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检索条件"机构=Key Lab. of Image Processing and Intelligent Control"

共 182 条记录，以下是61-70 订阅

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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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Combinational method for focal brain activation detection using MEG signal

Combinational method for focal brain activation detection us...

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Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Rajabioun, Mehdi Babajani-Feremi, Abbas Soltanian-Zadeh, Hamid Control and Intelligent Processing Center of Excellence Electrical and Computer Engineering Department University of Tehran Tehran 14395-515 Iran Image Analysis Lab. Radiology Department Henry Ford Hospital Detroit MI 48202 United States

ISBN: (纸本)9780819475138

Magnetoencephalography (MEG) is a neuroimaging technique for brain activation detection. This technique does not provide a unique solution due to ill-posedness of its inverse solution. Several methods are proposed to improve the MEG inverse solution. Minimum Norm (MN) is a simple method whose solution is distributed and biased toward the superficial sources. In addition, its solution is sensitive to the noise. Several methods are proposed to improve performance of the MN method. In this paper, we propose a method whose solution is less sensitive to the noise and spatially unbiased toward the superficial sources. control of focal solution properties is achieved by specifying a parameter in the proposed method. Performance of the proposed method is compared to others using simulation studies consisting of single and multiple dipole sources as well as an extended source model. Proposed method has superior performance compared to non-iterative methods. Its performance is similar to the iterative methods but its computational load is lower. © 2009 SPIE.

关键词： Inverse problems

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Do Not Disturb Me: Person Re-identification Under the Interference of Other Pedestrians 16th

Do Not Disturb Me: Person Re-identification Under the Interf...

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16th European Conference on Computer Vision, ECCV 2020

作者： Zhao, Shizhen Gao, Changxin Zhang, Jun Cheng, Hao Han, Chuchu Jiang, Xinyang Guo, Xiaowei Zheng, Wei-Shi Sang, Nong Sun, Xing Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9783030585389

In the conventional person Re-ID setting, it is assumed that cropped images are the person images within the bounding box for each individual. However, in a crowded scene, off-shelf-detectors may generate bounding boxes involving multiple people, where the large proportion of background pedestrians or human occlusion exists. The representation extracted from such cropped images, which contain both the target and the interference pedestrians, might include distractive information. This will lead to wrong retrieval results. To address this problem, this paper presents a novel deep network termed Pedestrian-Interference Suppression Network (PISNet). PISNet leverages a Query-Guided Attention Block (QGAB) to enhance the feature of the target in the gallery, under the guidance of the query. Furthermore, the involving Guidance Reversed Attention Module and the Multi-Person Separation Loss promote QGAB to suppress the interference of other pedestrians. Our method is evaluated on two new pedestrian-interference datasets and the results show that the proposed method performs favorably against existing Re-ID methods. © 2020, Springer Nature Switzerland AG.

关键词： Computers

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Dynamic integral sliding mode for launch vehicle attitude control system

Dynamic integral sliding mode for launch vehicle attitude co...

引用

Chinese control and Decision Conference, CCDC

作者： Zirui Wang Lei Liu Yongji Wang Zhishen Wang Key Lab for Image Processing & Intelligent Control Huazhong University of Science & Technology Wuhan China

As it is known that launch vehicle is facing a very harsh environment during the fight process. The challenge of various perturbations and uncertainties has lead to many traditional control methods' failure to meet the requirements of attitude control system. Due to the main advantage of sliding mode control's robustness to the system uncertainties and disturbances in the so-called sliding mode, it has been widely used in engineering. In this paper, regarding particularly on chattering problem, the authors developed a novel dynamic integral sliding mode control scheme and the comparative simulation results carried out with traditional dynamic integral sliding mode demonstrates the superiority of the newly designed control law.

关键词： Sliding mode control Rockets Attitude control Simulation Vehicle dynamics Vehicles

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Blurring length estimation using ringing artifacts in a deblurred image

Blurring length estimation using ringing artifacts in a debl...

