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8th International Conference on Computing and Artificial Intelligence, ICCAI 2022

作者： Wei, Yanxia Xiao, Hairong Feng, Deying Mu, Jian Yang, Jie School of Mechanical and Automotive Engineering Liaocheng University China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China Liaocheng University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781450396110

Recently, Discriminative Correlation Filter based trackers have increasingly become popular in the domain of visual object tracking, which is benefited by their effective and robustness in terms of tracking performance. However, the significant variations of target appearance, such as Occlusion or Deformation, always suppress the performance improvement. Although the multi-feature fusion strategy partly alleviates the above factors, the synthetic scheme also ambiguates the overall presentation ability of multiple features and results in degradation of discriminative power during classification filter learning. In this paper, we present a novel tracking framework based on the correlation filter with multiple kernel learning. Instead of directly integrating different features, we decompose a tracking process into several module, including low-dimension modules and high-dimension modules. The handcraft features extracted from low-dimension modules are robust to the small variation of target while the semantic convolutional features from the high-dimension modules can distinguish the target object from the relative complex tracking environment. In addition, we also design a reliability detection mechanism based on the confidence map to accurately measure the important degree of each kernel learning and precisely estimate the target state in the subsequent frames. For verifying our proposed tracker, we conduct extensive experiments on several standard sequence benchmark. Experiment results show that our proposed trackers perform favorably against other state-of-the-art trackers. © 2022 ACM.

关键词： Semantics

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Kernel Selection with Evolutionary Algorithm for Multiple Kernel Independent Component Analysis

Kernel Selection with Evolutionary Algorithm for Multiple Ke...

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International Joint Conference on Neural Networks

作者： Peng Wu Qian Yin Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781509006212

Kernel independent component analysis (KICA) has an important application in blind source separation, in which how to select the optimal kernel, including the kernel functional form and its parameters, is the key issue for obtaining the optimal performance. In practices, a single kernel is usually chosen as the kernel model of KICA in light of experience. However, selecting a suitable kernel model is a more difficult problem if one has not sufficient experience. To deal with this problem, an evolution based method to select the kernel model of KICA is proposed in this paper. There are two main features of the proposed method: one is that using a multiple kernel model, a convex combination of several single kernels, replaces the single kernel model;another is that particle swarm optimization (PSO) algorithm is utilized to find the combination weights of the composite kernel. Experiments conducted on separating one-dimensional mixed signals, nature images, and spectroscopic CCD images showed that using multiple kernels model with PSO kernel selection algorithm can enhance the performance of KICA.

关键词： Seeds Kernel evolutionary algorithm Independent component analysis

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image Segmentation Based on Visual Perception Model

Image Segmentation Based on Visual Perception Model

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2012年计算机应用与系统建模国际会议

作者： Shuai Shao Fuqing Duan Ping Guo Image Processing and Pattern Recognition Laboratory Beijing Normal University

image segmentation is the basis of image processing and image analysis. However, there are no common method that can be used in natural images, and present methods fail to explain understandings of human's visual system. In this paper, we propose to apply Karklin's visual perception model to extract feature vectors of images, and the features are clustered with K-means method. The results obtained in feature space are projected back to the image space to finish segmentation. A comparison with the Normalized Cuts (Ncut) method is done, and it turns out that proposed method outperform Ncut in texture rich images.

关键词： Visual perception model image segmentation Normalized cuts Feather extraction

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Adaptive synthetic aperture radar image enhancement

Adaptive synthetic aperture radar image enhancement

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MIPPR 2009 - Multispectral image Acquisition and processing: 6th International Symposium on Multispectral image processing and pattern recognition

作者： Cheng, Hua Tian, Jinwen Huazhong University of Science and Technology Institute for Pattern Recognition and Artificial Intelligence State Key Laboratory for Multi-spectral Information Processing Technologies China

ISBN: (纸本)9780819478054

An adaptive SAR image enhancement method is presented for reducing the speckle noise and increasing the contrast of synthetic aperture radar (SAR) images. First, a fuzzy logic based filter, employing fuzzy edge to weight the contributions of pixel values in filter window, is used to filter the speckles. Second, the original SAR image is decomposed into lowfrequency component and high-frequency component. The fuzzy filtered image is viewed as the low-frequency component, and the contrast limited adaptive histogram equalization algorithm is used to increase its contrast. The highfrequency component is obtained by subtracting the low-frequency component from the original image, and its gain is controlled by fuzzy structural which employed to express the degree of a pixel belonging to structures. After processed one after the other, the two components are added together to form the final enhanced SAR image. Experimental results show the excellent effect of the proposed method by visual observation and numerical measurement. Many fine structures and little speckle noise can be seen from the enhanced SAR images. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： image enhancement

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Imaging lidar based 3D terrain matching using feature vector

Imaging lidar based 3D terrain matching using feature vector

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MIPPR 2007: Multispectral image processing

作者： Hua, Cheng Jinwen, Tian Jie, Ma Junbin, Gong Lin, Wu State Key Laboratory for Multi-spectral Information Processing Technologies Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

