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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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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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Constructing multi-modality and multi-classifier radiomics predictive models through reliable classifier fusion

arXiv

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arXiv 2017年

作者： Zhou, Zhiguo Zhou, Zhi-Jie Hao, Hongxia Li, Shulong Chen, Xi Zhang, You Folkert, Michael Wang, Jing Department of Radiation Oncology UT Southwestern Medical Center DallasTX United States High-Tech Institute of Xi'an Xi'an China Department of Information and Control Engineering Xi'an University of Technology Xi'an China School of Computer Science and Technology Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University Xi'an China School of Biomedical Engineering Southern Medical Unversity Guangzhou China Institute of Image Processing and Pattern Recognition Xi'an Jiaotong Unversity Xi'an China

Radiomics aims to extract and analyze large numbers of quantitative features from medical images and is highly promising in staging, diagnosing, and predicting outcomes of cancer treatments. Nevertheless, several challenges need to be addressed to construct an optimal radiomics predictive model. First, the predictive performance of the model may be reduced when features extracted from an individual imaging modality (e.g. PET, CT, MRI) are blindly combined into a single predictive model. Second, because many different types of classifiers are available to construct a predictive model, selecting an "optimal" classifier for a particular application is still challenging. In this work, we developed multi-modality and multi-classifier radiomics predictive models that address the aforementioned issues in currently available models. Specifically, a new reliable classifier fusion strategy was proposed to optimally combine output from different modalities and classifiers. In this strategy, modality-specific classifiers were first trained, and an analytic evidential reasoning (ER) rule was developed to fuse the output score from each modality to construct an optimal predictive model. One public datasets and two clinical case studies were performed to validate model performance. The experimental results indicated that the proposed ER rule based radiomics models outperformed the traditional models that rely on a single classifier or simply use combined features from different modalities. Copyright © 2017, The Authors. All rights reserved.

关键词： Diagnosis

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Research advances on sparse cognitive learning, computing and recognition

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Jisuanji Xuebao/Chinese Journal of computers 2016年第4期39卷 835-852页

作者： Jiao, Li-Cheng Zhao, Jin Yang, Shu-Yuan Liu, Fang Xie, Wen Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Xidian University Xi'an710071 China School of Computer Science and Technology Xidian University Xi'an710071 China

Sparse Cognitive Learning, Computing and Recognition is a widespread concern front filed by international academic circles in recent years, this new paradigm will have be a revolutionary impact on the fields of machine learning, pattern recognition, computational intelligence,big data etc. In order to better grasp the law of development, three forms of this new paradigm, such as sparse coding, structured sparse and hierarchy sparse, are reviewed in detail from the perspective of biological visual cognitive mechanism and model structure in this paper. In the meantime, outlined the application research of this computational model and exemplified some concrete achievements. Furthermore, some existing problems and research directions are discussed by combining with the current application research. In order to arouse interested researcher pay more attention to this new paradigm. © 2016, Science Press. All right reserved.

关键词： Artificial intelligence

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SAR image Despeckling Using Scale Mixtures of Gaussians in the Nonsubsampled Contourlet Domain

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Chinese Journal of Electronics 2015年第1期24卷 205-211页

作者： CHANG Xia JIAO Licheng LIU Fang SHA Yuheng Institute of Image Processing and Understanding School of Mathematics and Information Science Beifang University of Nationalities Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China International Research Center for Intelligent Perception and Computation Xidian University School of Computer Science and Technology Xidian University

The edge and contour details in SAR images are important for subsequent processing tasks. The multiscale geometric analysis method — Nonsubsampled contourlet transform(NSCT) is able to capture the geometric information of SAR images effectively. Describing the aggregation behavior of the neighborhoods coefficients, the scale mixtures of Gaussians model has exhibited favorable performances. A novel SAR image despeckling method is presented by constructing the scale mixtures of Gaussians model of NSCT. This method models the SAR images using the multiscale and multidirection information in NSCT domain. The dependency relationship of NSCT neighborhoods coefficients are also taken into consideration in our model. The speckle noise coefficients are shrinkaged by statistical prior estimation based on SAR image model constructed. Experimental results demonstrate that our method is advantageous at directional information preservation and the speckle restraint.

关键词： Synthetic aperture radar(SAR) image despeckling Stationary wavelet transform Nonsubsampled contourlet transform Scale mixtures of Gaussians model.

