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International Symposium on Intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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Skeleton-based fast path planning for UAV

Skeleton-based fast path planning for UAV

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MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern Recognition

作者： Liu, Xin Zhou, Chengping Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478092

A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to construct the graph of the planning space considering the configuration of the obstacles and utilizes the graph to find a shortest collision-free path, and a novel technique is utilized to convert the free path into a feasible path. The method can be applied to different applications and easy to be implemented. Experimental results showed that the path planning can be done in a fraction of second on a contemporary workstation (2-3 seconds) under the condition of satisfying the kinematic constraints. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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Remote sensing image compression for deep space based on region of interest

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Journal of Harbin Institute of Technology(New Series) 2003年第3期10卷 300-303页

作者：王振华吴伟仁田玉龙田金文柳健 Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China Institute for Pattern Recognition and Artificial Intelligence State Key Lab for Image Processing and Intelligent ControlHuazhong University of Science and Technology Wuhan 430074 China major limitation for deep space communication is the limited bandwidths available. The downlink rate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However the Next Generation Space Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be reduced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image compression techniques. An very low bit rate image compression method based on region of interest(ROI) has been proposed for deep space image. The conventional image compression algorithms which encode the original data without any data analysis can maintain very good details and haven't high compression rate while the modern image compressions with semantic organization can have high compression rate even to be hundred and can't maintain too much details. The algorithms based on region of interest inheriting from the two previews algorithms have good semantic features and high fidelity and is therefore suitable for applications at a low bit rate. The proposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal local quality with bit rate control. The Result shows that our method can maintain more details in ROI than general image compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas

A major limitation for deep space communication is the limited bandwidths available. The downlinkrate using X-band with an L2 halo orbit is estimated to be of only 5.35 GB/d. However, the Next GenerationSpace Telescope (NGST) will produce about 600 GB/d. Clearly the volume of data to downlink must be re-duced by at least a factor of 100. One of the resolutions is to encode the data using very low bit rate image com-pression techniques. An very low bit rate image compression method based on region of interest(ROI) has beenproposed for deep space image. The conventional image compression algorithms which encode the original datawithout any data analysis can maintain very good details and haven' t high compression rate while the modernimage compressions with semantic organization can have high compression rate even to be hundred and can' tmaintain too much details. The algorithms based on region of interest inheriting from the two previews algorithmshave good semantic features and high fidelity, and is therefore suitable for applications at a low bit rate. Theproposed method extracts the region of interest by texture analysis after wavelet transform and gains optimal localquality with bit rate control. The Result shows that our method can maintain more details in ROI than generalimage compression algorithm(SPIHT) under the condition of sacrificing the quality of other uninterested areas.

关键词： wavelet compression ROI deep space

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Terahertz compressive imaging: understanding and improvement by a better strategy for data selection

Terahertz compressive imaging: understanding and improvement...

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作者： Xing, Chungui Qi, Feng Liu, Zhaoyang Wang, Yelong Guo, Shuxu State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering Jilin University Changchun China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Sciences Shenyang China Key Lab of Image Understanding and Computer Vision Shenyang China

Compressive sensing (CS) is a novel sampling modality, which indicates the signals can be sampled at a rate much below the Nyquist sampling rate. CS has increasing interest recently due to high demand of rapid, efficient, and in-expensive signal processing applications in the μmWave and mmWave frequencies, such as communication and imaging. There have been a lot of theoretical studies on this topic, but there is a lack of systematic experimental analysis of the implementation method itself. In this paper, we have investigated the influencing factors of terahertz compressive sensing based on experimental results, including illumination and the size of the pixel. Besides, to differentiate from current approaches, which generally make full use of the data, we propose to sort the data first and select a part of them based on amplitude, which might deliver a better image by prompting the mathematical calculations compulsively. We believe that such considerations given above would help to make a better system design and improve the performance of compressive imaging, and these results will also be helpful in the application of terahertz communication. © 2021 John Wiley & Sons, Ltd

关键词： Compressed sensing

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Adaptive Person-Specific Appearance-Based Gaze Estimation 16th

Adaptive Person-Specific Appearance-Based Gaze Estimation

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16th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2019

作者： Zheng, Chuanyang Zhou, Jun Sun, Jun Zhao, Lihua Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Key Lab of Digital Media Processing and Transmissions Shanghai Jiao Tong University Shanghai200240 China Children’s Hospital of Shanghai Shanghai200062 China

ISBN: (纸本)9789811533402

Non-invasive gaze estimation from only eye images captured by camera is a challenging problem due to various eye shapes, eye structures and image qualities. Recently, CNN network has been applied to directly regress eye image to gaze direction and obtains good performance. However, generic approaches are susceptible to bias and variance highly relating to different individuals. In this paper, we study the person-specific bias when applying generic methods on new person. And we introduce a novel appearance-based deep neural network integrating meta-learning to reduce the person-specific bias. Given only a few person-specific calibration images collected in normal calibration process, our model adapts quickly to test person and predicts more accurate gaze directions. Experiments on public MPIIGaze dataset and Eyediap dataset show our approach has achieved competitive accuracy to current state-of-the-art methods and are able to alleviate person-specific bias problem. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

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Tangram Algorithm:High image Quality Secret Sharing Scheme Using Two Steganographic images

Tangram Algorithm:High Image Quality Secret Sharing Scheme U...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Wanli Lyu Jiahao Deng Yu Zhang Dongshuo Yin Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

In this paper,our proposed scheme using two steganography *** other data hiding schemes,this scheme studies the characteristics of the Tangram puzzle model and the secret message bytes that make up the eight binary bits that are hidden into the two *** matrix M *,horizontal and vertical coordinate range from 0 to 255,and the pixel pairs in the cover image are used to help hide the secret message bytes in our *** paired pixel points into single and double number hidden in two *** show that the program can hide relatively more pixels in a cover image with less losing image quality.

