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International Conference on Machine Learning and Applications (ICMLA)

作者： Yunfei Wang Zhaoxiang Zhang Yunhong Wang Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing China

We address the problem of moving object detection in aerial video. Moving object detection in aerial video is still a challenging problem for the reason that when capturing the video the camera (or the platform) is moving all the time. As a result, the problem is detecting moving object from moving background which is much more difficult than the case that the background is constant. To this end, a novel approach is proposed in this paper. Moving object detection in stationary scene usually modeling the pixel value changes over time, but in aerial video the change does not have regular patterns. Therefore, we model the motion of the background rather than modeling the background directly. The optical flow between every two adjacent frames is computed first to get the motion information for each pixel. Based on this, we define a notion named ``pixel motion process" which means the motion changes (the optical flow value changes) of a particular pixel over time, and transfer the Gaussian mixture model framework used for modeling background in the stationary scene to model the background motion. The result is an accurate, adaptive and general background motion model which is used to detect foreground moving objects. Experimental results demonstrate the effectiveness of our approach.

关键词： Adaptation models Cameras Mathematical model Computational modeling Computer vision Object detection Optical imaging

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Detection of Billet Character in Complex Production Line Scene

Detection of Billet Character in Complex Production Line Sce...

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International Conference on Digital Manufacturing and Automation (ICDMA)

作者： Hong Hanyu Yu Zhejun Zhang Xiuhua Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan China Laboratory for Image Processing and Intelligent Control Wuhan Institute of Technology Wuhan China

Character information is hard to detect in billet scene images by CCD camera. In this paper, we present a method for detection of billet characters from measurements of recursive segmented image. This recursive segmented method can be used in a wide variety of billet scenes. According to high temperature and complex scene in the rolling line, we use an effective clustering and projection characteristics to determine the terminal condition of recursive segmentation. Then we can label character candidate regions in turn by this effective characteristics, and select the regions we want to achieve. The experiments show that this method makes full use of the characteristics of region and clustering. It can improve the quality of detection, and the detection result meets the need of practical application.

关键词： Billets image segmentation Steel Arrays Filtering Licenses

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Research on scene infrared image simulation

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第4期6卷 77-86页

作者： Zou, Lamei Zhang, Tianxu Yang, Weidong Institute for Pattern Recognition and Artificial Intelligence Science and Technology on Multi-spectral Information Processing Laboratory Huazhong University of Science and Technology Wuhan China

Vega has been widely used in the Virtual Reality field. Its infrared (IR) module can implement IR simulation, but Vega IR imaging simulation's general approach does not apply to the complex scene. This article deeps into the scene's IR simulation method based on Vega. We design and realize a real time scene IR image simulation system in this article. We quantitatively define the scene as a simple and complex scene according to the scene range and whether it includes Digital Elevation Model (DEM)/Digital Surface Model (DSM) data. For the simple scene, we directly process IR image simulation according to the Vega general IR simulation process. While for the complex scene, we propose an IR image simulation method based on image classification and automatic texture material mapping technique. At the aspect of image classification, we develop a coarse to fine K-means clustering method based on the consistency of image color for color image classification and an additional Support Vector Machine (SVM) classification method based on texture features for gray level image classification. The method was tested on different scene's IR simulation. Experimental results show that the proposed approach can achieve better applicability and greater efficiency than the popular Vega IR simulation method.

关键词： image classification

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Perceptually weighted Non-negative Matrix Factorization for blind single-channel music source separation

Perceptually weighted Non-negative Matrix Factorization for ...

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International Conference on pattern recognition

作者： S. Kırbız B. Gunsel Multimedia Signal Processing and Pattern Recognition Laboratory Department of Electronics and Communications Engineering Technical University Maslak Istanbul Turkey

We propose a blind single-channel musical source separation method that improves perceptual quality of the separated sources. It uses the advantages of subspace learning based on Non-negative Matrix Factor 2-D Deconvolution (NMF2D). To improve the perceptual quality of separation, we propose a weighted divergence type cost function for the optimization that adopts the auditory model defined in ITU-R BS.1387 into the source separation. It is shown that the proposed perceptually weighted NMF2D scheme efficiently clusters the bases of subspace representation corresponding to notes generated by single instruments. Source separation performance has been reported on musical mixtures resulting an improvement in perceptual quality measures.

关键词： Source separation Spectrogram Time frequency analysis Instruments Humans Time domain analysis Computational complexity

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Comparative analysis of audio watermarking technique in MDCT domain with other references in spectral domain

Comparative analysis of audio watermarking technique in MDCT...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Maha Bellaaj Kaïs Ouni Research Unit of Signal Processing Image and Pattern Recognition National Engineering School of Tunis University Tunis El Manar II Tunis Tunisia

ISBN: (纸本)9781467315906

In this paper we propose a comparative review between the proposed digital audio watermarking technique and those achieved by Luigi Rosa and Rolf Brigola. The performed technique operates in the frequency domain. The time-frequency mapping is done using a Modified Discrete Cosine Transform (MDCT). The technique developed by Luigi Rosa operates in the frequency domain but using the Discrete Cosine Transform (DCT) as transformation and that proposed by Rolf Brigola uses the Fast Fourier Transform (FFT). We studied the robustness of each technique against different types of attack and we evaluated the inaudibility by using a statistical approach by calculating the SNR and an objective approach by calculating the ODG notes given by PEAQ.

关键词： Robustness Discrete cosine transforms Frequency domain analysis Watermarking Signal to noise ratio Niobium Psychoacoustic models

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BP Neural Networks with Harmony Search Method-based Training for Epileptic EEG Signal Classification

BP Neural Networks with Harmony Search Method-based Training...