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3rd International Conference on image Analysis and Signal processing, IASP 2011

作者： Yang, Shuangxue Liu, Hong Liu, Benyong Liao, Xiang School of Computer Science and Information Institute of Intelligent Information Processing Guizhou University Guiyang 550025 China Key Lab. of Sound-Image Material Examination and Authentication Ministry of Public Security of PRC Guiyang 550001 China

ISBN: (纸本)9781612848808

The restoration quality of a motion-blurred image is highly dependent on the estimation accuracy of the motion blurring parameter. This manuscript presents a novel and precise method for estimation of the motion blurring length, wherein the ringing artifact amount of a deblurred image is measured by energy proportion contained in appropriate frequency bands, and a blurring length with minimum ringing artifact amount is taken as the optimal estimation of the true blurring length. Experimental results show good performance of the proposed algorithm in motion blurring length estimation. © 2011 IEEE.

关键词： image enhancement

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Modeling of McKibben Pneumatic Artificial Muscles Using Optimized Echo State Networks

Modeling of McKibben Pneumatic Artificial Muscles Using Opti...

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The 8th World Congress on intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Hanying Zhou Yongji Wang Kexin Xing Key Lab for Image Processing &Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology

In conventional techniques for modeling Pneumatic artificial muscle, there are difficulties such as poor knowledge of the process, inaccurate process or complexity of the resulting mathematical model. Trying to solve these problems, this study investigates the method of establishing the model using a novel network—Echo State Network (ESN). We introduce the mechanism of this net and apply it to our modeling work. The relevant parameters of the net were optimized using Particle Swarm Optimization (PSO). Then we get the simulation results which reveal that it can get quite satisfactory results.

关键词： Training Trajectory Muscles Data models Mathematical model Force Artificial neural networks

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Color image filtering based on impulsive noise detection

Color image filtering based on impulsive noise detection

引用

MIPPR 2007: Automatic Target Recognition and image Analysis;and Multispectral image Acquisition

作者： Li, Dehua Jin, Lianghai Yao, Xingzhong Xiong, Caiquan State Key Lab. for Image Processing and Intelligent Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology Wuhan 430074 China Second Artillery Command College Wuhan 430012 China School of Computer Science Hubei University of Technology Wuhan 430068 China

ISBN: (纸本)9780819469502

This paper introduces a new class of switching vector median filter. The proposed algorithm first uses four directional masks to analyze the color difference between the central pixel and its neighborhood pixels in the RGB color space and classify each color pixel into noisy pixel or noise-free one, and then employs the standard vector median filtering operations in the detected noisy locations to restore the corrupted pixels and leave the noise-free ones unchanged. The simulation results show that the proposed method excellently suppresses impulsive noise as well as preserving the image details well, and significantly outperforms the existing vector filtering solutions in terms of both the objective measures and the perceptual visual quality.

关键词： Color image processing

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Adversarial Refinement Network for Human Motion Prediction 15th

Adversarial Refinement Network for Human Motion Prediction

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15th Asian Conference on Computer Vision, ACCV 2020

作者： Chao, Xianjin Bin, Yanrui Chu, Wenqing Cao, Xuan Ge, Yanhao Wang, Chengjie Li, Jilin Huang, Feiyue Leung, Howard City University of Hong Kong Hong Kong China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Tencent Youtu Lab Shanghai China

ISBN: (纸本)9783030695316

Human motion prediction aims to predict future 3D skeletal sequences by giving a limited human motion as inputs. Two popular methods, recurrent neural networks and feed-forward deep networks, are able to predict rough motion trend, but motion details such as limb movement may be lost. To predict more accurate future human motion, we propose an Adversarial Refinement Network (ARNet) following a simple yet effective coarse-to-fine mechanism with novel adversarial error augmentation. Specifically, we take both the historical motion sequences and coarse prediction as input of our cascaded refinement network to predict refined human motion and strengthen the refinement network with adversarial error augmentation. During training, we deliberately introduce the error distribution by learning through the adversarial mechanism among different subjects. In testing, our cascaded refinement network alleviates the prediction error from the coarse predictor resulting in a finer prediction robustly. This adversarial error augmentation provides rich error cases as input to our refinement network, leading to better generalization performance on the testing dataset. We conduct extensive experiments on three standard benchmark datasets and show that our proposed ARNet outperforms other state-of-the-art methods, especially on challenging aperiodic actions in both short-term and long-term predictions. © 2021, Springer Nature Switzerland AG.