关键词： steganography images Tangram puzzle matrix M hide relatively scenario

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FC-GNN: Recovering Reliable and Accurate Correspondences from Interferences

FC-GNN: Recovering Reliable and Accurate Correspondences fro...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haobo Xu Jun Zhou Hua Yang Renjie Pan Cunyan Li Institute of Image Communication and Network Engineering Shanghai Jiao Tong University Shanghai Key Lab of Digital Media Processing and Transmission

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

Finding correspondences between images is essential for many computer vision tasks and sparse matching pipelines have been popular for decades. However, matching noise within and between images, along with inconsistent key-point detection, frequently degrades the matching performance. We review these problems and thus propose: 1) a novel and unified Filtering and Calibrating (FC) approach that jointly rejects outliers and optimizes inliers, and 2) leveraging both the matching context and the underlying image texture to remove matching uncertainties. Under the guidance of the above innovations, we construct Filtering and Calibrating Graph Neural Network (FC-GNN), which follows the FC approach to recover reliable and accurate correspondences from various interferences. FC-GNN conducts an effectively combined inference of contextual and local information through careful embedding and multiple information aggregations, predicting confidence scores and calibration offsets for the input correspondences to jointly filter out outliers and improve pixel-level matching accuracy. Moreover, we exploit the local coherence of matches to perform inference on local graphs, thereby reducing computational complexity. Overall, FC-GNN operates at lightning speed and can greatly boost the performance of diverse matching pipelines across various tasks, showcasing the immense potential of such approaches to become standard and pivotal components of image matching. Code is avaiable at https://***/xuy123456/fcgnn.

关键词： Matched filters Computer vision Technological innovation Accuracy Uncertainty Computer network reliability Pipelines

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Robust Mobile Spamming Detection via Graph Patterns

Robust Mobile Spamming Detection via Graph Patterns

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International Conference on Pattern Recognition

作者： Yuhang Zhao Zhaoxiang Zhang Yunhong Wang Jianyun Liu Intelligent Recognition and Image Processing Lab Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University

ISBN: (纸本)9781467322164

Short message service (SMS) is now an indispensable way of social communication. However the mobile spam is getting increasingly serious, troubling users' daily life and ruining the service quality. We propose a novel approach for spam message detection based on mining the underlying social network of SMS activities. Comparing with strategies on keywords or flow detection, our network-based approach is more robust and difficult to defeat by human spammers. Various levels of features are employed to describe multiple aspects of the network, such as static structures, node activities and evolving situations. Experimental results on real dataset illustrate effectiveness of various features, showing our promising results.

关键词： data mining electronic messaging feature extraction mobile computing network theory (graphs) social networking (online) unsolicited e-mail

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A Reliable Data Hiding Scheme Using Jigsaw Sudoku

A Reliable Data Hiding Scheme Using Jigsaw Sudoku

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Wanli Lyu Dongshuo Yin Yu Zhang Jiahao Deng Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and TechnologyAnhui UniversityKey Lab of Industrial Image Processing and Analysis of Anhui Province

ISBN: (纸本)9781510871076

A distinctive data hiding method based on Jigsaw Sudoku is proposed in this *** from other methods,the proposed method is based on Jigsaw Sudoku which is a kind of irregularly shaped *** to the novelty of Jigsaw Sudoku,the proposed method can provide a safer scheme for data hiding than that with pure *** proposed method can hide a certain number of 9-ary numbers in a steganographic image which has a corresponding *** a 256*256 key matrix stretched from a size 9*9 Jigsaw Sudoku,secret message can be hidden in a vector image byte by byte in the form of *** results show that,the proposed method has a better peaksignal-to-noise-ratio(PSNR) compared with some other data hiding methods.

关键词： Sudoku PSNR hiding methods irregularly shaped steganographic image corresponding specification

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Learning large number of local statistical models via variational Bayesian inference for brain voxel classification in magnetic resonance images

Learning large number of local statistical models via variat...

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International Conference on Security, Pattern Analysis, and Cybernetics (SPAC)

作者： Yong Xia Yanning Zhang Shaanxi Key Lab of Speech & Image Information Processing (SAIIP) School of Computer Science Northwestern Polytechnical University Xi'an China

As an essential step in brain studies, measuring the distribution of major brain tissues, including gray matter, white matter and cerebrospinal fluid (CSF), using magnetic resonance imaging (MRI) has attracted extensive research efforts over the past years. Many brain tissue differentiation methods resulted from these efforts are based on the finite statistical mixture model, which however, in spite of its computational efficiency, is not strictly followed due to the intrinsically limited quality of MRI data and may lead to less accurate results. In this paper, a novel large-scale variational Bayesian inference (LS-VBI) learning algorithm is proposed for automated brain MRI voxels classification. To cope with the complexity and dynamic nature of MRI data, this algorithm uses a large number of local statistical models, in each of which all statistical parameters are assumed to be random variables sampled from conjugate prior distributions. Those models are learned using variational Bayesian inference and combined to predict the class label of each brain voxel. This algorithm has been evaluated against several state-of-the-art brain tissue segmentation methods on both synthetic and clinical brain MRI data sets. Our results show that the proposed algorithm can classify brain voxels more effectively and provide more precise distribution of major brain tissues.

关键词： Magnetic resonance imaging Classification algorithms Brain models Inference algorithms Data models

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