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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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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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Consensus algorithms for biased labeling in crowdsourcing

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Information Sciences 2017年 382-383卷 254-273页

作者： Jing Zhang Victor S. Sheng Qianmu Li Jian Wu Xindong Wu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing 210094 P. R. China Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing 210094 P. R. China Department of Computer Science University of Central Arkansas Conway AR 72035 USA Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing 210044 P. R. China Institute of Intelligent Information Processing and Application Soochow University Suzhou 215006 P. R. China School of Computing and Informatics University of Louisiana at Lafayette LA 70504 USA

Although it has become an accepted lay view that when labeling objects through crowdsourcing systems, non-expert annotators often exhibit biases, this argument lacks sufficient evidential observation and systematic empirical study. This paper initially analyzes eight real-world datasets from different domains whose class labels were collected from crowdsourcing systems. Our analyses show that biased labeling is a systematic tendency for binary categorization; in other words, for a large number of annotators, their labeling qualities on the negative class (supposed to be the majority) are significantly greater than are those on the positive class (minority). Therefore, the paper empirically studies the performance of four existing EM-based consensus algorithms , DS, GLAD, RY, and ZenCrowd, on these datasets. Our investigation shows that all of these state-of-the-art algorithms ignore the potential bias characteristics of datasets and perform badly although they model the complexity of the systems. To address the issue of handling biased labeling, the paper further proposes a novel consensus algorithm, namely adaptive weighted majority voting (AWMV), based on the statistical difference between the labeling qualities of the two classes. AWMV utilizes the frequency of positive labels in the multiple noisy label set of each example to obtain a bias rate and then assigns weights derived from the bias rate to negative and positive labels. Comparison results among the five consensus algorithms (AWMV and the four existing) show that the proposed AWMV algorithm has the best overall performance. Finally, this paper notes some potential related topics for future study.

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Violent Interaction Detection in Video Based on Deep Learning

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

作者： Peipei Zhou Qinghai Ding Haibo Luo Xinglin Hou Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 University of Chinese Academy of Sciences Beijing 100049 Key Laboratory of Opto-Electronic Information Processing CAS Shenyang 110016 The Key Lab of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 Space star technology co. LTD Beijing 100086

Violent interaction detection is of vital importance in some video surveillance scenarios like railway stations, prisons or psychiatric centres. Existing vision-based methods are mainly based on hand-crafted features such as statistic features between motion regions, leading to a poor adaptability to another dataset. En lightened by the development of convolutional networks on common activity recognition, we construct a FightNet to represent the complicated visual violence interaction. In this paper, a new input modality, image acceleration field is proposed to better extract the motion attributes. Firstly, each video is framed as RGB images. Secondly, optical flow field is computed using the consecutive frames and acceleration field is obtained according to the optical flow field. Thirdly, the FightNet is trained with three kinds of input modalities, i.e., RGB images for spatial networks, optical flow images and acceleration images for temporal networks. By fusing results from different inputs, we conclude whether a video tells a violent event or not. To provide researchers a common ground for comparison, we have collected a violent interaction dataset (VID), containing 2314 videos with 1077 fight ones and 1237 no-fight ones. By comparison with other algorithms, experimental results demonstrate that the proposed model for violent interaction detection shows higher accuracy and better robustness.

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Shell feature: a new radiomics descriptor for predicting distant failure after radiotherapy in non-small cell lung cancer and cervix cancer

arXiv

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arXiv 2017年

作者： Hao, Hongxia Zhou, Zhiguo Li, Shulong Maquilan, Genevieve Folkert, Michael R. Iyengar, Puneeth Westover, Kenneth D. Albuquerque, Kevin Liu, Fang Choy, Hak Timmerman, Robert Yang, Lin Wang, Jing School of Computer Science and Technology Xidian University Xi'an710071 China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education International Research Center for Intelligent Perception and Computation Joint International Research Laboratory of Intelligent Perception and Computation Xidian University Xi'an710071 China Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75235 United States School of Biomedical Engineering Southern medical university Guangzhou510515 China Department of Pathology National Cancer Center/Cancer Hospital Chinese Academy of Medical Sciences Beijing100021 China

Purpose To develop and demonstrate a novel tumor shell feature for predicting distant failure in non-small cell lung cancer (NSCLC) and cervical cancer (CC) patients. Patients and Methods The shell predictive model was constructed using pre-treatment positron emission tomography (PET) images from 48 NSCLC patients received stereotactic body radiation therapy (SBRT) and 52 CC patients underwent external beam radiation therapy and concurrent chemotherapy followed with high-dose-rate intracavitary brachytherapy. A shell feature, consisting of outer voxels around the tumor boundary, was extracted from a series of axial PET slices. The hypothesis behind this feature is that non-invasive and invasive tumors may have different morphologic patterns in the tumor periphery, in turn reflecting the differences in radiological presentations in the PET images. The shell’s utility was evaluated by the support vector machine (SVM) classifier in comparison with intensity, geometry, gray level co-occurrence matrix (GLCM)-based texture, neighborhood gray tone difference matrix (NGTDM)-based texture, and a combination of these four features. The results were assessed in terms of accuracy, sensitivity, specificity, and the area under the receiver operating curve (AUC). Results For NSCLC, the AUC achieved by the shell feature was 0.82 while the highest AUC achieved by the other features was 0.76. Similarly, for CC, the AUC achieved by the shell feature was 0.83 while the highest AUC achieved by the other features was 0.76. Also, the difference in performance between shell and the other features was significant (P Copyright © 2017, The Authors. All rights reserved.

关键词： Radiotherapy

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