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Frequency-DOA Joint Estimation by Ant Colony Optimization

Frequency-DOA Joint Estimation by Ant Colony Optimization

The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

作者： Yong Jin Yunshan Hou Min Jiang Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475004 China College of Marine Northwestern Polytechnical University Xi'an 710072 China

ISBN: (纸本)9781424472352

The Multiple Signal Classification (MUSIC) method is a typical method for high-resolution Direction Of Arrival(DOA) and frequency estimation. Usually it performs spectrum search in certain grid space, which inevitably leads to high computational cost in the muti-dimensional case, for example the search for frequency and azimuth at the same time. To overcome this problem, in this paper, we introduced Ant Colony Optimization(ACO) to work with MUSIC. A new kind of ACO for continuous domain featured by Gauss kernel function is used to sample the MUSIC spectrum, which is regarded as the fitness function in the process. The resulted estimator is called Ant Colony Optimization based MUSIC (ACO-MUSIC). Simulations show that ACO-MUSIC not only reduces the computational complexity greatly but also maintains the excellent performance of the original MUSIC estimator.

关键词： direction of arrival multiple signal classification ant colony optimization computational complexity

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Motion blur detection based on lowest directional high-frequency energy

Motion blur detection based on lowest directional high-frequ...

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IEEE International Conference on image processing

作者： Xiaogang Chen Jie Yang Qiang Wu Jiajia Zhao Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Centre of Innovation in IT Services and Applications Sydney University of Technology Sydney Australia

Motion blur detection and the relevant blurring parameter estimation are important for many computer vision tasks. The contribution of this paper is in two folds. First, we propose a closed-form solution for motion direction estimation on blurred image. Secondly, a novel method is proposed for motion blurred region detection. The proposed direction estimation is based on measurement of lowest directional high-frequency energy. Compared with traditional methods, it will improve accuracy with less computational cost. Moreover, the proposed motion blurred region detection can efficiently estimate blurred regions without Point Spread Function estimation. Encouraging results are shown by experiments.

关键词： Estimation Pixel image segmentation image restoration Transforms Deconvolution Closed-form solution

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Action recognition by Multiple Features and Hyper-Sphere Multi-class SVM

Action Recognition by Multiple Features and Hyper-Sphere Mul...

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International Conference on pattern recognition

作者： Jia Liu Jie Yang Yi Zhang Xiangjian He Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Faculty of Engineering and Information Technology Sydney University of Technology Sydney Australia

ISBN: (纸本)9781424475421

In this paper we propose a novel framework for action recognition based on multiple features for improve action recognition in videos. The fusion of multiple features is important for recognizing actions as often a single feature based representation is not enough to capture the imaging variations (view-point, illumination etc.) and attributes of individuals (size, age, gender etc.). Hence, we use two kinds of features: i) a quantized vocabulary of local spatio-temporal (ST) volumes (cuboids and 2-D SIFT), and ii) the higher-order statistical models of interest points, which aims to capture the global information of the actor. We construct video representation in terms of local space-time features and global features and integrate such representations with hyper-sphere multi-class SVM. Experiments on publicly available datasets show that our proposed approach is effective. An additional experiment shows that using both local and global features provides a richer representation of human action when compared to the use of a single feature type.

关键词： Support vector machines Humans Videos Feature extraction Clouds image recognition pattern recognition

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A Novel Robust Watermarking Method for Certificates Based on DFT and Hough Transforms

A Novel Robust Watermarking Method for Certificates Based on...

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International Conference on Intelligent Information Hiding and Multimedia Signal processing (IIHMSP)

作者： Jiancheng Zou Xin Yang Shaozhang Niu Institute of Image Processing and Pattern Recognition North-China University of Technology Beijing China School of Computer Beijing University of Posts and Telecommunications Beijing China

Based on the discrete Fourier transformation (DFT) and Hough transforms, a novel digital watermarking method is proposed. The experiment results show that the algorithm is more robust than the traditional watermark algorithm. The proposed algorithm can endure severe attacks such as printing-scanning, very high loss in its data or data packets, scaling and rotating. The most advantage of the algorithm presented in this paper is that it is robust for the first time of print and scan, but fragile for the second time of print and scan. So this method can be used in the anti counterfeit of certificates.

关键词： Watermarking Discrete Fourier transforms Robustness Signal processing algorithms Pixel Binary codes

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Feature data optimization with LVQ technique in semantic image annotation

Feature data optimization with LVQ technique in semantic ima...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Ziheng Jiang Jing He Ping Guo School of Computer Science and Technology Beijing Institute of Technology Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China

In order to improve the classifier performance in semantic image annotation, we propose a novel method which adopts learning vector quantization (LVQ) technique to optimize low level feature data extracted from given image. Some representative vectors are selected with LVQ to train support vector machine (SVM) classifier instead of using all feature data. Performance is compared between the methods with and without feature data optimization when SVM is applied to semantic image annotation. Experiment results show that the proposed method has a better performance than that without using LVQ technique.

