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Improved Kernel Correlation Filter Tracking with Gaussian scale space

Improved Kernel Correlation Filter Tracking with Gaussian sc...

International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.12 Infrared Technology and Application

作者： Shukun Tan Yunpeng Liu Yicui Li Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Key Laboratory of Opto-Electronic Information Processing Chinese Academy of Science The Key Lab of Image Understanding and Computer Vision

Visual tracking is one of the most challenging tasks in the field of computer vision and is related to a wide range of applications like surveillance and robotics. Tracking-by-detection methods are wi

ISBN: (纸本)9781510607729

关键词： visual tracking kernel correlation filter Gaussian scale space bilinear interpolation Mean Absolute Difference visual tracking bilinear interpolation images absolute difference imagery (Psychotherapy) Gauss Scale formation scale parameter Targets image Seeds jigs(templates) scales Scale

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An Improved Real-time Visual Tracking Method for Space Non-cooperate Target

An Improved Real-time Visual Tracking Method for Space Non-c...

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International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.6 Robot Sensing and Advanced Control

作者： Limin Zhang Feng Zhu Yingming Hao Shenyang institute of automation Chinese academy of sciences University of Chinese Academy of Sciences Key laboratory of optical-electronics information processing Chinese academy of science Key laboratory of image understanding and computer vision

In order to enable the non-cooperative rendezvous, capture, and removal of large space debris, robust and fast tracking of the non-cooperative target is needed. This paper proposes an algorithm of rea

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Shape Matching under Affine Transformation Using Normalization and Multi-scale Area Integral Features

Shape Matching under Affine Transformation Using Normalizati...

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International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.6 Robot Sensing and Advanced Control

作者： Huiying Cai Feng Zhu Yingming Hao Rongrong Lu Shenyang institute of automation Chinese academy of sciences University of Chinese Academy of Sciences Key laboratory of optical-electronics information processing Chinese academy of science Key laboratory of image understanding and computer vision

Shape refers to the equivalence representation of objects obtained through projecting the objects onto the imaging planes. The projecting action can be fitted by some finite-dimensional transformation

ISBN: (纸本)9781510607729

关键词： Shape matching Orthogonal normalization Equivalent resample Multi-scale area integral features Affine transformation Area integral shape analysis edge pixel Shape normalization multi-scale

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The correction method of computation of heat flux over dome based on turbulence models

The correction method of computation of heat flux over dome ...

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International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.12 Infrared Technology and Application

作者： ZHANG Daijun LUO Haibo ZHANG Junchao ZHANG Xiangyue Shenyang Institute of Automation Chinese Academy of Science University of Chinese Academy of Science Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision

Aerodynamic heating effect caused by high speed flight of optical guidance system will lead to optical dome fracture failure or thermal barrier effect, which results in the inefficiency of the optical

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Theanalysis and test ofthethermal shock resistance of the Multi-spectralCVD ZnS dome

Theanalysis and test ofthethermal shock resistance of the Mu...

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International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.12 Infrared Technology and Application

作者： ZHANG Daijun LUO Haibo ZHOU Peipei HOU Xinglin Shenyang Institute of Automation Chinese Academy of Science University of Chinese Academy of Science Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision

Optical guidance system start infrared guided mode by throwing away the fairing in the terminal guidance phase. After throwing away the fairing, the infrared dome will be suddenly exposed to the aerod

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Author Correction: Why rankings of biomedical image analysis competitions should be interpreted with care

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Nature communications 2019年第1期10卷 588页

作者： Lena Maier-Hein Matthias Eisenmann Annika Reinke Sinan Onogur Marko Stankovic Patrick Scholz Tal Arbel Hrvoje Bogunovic Andrew P Bradley Aaron Carass Carolin Feldmann Alejandro F Frangi Peter M Full Bram van Ginneken Allan Hanbury Katrin Honauer Michal Kozubek Bennett A Landman Keno März Oskar Maier Klaus Maier-Hein Bjoern H Menze Henning Müller Peter F Neher Wiro Niessen Nasir Rajpoot Gregory C Sharp Korsuk Sirinukunwattana Stefanie Speidel Christian Stock Danail Stoyanov Abdel Aziz Taha Fons van der Sommen Ching-Wei Wang Marc-André Weber Guoyan Zheng Pierre Jannin Annette Kopp-Schneider Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. l.maier-hein@dkfz.de. Division of Computer Assisted Medical Interventions (CAMI) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Centre for Intelligent Machines McGill University Montreal QC H3A0G4 Canada. Christian Doppler Laboratory for Ophthalmic Image Analysis Department of Ophthalmology Medical University Vienna 1090 Vienna Austria. Science and Engineering Faculty Queensland University of Technology Brisbane QLD 4001 Australia. Department of Electrical and Computer Engineering Department of Computer Science Johns Hopkins University Baltimore MD 21218 USA. CISTIB - Center for Computational Imaging & Simulation Technologies in Biomedicine The University of Leeds Leeds Yorkshire LS2 9JT UK. Department of Radiology and Nuclear Medicine Medical Image Analysis Radboud University Center 6525 GA Nijmegen The Netherlands. Institute of Information Systems Engineering TU Wien 1040 Vienna Austria. Complexity Science Hub Vienna 1080 Vienna Austria. Heidelberg Collaboratory for Image Processing (HCI Heidelberg University 69120 Heidelberg Germany. Centre for Biomedical Image Analysis Masaryk University 60200 Brno Czech Republic. Electrical Engineering Vanderbilt University Nashville TN 37235-1679 USA. Institute of Medical Informatics Universität zu Lübeck 23562 Lübeck Germany. Division of Medical Image Computing (MIC) German Cancer Research Center (DKFZ) 69120 Heidelberg Germany. Institute for Advanced Studies Department of Informatics Technical University of Munich 80333 Munich Germany. Information System Institute HES-SO Sierre 3960 Switzerland. Departments of Radiology Nuclear Medicine and Medical Informatics Erasmus MC 3015 GD Rotterdam The Netherlands. Department of Computer Science University of Warwick Coventry CV4 7AL UK. Department of Radiation Oncology Massachusetts General Hospital Boston MA