关键词： Errors

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Texture image segmentation using Brushlet-domain hidden Markov models

Texture image segmentation using Brushlet-domain hidden Mark...

引用

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

作者： Liu, Fang Yang, Kai Hao, Hongxia Hou, Biao Zhong, Hua School of Computer Science and Technology Xidian University Xi'an Shanxi 710071 China Intelligent Perception and Image Understanding Key Lab. of Ministry of Education of China Xi'an Shanxi 710071 China Intelligent Information Processing Laboratory Xidian University Xi'an Shanxi 710071 China

ISBN: (纸本)9780819478092

By researching the Brushlet domain coefficients of texture images, we found that the distribution of the magnitudes of Brushlet domain coefficients roughly meet rayleigh distribution. And there are correlations between Brushlet coefficients in adjacent scales. Therefore, Rayleigh Mixture Model (RMM) is used to characterize the statistics of the magnitudes of Brushlet coefficients. To capture the inter-scale persistence of Brushlet coefficients, a "four to four" models with markov property is adopted in this paper. On the basis, by combining with the multi-scale Bayesian segmentation method, we propose a multiscale Bayesian texture segmentation algorithm that is based on a Brushlet domain hidden Markov tree (BruHMT) model. The experiment results indicate that our method is feasible and effective. Especially for coarse texture, our method is superior than texture segmentation method using Wavelet domain hidden Markov tree (WHMT) model. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Hidden Markov models

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Computer Aided Diagnosis of Fatty Liver Ultrasonic images Based on Support Vector Machine

Computer Aided Diagnosis of Fatty Liver Ultrasonic Images Ba...

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30th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society

作者： Li, Guokuan Luo, Yu Deng, Wei Xu, Xiangyang Liu, Aihua Song, Enmin Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan 430074 Peoples R China Minist Educ Key Lab Image Processing & Intelligent Control Wuhan 430074 Peoples R China Sixth Hosp Ultrasound Dept Wuhan Peoples R China

ISBN: (纸本)9781424418145

B-scan ultrasound is the primary means for the diagnosis of fatty liver. However, due to use of various ultrasound equipments, poor quality of ultrasonic images and physical differences of patients, fatty liver diagnosis is mainly qualitative, and often depends on the subjective judgment of technicians and doctors. Therefore, computer-aided feature extraction and quantitative analysis of liver B-scan ultrasonic images will help to improve clinical diagnostic accuracy, repeatability and efficiency, and could provide a measure for severity of hepatic steatosis. This paper proposed a novel method of fatty liver diagnosis based on liver B-mode ultrasonic images using support vector machine (SVM). Fatty liver diagnosis was transformed into a pattern recognition problem of liver ultrasound image features. According to the different characteristics of fatty liver and healthy liver, important image features were extracted and selected to distinguish between the two categories. These features could be represented by near-field light-spot density, near-far-field grayscale ratio, grayscale co-occurrence matrix, and neighborhood gray-tone difference matrix (NGTDM). A SVM classifier was modeled and trained using the clinical ultrasound images of both fatty liver and normal liver. It was then exploited to classify normal and fatty livers, achieving a high recognition rate. The diagnostic results are satisfactorily consistent with those made by doctors. This method could be used for computer-aided diagnosis of fatty liver, and help doctors identify the fatty liver ultrasonic images rapidly, objectively and accurately.

关键词： fatty liver B-scan ultrasound grayscale co-occurrence matrix SVM feature extraction

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