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ACM/IEEE International Conference on Distributed Smart Cameras, ICDSC

作者： Qiao Huang Jie Yang Yu Qiao Institute of Image Processing and Pattern Recognition and Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai China

Tracking the same person across multiple cameras is an important task in multi-camera systems. It is also desirable to re-identify the individuals who have been previously seen with a single-camera. This paper addresses this problem by the re-identification of the same individual in two different datasets, which are both challenging situations from video surveillance system. In this paper, local descriptors are introduced for image description, and support vector machines are employed for high classification performance and so an efficient Bag of Features approach for image presentation. In this way, robustness against low resolution, occlusion and pose, viewpoint and illumination changes is achieved in a very fast way. We get promising results from the evaluation with situations where a number of individuals vary continuously from a multi-camera system.

关键词： Vocabulary Cameras Histograms Feature extraction Image color analysis Support vector machine classification Kernel

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Incremental tensor principal component analysis for image recognition

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Advanced Materials Research 2013年 710卷 584-588页

作者： Zhao, Wei Dong Liu, Chang Yan, Tao College of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu 610106 China

ISBN: (纸本)9783037857205

Aiming at the disadvantages of the traditional off-line vector-based learning algorithm, this paper proposes a kind of Incremental Tensor Principal Component Analysis (ITPCA) algorithm. It represents an image as a tensor data and processes incremental principal component analysis learning based on update-SVD technique. On the one hand, the proposed algorithm is helpful to preserve the structure information of the image. On the other hand, it solves the training problem for new samples. The experiments on handwritten numeral recognition have demonstrated that the algorithm has achieved better performance than traditional vector-based Incremental Principal Component Analysis (IPCA) and Multi-linear Principal Component Analysis (MPCA) algorithms. © (2013) Trans Tech Publications, Switzerland.

关键词： Principal component analysis

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Detection of wide linear structures by fusion of width and gray

Detection of wide linear structures by fusion of width and g...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Anna Zhu Guoyou Wang Ran Wang State Key Laboratory for Multispectral Information Processing Technology Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology China

ISBN: (纸本)9781479900145

Lines provide important information in images and line detection is crucial in many applications. Many line features can be used to detect line position while line width (i.e., thickness) is a more structured, higher-level feature compared to edge or other line features. Every point of the wide line structure has its own width in spite of the structure's asymmetry. In this paper, we use the parallel edges to get the line orientation and width. We do not need to know the actual width of the line, but rather recover it to get the region of interest (ROI). Then use a modified OTSU obtaining proper region threshold T to segment the wide line structures. By fusing feature of width and gray, a sequence of tests has been conducted on a variety of image samples obtained from simple natural scene and our experimental results demonstrate the practical and robust of the proposed method.

关键词： Image edge detection Estimation Detectors Feature extraction Image segmentation Transforms Roads

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Research on the identification of obstacle image based on convolutional neural network

Research on the identification of obstacle image based on co...

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2021 International Conference on Signal Image processing and Communication, ICSIPC 2021

作者： Zhongyu, Li Huajun, Wang Yuhang, Cao Yuting, Dai College of Geophysics Chengdu University of Technology Chengdu610059 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China College of Computer Science Chengdu Normal University Chengdu611130 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China Key Laboratory of Interior Layout Optimization and Security Institutions of Higher Education of Sichuan Province Chengdu Normal University Chengdu Sichuan611130 China

ISBN: (数字)9781510645417

ISBN: (纸本)9781510645400

To effectively realize the reasonable obstacle avoidance of the detection robot, VGG based obstacle discrimination method is proposed. Above all, the image captured by the robot is input into the multi-layer convolutional neural network to obtain the high-level image features, which are used to construct the more accurate neural network model parameters and to train the softmax classifier with these parameters. Then the distance between the imported images and the data images is calculated by using the softmax classifier, and the similarity between the obstacles and non-obstacles is estimated. The experimental results show that the discrimination accuracy increase to above 94%. And the proposed method is more effectively compared with traditional ultrasonic and radar methods. © 2021 SPIE.

关键词： Convolution

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Needle segmentation using 3D quick randomized hough transform

Needle segmentation using 3D quick randomized hough transfor...

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1st International Conference on Intelligent Networks and Intelligent Systems, ICINIS 2008

作者： Qiu, Wu Ding, Mingyue Yuchi, Ming Institute for Pattern Recognition and Artificial Intelligence Image Processing and Intelligence Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan Hubei China Department of Biomedical Engineering School of Life Science and Technology Huazhong University of Science and Technology Wuhan Hubei China

ISBN: (纸本)9780769533919

A quick 3D needle segmentation algorithm for 3D US data is described in this paper. The algorithm includes the 3D Quick Randomized Hough Transform (3DGHT), which is based on the 3D Randomized Hough Transform and coarse-fine searching strategy. We tested it with water phantom. The results show that our algorithm works well in 3D US images with angular deviation less than 1 and position deviation less than 1mm, and the computational time of segmentation with 35MB data is within 1s. © 2008 IEEE.