In the original version of this Article the values in the rightmost column of Table 1 were inadvertently shifted relative to the other columns. This has now been corrected in the PDF and HTML versions of the Article.

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Edge Detection Using Matched Filter 27

Edge Detection Using Matched Filter

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第27届中国控制与决策会议

作者： LUO Hai-bo JIAO An-bo XU Ling-yun SHAO Chun-yan Shenyang Institute of Automation Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Science Key Laboratory of Image Understanding and Computer Vision University of Chinese Academy of Sciences Key Laboratory of Image Understanding and Computer Vision Liaoning Province

ISBN: (纸本)9781479970186

For target tracking,automatic target recognition and detection applications,they value edge detection sensibility,precision and location accuracy rather than other *** at these three criterions,this paper presents an edge detection algorithm based on matched ***,a matched filter was designed by analyzing the edge model for natural image,then the edge response was computed using the designed matched filter;secondly,the lower and discontinuous filtered responses were further suppressed using a dedicated one-dimension filter;finally,the edge image was obtained by binarizing the edge response with a local adaptive *** results illustrate that the proposed algorithm has more improvement than the Sobel and Canny operators in detection sensibility,precision and location ***,the algorithm can be implemented with parallel pipeline using FPGA,so it is also rather suitable for real-time applications.

关键词： edge detection,matched filter,local adaptive threshold,real-time application,noise suppression

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Combining Depth and Gray image for Fast 3D Object Recognition

Combining Depth and Gray Image for Fast 3D Object Recognitio...

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International Symposium on Optoelectronic Technology and Application 2016(OTA 2016)——Conf.5 Optical Measurement Technology and Instrument

作者： Wang Pan Feng Zhu Yingming Hao Optoelectronic Information Technology Laboratory Shenyang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Chinese Academy of Sciences Key Laboratory of Optical-Electronics Information Processing Liaoning Key Laboratory of Image Understanding and Computer Vision

Reliable and stable visual perception systems are needed for humanoid robotic assistants to perform complex grasping and manipulation *** recognition of the object and its precise 6D pose are re

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Dehazed image Quality Assessment by Haze-Line Theory

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Journal of Physics: Conference Series 2017年第1期844卷

作者： Yingchao Song Haibo Luo Rongrong Lu Junkai Ma Shenyang Institute of Automation CAS Shenyang 110016 China University of Chinese Academy of Sciences Beijing 100049 China Key Laboratory of Opto-Electronic Information Processing CAS Shenyang 110016 China The Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 China

images captured in bad weather suffer from low contrast and faint color. Recently, plenty of dehazing algorithms have been proposed to enhance visibility and restore color. However, there is a lack of evaluation metrics to assess the performance of these algorithms or rate them. In this paper, an indicator of contrast enhancement is proposed basing on the newly proposed haze-line theory. The theory assumes that colors of a haze-free image are well approximated by a few hundred distinct colors, which form tight clusters in RGB space. The presence of haze makes each color cluster forms a line, which is named haze-line. By using these haze-lines, we assess performance of dehazing algorithms designed to enhance the contrast by measuring the inter-cluster deviations between different colors of dehazed image. Experimental results demonstrated that the proposed Color Contrast (CC) index correlates well with human judgments of image contrast taken in a subjective test on various scene of dehazed images and performs better than state-of-the-art metrics.

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Blind deconvolution for single noisy and blurry image using alternating maximum a posteriori estimation with low rank prior

Blind deconvolution for single noisy and blurry image using ...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Shijie Sun Huaici Zhao Jinfeng Lv Mingguo Hao Bo Li Key Laboratory of Image Understanding and Computer Vision Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Key Laboratory of Optical-Electronics Information Processing Chinese Academy of Sciences Shenyang China College of Information Shenyang Institute of Engineering Shenyang China

ISBN: (纸本)9781509041008

The purpose of single image blind deconvolution is to estimate the unknown blur kernel from a single observed blurred image and recover the original sharp image. Such task is severely ill-posed and even more challenging especially in the condition that the noise in the input image cannot be negligible. In this paper, the main problem we focus on is how to effectively apply low rank prior to blind deconvolution. A single noisy and blurry image blind deconvolution algorithm is proposed, using alternating maximum a posteriori (MAP) estimation combined with low rank prior. When estimating the intermediate latent image, low rank prior is used as the constraint that is used for noise suppression of the restored image. The denoised intermediate latent image in turn leads to higher quality blur kernel estimation. These two operations are iterated in this manner to arrive at reliable blur kernel estimation. Extensive experiments show the superiority of the proposed method over state-of-the-art techniques, both qualitatively and quantitatively.

关键词： C# languages

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