关键词： Feature extraction Training Semantics Support vector machine classification Testing Neurons

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Kernel ICA applied to feature extraction for image annotation

Kernel ICA applied to feature extraction for image annotatio...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China The School of Computer Science and Technology Beijing Institute of Technology Beijing China

In automatic image annotation, it is often extracting low-level visual features from original image for the purpose of mapping to high level image semantic information. In this paper, we propose a novel method which integrates kernel independent component analysis (KICA) and support vector machine (SVM) for analyzing the semantic information of natural images. KICA, which contains a nonlinear kernel mapping component, is adopted to extract low-level features from the original image data. Then these feature vectors are mapped to high-level semantic words using SVM to annotate images with labels in a given semantic label set. Comparative studies have done for the performance of KICA with traditional color histogram and discrete cosine transform features. The experimental results show that the proposed method is capable of extracting the components of images as key features, and with these features to map into semantic categories, higher accuracy is achieved.

关键词： Feature extraction image color analysis Support vector machines Discrete cosine transforms Histograms Semantics Kernel

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Data Classification on Multiple Manifolds

Data Classification on Multiple Manifolds

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International Conference on pattern recognition

作者： Rui Xiao Qijun Zhao David Zhang Pengfei Shi Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China Department of Computer Science Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)9781424475421

Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside on different manifolds of possible different dimensions. Therefore, better classification accuracy would be achieved by modeling the data by multiple manifolds each corresponding to a class. To this end, a general framework for data classification on multiple manifolds is presented. The manifolds are firstly learned for each class separately, and a stochastic optimization algorithm is then employed to get the near optimal dimensionality of each manifold from the classification viewpoint. Then, classification is performed under a newly defined minimum reconstruction error based classifier. Our method could be easily extended by involving various manifold learning methods and searching strategies. Experiments on both synthetic data and databases of facial expression images show the effectiveness of the proposed multiple manifold based approach.

关键词： Manifolds Classification algorithms Accuracy Databases Training Tuning image reconstruction

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Modeling multi-source remote sensing image classifier based on the MDL principle: Experimental studies

Modeling multi-source remote sensing image classifier based ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huaiying Xia Rukun Hu Bingxin Xu Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

In classification of multi-source remote sensing image, it is usually difficult to obtain higher classification accuracy. In the previous work, the modeling technique for the remote sensing image classification based on the minimum description length (MDL) principle with mixture model is analyzed theoretically. In this work, experimental studies are performed for investigating the modeling technique. With intensive experiments and sophisticated analysis, it is found that the developed modeling technique can build a robust classification system, which can avoid classifier over-fitting training data and make the learning process trade-off between bias and variance. Meanwhile, designed mixture model is more efficient to represent real multi-source remote sensing images compared to single model.

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Spatial localization of concurrent multiple sound sources

Spatial localization of concurrent multiple sound sources

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2010 2nd International Symposium on Aware Computing

作者： Huakang Li Akira Saji Keita Tanno Jun Ma Jie Huang Qunfei Zhao School of Computer Science and Engineering University of Aizu Aizu-Wakamatsu Japan Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China

Based on the human auditory system for spatial localization theory, we proposed a spatial localization of multiple sound sources using a spherical robot head. Space sound vectors recorded by a microphone array with spatial configuration, are used to estimate the histograms of spatial arrival time difference vectors by solving the simultaneous equations in different frequency bands. The echo avoidance model based on precedence effect is used to reduce the interference of environment reverberations which provide the strong interference for phase vectors especially in small indoor environments. To integrate spatial cues of different microphone pairs, we propose a mapping method from the correlation between different microphone pairs to a 3D map corresponding to azimuth and elevation of sound sources directions. Experiments indicate that the system provides the distribution of sound source in azimuth-elevation localization, even concurrently in reverberant environments.

关键词： Microphones Humans

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DSA image registration based on 3D space-time detection

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Procedia Engineering 2010年 7卷 426-431页

作者： Xinhong Zhang Fan Zhang Rui Li Computing Center Henan University Kaifeng 475001 China College of Computer and Information Engineering Henan University Kaifeng 475001 China Institute of Image Processing and Pattern Recognition Henan University Kaifeng 475001 China

An image registration algorithm of digital subtraction angiography (DSA) is proposed based on 3D space-time detection. The DSA image sequence is considered as a 3D space-time sequence. In DSA image sequence, the movement of image points is detected for the control points selection and image registration. If the control points allocate in the blood vessels, their gray value will change obviously in the 3D space-time sequence. According to the location of control points, 3D space-time characteristics are used to select control points. Experimental results show that proposed scheme has a good performance in DSA image registration.

关键词： Digital subtraction angiography image registration 3D space-time detection

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