关键词： Hough transforms

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An improved K-OPT local search algorithm for the vertex separator problem

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Journal of Computational and Theoretical Nanoscience 2015年第11期12卷 4942-4958页

作者： Zhang, Zhiqiang Shao, Zehui Key Laboratory of Pattern Recognition and Intelligent Information Processing Institutions of Higher Education of Sichuan Province China School of Computer Science Chengdu University Chengdu610106 China

A vertex separator in an undirected graph is a subset of the vertices, whose removal disconnects the graph in at least two nonempty connected components. Given a connected undirected graph G = (V ,E) with |V| = n, an integer 1 ≤ b ≤ n, the vertex separator problem (VSP) asks to find a partition of V into disjoint sets A, B, C, with A and B nonempty, such that (1) E contains no edge (i, j) with i A, j ? B, (2) max{|A|} |B|} = b, (3) |C| is minimized. The vertex separator problem is a classic NP complete problem. It is widely applied to circuit design, computer network communication, protein combination in bioinformatics, etc. At present there has already been exact algorithm to search vertex separator of undirected graph, while there are only few heuristic algorithms. Aiming at the VSP and a solution design for heuristic algorithm, this paper designs an improved K-OPT local search algorithm. In order to compare the searching efficiency of a variety of heuristic algorithms for VSP, this paper also designs simulated annealing algorithm and variable neighborhood search algorithm, and performs comparative experiments on these algorithms using DIMACS graph, MATRIXMARKET graph, MIPLIB graph. The experimental results show that the improved K-OPT local search algorithm goes the best. In the experiment, using improved K-OPT local search algorithm, existing Relax-and-Cut algorithm is further compared, whose experimental results show that in some graph tests, the improved K-OPT local search algorithm goes better than Relax-and-Cut algorithm. © 2015 American Scientific Publishers.

关键词： Undirected graphs

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Incremental tensor discriminant analysis for image detection

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Advanced Materials Research 2013年 710卷 579-583页

作者： Yan, Tao Liu, Chang Zhao, Wei Dong College of Information Science and Technology Chengdu University Chengdu 610106 China Key Laboratory of Pattern Recognition and Intelligent Information Processing Sichuan Province Chengdu 610106 China

ISBN: (纸本)9783037857205

In order to settle incremental learning and preserve the space information of images, this paper proposes an incremental tensor discriminant analysis for facial image detection. The proposed algorithm employs tensor representation to preserve the structure information and introduces the incremental learning to solve the on-line learning for new added samples. The experiments have shown that our method achieves better classification performance and reduces the computational cost effectively compared with other algorithms. © (2013) Trans Tech Publications, Switzerland.

关键词： Discriminant analysis

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Region-Based Simplification of Laser Scanned Meshes

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Journal of Graphics Tools 2003年第2期8卷 41-50页

作者： Ye Jianhui[a] [a]Image Processing & Intelligent Control Key Laboratory of China Institute of Pattern Recognition & Artificial Int elligence Huazhong University of Science and Technology Wuhan 430074 PR China E-mail:

To simplify the mesh acquired from at hree-dimensional laser scanner, it is more important to keep the boundary and quality of the region of interest than of other regions. The algorithm must not be sensitive to noise introduced in practical applications. In this paper, we present a novel vertex merging mesh simplification algorithm based on region segmentation. The algorithm can be divided into two stages: segmentation and simplification. After the segmentation of the 3D color mesh into different regions, vertices are classed into a region-boundary vertex, which can only be merged into a region-boundary vertex in order to guarantee the completeness of the regions' boundary, and region-inner vertex. The iterative vertex merging is applied with a region-weighted error metric, which implements controllable simplifications. We demonstrate our method with several examples of a 3D color human head mesh.

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FASTC: A Fast Attentional Framework for Semantic Traversability Classification Using Point Cloud

arXiv

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arXiv 2024年

作者： Chen, Yirui Wei, Pengjin Liu, Zhenhuan Wang, Bingchao Yang, Jie Liu, Wei Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Key Laboratory of System Control and Information Processing Ministry of Education China

Producing traversability maps and understanding the surroundings are crucial prerequisites for autonomous navigation. In this paper, we address the problem of traversability assessment using point clouds. We propose a novel pillar feature extraction module that utilizes PointNet to capture features from point clouds organized in vertical volume and a 2D encoder-decoder structure to conduct traversability classification instead of the widely used 3D convolutions. This results in less computational cost while even better performance is achieved at the same time. We then propose a new spatio-temporal attention module to fuse multi-frame information, which can properly handle the varying density problem of LIDAR point clouds, and this makes our module able to assess distant areas more accurately. Comprehensive experimental results on augmented Semantic KITTI and RELLIS-3D datasets show that our method is able to achieve superior performance over existing approaches both quantitatively and quantitatively. Our code is publicly available at https://***/chenyirui/FASTC. © 2024, CC BY.

关键词： Semantics

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Prototype-guided Unsupervised Domain Adaptation for Semantic Segmentation

Prototype-guided Unsupervised Domain Adaptation for Semantic...

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Control, Electronics and Computer Technology (ICCECT), IEEE International Conference on

作者： Shaoqi Xia Hua Huang Qishen Li Yifan He Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Information Engineering Nanchang Hangkong University Nanchang China School of Information Engineering Nanchang Hangkong University Nanchang China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition School of Software Nanchang Hangkong University Nanchang China

Semantic segmentation is one of the most important research directions in the field of computer vision, and has a wide range of applications for autonomous driving, medical imaging, intelligent security, etc. Unsupervised domain adaptation is the mainstream research topic in recent years, which can use a large number of labeled source samples to complete the segmentation task in target domain without labeled target samples. In this paper, we propose a prototype-guided unsupervised domain adaptation for semantic segmentation based on ProDA model. Due to lacking of labeled target samples and the prior probability, a prototype distance loss based on target domain is proposed to optimize the distribution of features by measuring the distance between features and the updated prototype and designing an adaptive threshold strategy. Meanwhile, a smoothing loss is proposed to alleviate the impact of source samples on our model and improve the prediction performance of the network. By conducting experiments on the GTA5 to Cityscapes scenarios, the results show that compared with the original model, the loss optimization improves mIoU by1.52.

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