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6th IEEE International Conference on computer and Communications, ICCC 2020

作者： Li, Haixing Luo, Haibo The Key Lab of Image Understanding and Computer Vision Shenyang City Liaoning Province China

ISBN: (纸本)9781728186351

In medical images, the observer's manual description of different structures is very different, and it spans a wide range of various structures and pathologies. This variability (which is a characteristic of biological issues, imaging modality and expert annotators) has not been fully considered in the design of computer algorithms for medical image quantification. So far, few people predict the uncertainty of medical image segmentation. In this paper, we designed a V-shaped network to quantify the uncertainty in prostate MRI image segmentation. We have embedded a feature pyramid attention module in the backbone network, which can extract high-level semantic context information at different scales and provide a pixel-level attention to the decoder. At the same time, the module will not bring a large computational burden. In our experiments, we tested the performance of the proposed method on 55 clinical subjects. © 2020 IEEE.

关键词： Magnetic resonance imaging

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SAR image despeckling based on edge detection and nonsubsampled second generation bandelets

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Journal of Systems Engineering and Electronics 2009年第3期20卷 519-526页

作者： Zhang Wenge~(1,2),Liu Fang~(1,2),Jiao Licheng~(2,3)& Gao Xinbo~(2,3) 1.School of computer Science and Technology,Xidian Univ.,Xi’an 710071,P.R.China 2.key lab.of Intelligent Perception and image understanding of Ministry of Education of China,Xi’an 710071,P.R.China 3.Inst,of Intelligent Information Processing,Xidian Univ.,Xi’an 710071,P.R.China School of Computer Science and Technology Xidian Univ. Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Key Lab. of Intelligent Perception and Image Understanding of Ministry of Education of China Xi'an 710071 P. R. China Inst. of Intelligent Information Processing Xidian Univ. Xi'an 710071 P. R. China

To preserve the sharp features and details of the synthetic aperture radar （SAR） image effectively when despeckling, a despeckling algorithm with edge detection in nonsubsampled second generation bandelet transform （NSBT） domain is proposed. First, the Canny operator is utilized to detect and remove edges from the SAR image. Then the NSBT which has an optimal approximation to the edges of images and a hard thresholding rule are used to approximate the details while despeckling the edge-removed image. Finally, the removed edges are added to the reconstructed image. As the edges axe detected and protected, and the NSBT is used, the proposed algorithm reaches the state-of-the-art effect which realizes both despeckling and preserving edges and details simultaneously. Experimental results show that both the subjective visual effect and the mainly objective performance indexes of the proposed algorithm outperform that of both Bayesian wavelet shrinkage with edge detection and Bayesian least square-Gaussian scale mixture （BLS-GSM）.

关键词： computer image processing synthetic aperture radar speckle edge detection nonsubsampled second generation bandelet transform Canny operator threshold shrinkage.

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Static human behavior classification based on LLC features and GIST features 7

Static human behavior classification based on LLC features a...

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7th IEEE Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems, CYBER 2017

作者： Ende, Wang Xukui, Hou Xuepeng, Li Shenyang Institute of Automation Chinese Academic of Sciences Key Lab of Image Understanding and Computer Vision Key Laboratory of Optical Electrical Image Processing Shenhe District Shenyang Liaoning China

ISBN: (纸本)9781538604892

This paper presents a method for recognizing human behavior in static images based on LLC and GIST features. The feature points in the image is densely located in sub-region of images and we extract SIFT feature from each sub-region. Then using the LLC method to encode the extracted dense SIFT features of each sub-region and each feature point descriptor is assigned to several nearest words, so that each descriptor can be expressed by the linear correlation coefficient of vocabulary. In order to increase the spatial information, we use the SPM model to divide the image into many blocks under different levels. We can get the pooled feature from each sub-region using the max pooling method. Then these pooled features from sub-region are concatenated and normalized as the final pooled features. In order to get GIST features, we divide the image into blocks and construct the Gabor filter group of 32 different scales and directions. Then each Gabor filter in the Gabor filter group does convolution operation with image blocks and we take the mean value of each blocks under each Gabor filter as the feature description. The features from each block under each Gabor filter are concatenated and normalized as the final GIST feature representation of the image. Then concatenating the final pooled features and the GIST features and take as the final feature representation. Finally, the intersection kernel function of SVM is used for classification. We performed experiments on several databases. By contrast, this algorithm has a good effect in accuracy and efficiency. © 2017 IEEE.

关键词： Gabor filters

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End-to-end, single-stream temporal action detection in untrimmed videos 28

End-to-end, single-stream temporal action detection in untri...

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28th British Machine vision Conference, BMVC 2017

作者： Buch, Shyamal Escorcia, Victor Ghanem, Bernard Fei-Fei, Li Niebles, Juan Carlos Stanford Vision and Learning Lab. Dept. of Computer Science Stanford University United States Image and Video Understanding Lab. Visual Computing Center KAUST Saudi Arabia

ISBN: (纸本)190172560X

In this work, we present a new intuitive, end-to-end approach for temporal action detection in untrimmed videos. We introduce our new architecture for Single-Stream Temporal Action Detection (SS-TAD), which effectively integrates joint action detection with its semantic sub-tasks in a single unifying end-to-end framework. We develop a method for training our deep recurrent architecture based on enforcing semantic constraints on intermediate modules that are gradually relaxed as learning progresses. We find that such a dynamic learning scheme enables SS-TAD to achieve higher overall detection performance, with fewer training epochs. By design, our single-pass network is very efficient and can operate at 701 frames per second, while simultaneously outperforming the state-of-the-art methods for temporal action detection on THUMOS’14. © 2017. The copyright of this document resides with its authors.