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International Conference on Computational Intelligence and Security

作者： Xiao-Zhi Gao Jing Wang Jarno M.A. Tanskanen Rongfang Bie Ping Guo College of Information Engineering Shanghai Maritime University China laboratory of Image Processing and Pattern Recognition Beijinz Normal University China Tampereen yliopisto - Hervannan kampus Tampere Pirkanmaa FI

In this paper, the Harmony Search (HS)-based BP neural networks are used for the classification of the epileptic electroencephalogram (EEG) signals. It is well known that the gradient descent-based learning method can result in local optima in the training of BP neural networks, which may significantly affect their approximation performances. Two HS methods, the original version and a new variation recently proposed by the authors of the present paper, are applied here to optimize the weights in the BP neural networks for the classification of the epileptic EEG signals. Simulations have demonstrated that the classification accuracy of the BP neural networks can be remarkably improved by the HS method-based training.

关键词： Electroencephalography Optimization Training Biological neural networks Feature extraction Epilepsy

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Optimal stopping condition for iterative deconvolution method by the measurement of image texture

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第15期6卷 109-119页

作者： Wang, Ze Yan, Luxin Jin, Mingzhi State Key Laboratory for Multispectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China

The non-regularized iterative image restoration algorithms have been widely investigated in the literature. In this work, we focus on a common issue of non-regularized iterative methods, the stopping condition. A no-reference criterion of optimal stopping condition for non-regularized iterative deconvolution called Total Local Binary patterns (TLBP), which is based on the measurement of varying image texture during the deblurring procedure, is proposed. The metric utilizes the minimum of TLBP that is computed according to the LBP map of the blurred image to obtain the optimal restored image. We applied the Richardson-Lucy (RL) method to test the blurred version of the synthetic images and real images in experiments. Deconvolution experiments for Gaussian and out-offocus blur validate the effectiveness of the proposed method.

关键词： image reconstruction

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An Algorithm for Calculating the Hypervolume Contribution of a Set

An Algorithm for Calculating the Hypervolume Contribution of...

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World Automation Congress

作者： Xiuling Zhou Ping Guo C. L. Philip Chen Image Processing and Pattern Recognition Lab Beijing Normal University Beijing 100875 China The Faculty of Science and Technology University of Macau Macau SAR China

ISBN: (纸本)9781467344975

The reliability model can be optimized with a multi-objective optimization algorithm, while hypervolume-based multi-objective evolutionary algorithms (MOEAs) have been shown to produce better results for multi-objective problem in practice. When hypervolume is used in some MOEAs as archiving strategy, diversity mechanism or selection criterion to guide the search, it is necessary to determine which subset contributes the least hypervolume contribution. Few algorithms have been designed for this purpose. In this paper a new algorithm based on HSO (hypervolume by slicing objective) is proposed for calculating the exclusive hypervolume contributions of each subset to the whole nondominated set directly for small dimension. The new algorithm is composed of two parts: the algorithm SHSO (set hypervolume contribution by slicing objective) and the algorithm SHSO*. SHSO is used to calculate the exclusive hypervolume contribution of a subset to the whole nondominated set. SHSO* is applied to select the subset which contributes the least hypervolume contribution by repeated application of SHSO. Compared with HSO adopted for calculating the exclusive hypervolume contribution, SHSO* outperforms HSO for all of the test fronts with small dimension.

关键词： Keyword - Reliability model Multi-objective evolutionary algorithms Hypervolume contribution

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Multiobjective evolutionary algorithm-based soft subspace clustering

Multiobjective evolutionary algorithm-based soft subspace cl...

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Congress on Evolutionary Computation

作者： Lin Zhu Longbing Cao Jie Yang Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University Shanghai China Advanced Analytics Institute Faculty of Engineering and IT University of Technology Sydney Sydney Australia

ISBN: (纸本)9781467315104

In this paper, a multiobjective evolutionary algorithm based soft subspace clustering, MOSSC, is proposed to simultaneously optimize the weighting within-cluster compactness and weighting between-cluster separation incorporated within two different clustering validity criteria. The main advantage of MOSSC lies in the fact that it effectively integrates the merits of soft subspace clustering and the good properties of the multiobjective optimization-based approach for fuzzy clustering. This makes it possible to avoid trapping in local minima and thus obtain more stable clustering results. Substantial experimental results on both synthetic and real data sets demonstrate that MOSSC is generally effective in subspace clustering and can achieve superior performance over existing state-of-the-art soft subspace clustering algorithms.

关键词： Clustering algorithms Optimization Indexes Biological cells Evolutionary computation Entropy Partitioning algorithms

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Classification of gothic and baroque architectural elements

Classification of gothic and baroque architectural elements

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International Conference on Systems, Signals and image processing, IWSSIP

作者： Gayane Shalunts Yll Haxhimusa Robert Sablatnig Institute of Computer Aided Automation Computer Vision Laboratory University of Technology Vienna Austria Institute of Computer Graphics and Algorithms Pattern Recognition and Image Processing Lab University of Technology Vienna Austria

Architectural elements are the components and details of buildings. Their unique set, combination, design, construction technique form the architectural style of buildings. Building facade classification by architectural styles is viewed as a task of classifying separate architectural structural elements. In the scope of building facade architectural style classification the current paper targets the problem of classification of Gothic and Baroque architectural elements called tracery, pediment and balustrade. Since certain gradient directions dominate on the shape of each architectural element, discrimination between dominating gradients means classification of architectural elements and thus architectural styles. We use local features to describe gradient directions. Our approach is based on clustering and learning of local features and yields a high classification rate.

关键词： Shape Three dimensional displays Visualization Computer vision Histograms Windows

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