ISBN: (纸本)9780819469519

A novel 3D terrain matching algorithm is presented in this paper. A terrain feature vector map (FVM), composed of local mean and local gradient, is employed to represent the terrain elevation map (TEM). Compared with traditional matching algorithm using the magnitude of gradient to match, the new algorithm uses each component of the gradient vector to match individually, and it is able to generate two interim matching positions. Different from traditional matching algorithms which usually estimate an optimum matching position under some criterions at the end, the new algorithm fused the two interim matching positions to generate a final matching position or refuse to position in order to increase the matching confidence, which is very important because it is hardly acceptable to employ a mismatched position to correct the error of Inertial Navigation System (INS). Due to the stability of terrain and the high-precision of lidar ranging, the mean of a sensed terrain elevation map (STEM) sized terrain is quite stable. So it is bestowed to accelerate the matching process and to reduce mismatches at different terrain heights. Compared with other mismatch-eliminated methods based on neural network (NN) or support vector machine (SVM), the new method do not need training samples and is more stable and robust. Experimental results show that the proposed algorithm is effective and robust.

关键词： pattern matching

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A Multiple Scattering in Participating Media for Real Time Rendering

A Multiple Scattering in Participating Media for Real Time R...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Ping Guo Yuanlong Wang Image Processing and Pattern Recognition Laboratory Beijing Normal University

ISBN: (纸本)9781467317139

At present, multiple scattering problems in participating media is still very challenging for real time rendering. Some methods have proposed to describe multiple scattering phenomena, however, there are some restriction conditions such as requiring the medium is static, etc., and rendering speed is not satisfied real-time requirement. In order to speed up the multiple scattering rendering, we propose a GPU based algorithm in this paper. First of all, the media is initialized with a particle system and the property of each particle is defined;secondly, according to the properties of each particle, a method of tracing the particle path, which is generated by uniformly sampling the surrounding particles of one particle, is proposed and this method is used to compute in-scattering radiance for each particle;finally, the total radiance is calculated by summing up contributions of particles along ray paths and the final image is rendered. The experimental results show that the proposed algorithm can achieve the real-time rendering effect.

关键词： Component Computer graphics Ray tracing Multiple scattering Real tint e rendering Parallel computing Rendering broadcasting Scattering Ray paths particles radiance Real time

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Feature Selection Based on Adaptive Fuzzy Membership Functions

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自动化学报 2006年第4期32卷 496-503页

作者： XIE Yan-Tao SANG Nong ZHANG Tian-Xu Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

Neuro-fuzzy(NF)networks are adaptive fuzzy inference systems(FIS)and have been applied to feature selection by some ***,their rule number will grow exponentially as the data dimension *** the other hand,feature selection algorithms with artificial neural networks(ANN)usually require normalization of input data,which will probably change some characteristics of original data that are important for *** overcome the problems mentioned above,this paper combines the fuzzification layer of the neuro-fuzzy system with the multi-layer perceptron(MLP)to form a new artificial neural ***,fuzzification strategy and feature measurement based on membership space are proposed for feature selection. Finally,experiments with both natural and artificial data are carried out to compare with other methods,and the results approve the validity of the algorithm.

关键词： Membership function feature selection architecture pruning artificial neural networks

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Edge Detection Using image Feature Detector

Edge Detection Using Image Feature Detector

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1998 Fourth International Conference Signal processing

作者： Yang Xuan Liang Dequn Yang Haijun Laboratory of Image Processing and Pattern Recognition Xi'an Jiaotong University

A new edge detection operator based on image feature is proposed,which analyze edges in image for edge feature in two *** local extreme of the operator is created at the edge location and low value is created at the smooth *** can be located by obtaining the local extreme and a threshold of the operator response. The detection operator is shown to be better than Canny operator in terms of signal-to-noise ratio and edge location accuracy.

关键词： Edge Detection image Feature

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Combining local binary patterns for scene recognition

Journal of Software

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Journal of Software 2014年第1期9卷 203-210页

作者： Song, Minguang Guo, Ping Image Processing Pattern Recognition Laboratory Beijing Normal University Beijing 100875 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. An improved representation called Local Difference Binary pattern (LDBP) also was proposed and performed better than that of PACT. LDBP is based on the comparisons between center pixel and its neighboring pixels, but the relationship among neighbor pixels is not considered. In this paper, we propose to combine Local Neighbor Binary pattern (LNBP) with LDBP to construct a spatial representation for scene recognition, because that LNBP can provide complementary information regarding neighboring pixels for LDBP. Experiments on widely used datasets demonstrate that the performance of image recognition is further improved with proposed method. © 2014 ACADEMY PUBLISHER.

关键词： Pixels

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A new noise erosion operator for anisotropic diffusion

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Chinese Optics Letters 2004年第5期2卷 268-270页

作者：蔡超丁名跃周成平张天序 Institute for Pattern Recognition and Artificial Intelligence State Education Commission Key Lab for Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan 430074

A noise erosion operator based on partial differential equation (PDE) is introduced, which has an excellent ability of noise removal and edge preservation for two-dimensional (2D) gradient data. The operator is applied to estimate a new diffusion coefficient. Experimental results demonstrate that anisotropic diffusion based on this new erosion operator can efficiently reduce noise and sharpen object boundaries.

关键词： Anisotropic diffusion Diffusion

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