Moreover, training a neural network always takes a lot of time because of using gradient descent (GD) based algorithm. In this work, we utilize the pseudoinverse learning algorithm instead of GD based algorithm to train proposed model. With the designed network architecture and adopted training algorithm, our model has the advantages not only with high steady-state precision but also good generalization performance.

关键词： Matrix decomposition Training Big Data Neural networks Task analysis Decoding Machine learning

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