关键词： Semantics

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Uncertainty Quantification in Medical image Segmentation

Uncertainty Quantification in Medical Image Segmentation

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International Conference on computer and Communications (ICCC)

作者： Haixing Li Haibo Luo The Key Lab of Image Understanding and Computer Vision Shenyang City Liaoning Province China

ISBN: (数字)9781728186351

ISBN: (纸本)9781728186368

关键词： image segmentation Uncertainty Magnetic resonance imaging Semantics Feature extraction Prediction algorithms Biomedical imaging

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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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Fast Candidate Region Extraction for SAR Ship Target 37

Fast Candidate Region Extraction for SAR Ship Target

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37th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2022

作者： Zhang, Panpan Luo, Haibo Xu, Zheng He, Miao Shenyang Institute of Automation Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Opto-Electronic Information Processing Shenyang110016 China The Key Lab of Image Understanding and Computer Vision Shenyang110016 China

ISBN: (纸本)9781665465366

At present, deep learning technology is widely used in ship target detection in synthetic aperture radar (SAR) images. However, high-resolution remote sensing SAR images cover a larger area and have larger image sizes. To be able to use the deep learning model for training and testing, the image needs to be cropped to the appropriate size. In a high-resolution SAR ship image, the ship target usually takes only a small part of the whole image. As a result, only part of the cropped image contains the target, and most of the rest are background regions. This will cause a lot of computational redundancy in the model inference stage. To solve this problem, a fast candidate region extraction algorithm is proposed in this paper for ship target extraction. The algorithm consists of three parts: firstly, extract the saliency map of the SAR ship image, secondly, perform median filtering on the segmented image, and thirdly, extract candidate regions. The superiority of the algorithm was demonstrated by experiments on the AIR-SARShip-1.0 dataset. © 2022 IEEE.

关键词： Synthetic aperture radar

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Texture image segmentation using Brushlet-domain hidden Markov models

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

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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, 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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Global Convergence Guarantees of (A)GIST for a Family of Nonconvex Sparse Learning Problems

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IEEE Transactions on Cybernetics 2022年第5期52卷 3276-3288页

作者： Zhang, Hengmin Qian, Feng Shang, Fanhua Du, Wenli Qian, Jianjun Yang, Jian East China University of Science and Technology Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education School of Information Science and Engineering Shanghai200237 China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai200092 China Xidian University Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xi'an710071 China Peng Cheng Laboratory Shenzhen518066 China Nanjing University of Science and Technology Pca Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China

In recent years, most of the studies have shown that the generalized iterated shrinkage thresholdings (GISTs) have become the commonly used first-order optimization algorithms in sparse learning problems. The nonconvex relaxations of the \ell _{0} -norm usually achieve better performance than the convex case (e.g., \ell _{1} -norm) since the former can achieve a nearly unbiased solver. To increase the calculation efficiency, this work further provides an accelerated GIST version, that is, AGIST, through the extrapolation-based acceleration technique, which can contribute to reduce the number of iterations when solving a family of nonconvex sparse learning problems. Besides, we present the algorithmic analysis, including both local and global convergence guarantees, as well as other intermediate results for the GIST and AGIST, denoted as (A)GIST, by virtue of the Kurdyka-Lojasiewica (KL) property and some milder assumptions. Numerical experiments on both synthetic data and real-world databases can demonstrate that the convergence results of objective function accord to the theoretical properties and nonconvex sparse learning methods can achieve superior performance over some convex ones. © 2013 IEEE.

关键词： Optimization

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Autoencoder-Based Latent Block-Diagonal Representation for Subspace Clustering

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IEEE Transactions on Cybernetics 2022年第6期52卷 5408-5418页

作者： Xu, Yesong Chen, Shuo Li, Jun Han, Zongyan Yang, Jian Nanjing University of Science and Technology PCA Laboratory Key Lab. of Intelligent Percept. and Syst. for High-Dimensional Information of Ministry of Education Nanjing210094 China Nanjing University of Science and Technology Jiangsu Key Laboratory of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing210094 China RIKEN Center for Advanced Intelligence Project Tokyo103-0027 Japan Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

Block-diagonal representation (BDR) is an effective subspace clustering method. The existing BDR methods usually obtain a self-expression coefficient matrix from the original features by a shallow linear model. However, the underlying structure of real-world data is often nonlinear, thus those methods cannot faithfully reflect the intrinsic relationship among samples. To address this problem, we propose a novel latent BDR (LBDR) model to perform the subspace clustering on a nonlinear structure, which jointly learns an autoencoder and a BDR matrix. The autoencoder, which consists of a nonlinear encoder and a linear decoder, plays an important role to learn features from the nonlinear samples. Meanwhile, the learned features are used as a new dictionary for a linear model with block-diagonal regularization, which can ensure good performances for spectral clustering. Moreover, we theoretically prove that the learned features are located in the linear space, thus ensuring the effectiveness of the linear model using self-expression. Extensive experiments on various real-world datasets verify the superiority of our LBDR over the state-of-the-art subspace clustering approaches. © 2013 IEEE.

关键词： Decoding

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