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arXiv 2020年

作者： Wan, Sheng Gong, Chen Pan, Shirui Yang, Jie Yang, Jian PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Laboratory of Image Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China Faculty of Information Technology Monash University ClaytonVIC3800 Australia

Nowadays, deep learning methods, especially the Graph Convolutional Network (GCN), have shown impressive performance in hyperspectral image (HSI) classification. However, the current GCN-based methods treat graph construction and image classification as two separate tasks, which often results in suboptimal performance. Another defect of these methods is that they mainly focus on modeling the local pairwise importance between graph nodes while lack the capability to capture the global contextual information of HSI. In this paper, we propose a Multi-level GCN with Automatic Graph Learning method (MGCN-AGL) for HSI classification, which can automatically learn the graph information at both local and global levels. By employing attention mechanism to characterize the importance among spatially neighboring regions, the most relevant information can be adaptively incorporated to make decisions, which helps encode the spatial context to form the graph information at local level. Moreover, we utilize multiple pathways for local-level graph convolution, in order to leverage the merits from the diverse spatial context of HSI and to enhance the expressive power of the generated representations. To reconstruct the global contextual relations, our MGCN-AGL encodes the long range dependencies among image regions based on the expressive representations that have been produced at local level. Then inference can be performed along the reconstructed graph edges connecting faraway regions. Finally, the multi-level information is adaptively fused to generate the network output. In this means, the graph learning and image classification can be integrated into a unified framework and benefit each other. Extensive experiments have been conducted on three real-world hyperspectral datasets, which are shown to outperform the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： image classification

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Unsupervised Local Discrimination for Medical images

arXiv

引用

arXiv 2021年

作者： Chen, Huai Wang, Renzhen Wang, Xiuying Li, Jieyu Fang, Qu Li, Hui Bai, Jianhao Peng, Qing Meng, Deyu Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science The University of Sydney SydneyNSW2006 Australia Department of Ophthalmology Shanghai Tenth People’s Hospital Tongji University Shanghai200240 China The Cooperative Medianet Innovation Center Shanghai Jiao Tong University Shanghai200240 China The Changchun GeneScience Pharmaceutical Co. LTD China

Contrastive learning, which aims to capture general representation from unlabeled images to initialize the medical analysis models, has been proven effective in alleviating the high demand for expensive annotations. Current methods mainly focus on instance-wise comparisons to learn the global discriminative features, however, pretermitting the local details to distinguish tiny anatomical structures, lesions, and tissues. To address this challenge, in this paper, we propose a general unsupervised representation learning framework, named local discrimination (LD), to learn local discriminative features for medical images by closely embedding semantically similar pixels and identifying regions of similar structures across different images. Specifically, this model is equipped with an embedding module for pixel-wise embedding and a clustering module for generating segmentation. And these two modules are unified through optimizing our novel region discrimination loss function in a mutually beneficial mechanism, which enables our model to reflect structure information as well as measure pixel-wise and region-wise similarity. Furthermore, based on LD, we propose a center-sensitive one-shot landmark localization algorithm and a shape-guided cross-modality segmentation model to foster the generalizability of our model. When transferred to downstream tasks, the learned representation by our method shows a better generalization, outperforming representation from 18 state-of-the-art (SOTA) methods and winning 9 out of all 12 downstream tasks. Especially for the challenging lesion segmentation tasks, the proposed method achieves significantly better performances. The source codes are publicly available at https://***/HuaiChen-1994/LDLearning. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Learning data-adaptive non-parametric kernels

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2020年第1期21卷 8590-8628页

作者： Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li Department of Electrical Engineering ESAT-STADIUS KU Leuven Belgium Institute of Image Processing and Pattern Recognition Institute of Medical Robotics Shanghai Jiao Tong University Shanghai China PCA Lab Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education School of Computer Science and Engineering Nanjing University of Science and Technology China and Department of Computing Hong Kong Polytechnic University Hong Kong SAR China Department of Automation BNRist Tsinghua University China

In this paper, we propose a data-adaptive non-parametric kernel learning framework in margin based kernel methods. In model formulation, given an initial kernel matrix, a data-adaptive matrix with two constraints is imposed in an entry-wise scheme. Learning this data-adaptive matrix in a formulation-free strategy enlarges the margin between classes and thus improves the model flexibility. The introduced two constraints are imposed either exactly (on small data sets) or approximately (on large data sets) in our model, which provides a controllable trade-off between model flexibility and complexity with theoretical demonstration. In algorithm optimization, the objective function of our learning framework is proven to be gradient-Lipschitz continuous. Thereby, kernel and classifier/regressor learning can be efficiently optimized in a unified framework via Nesterov's acceleration. For the scalability issue, we study a decomposition-based approach to our model in the large sample case. The effectiveness of this approximation is illustrated by both empirical studies and theoretical guarantees. Experimental results on various classification and regression benchmark data sets demonstrate that our non-parametric kernel learning framework achieves good performance when compared with other representative kernel learning based algorithms.

关键词： support vector machines non-parametric kernel learning gradient-Lipschitz continuous

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

引用

arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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A novel panoramic image stitching algorithm based on ORB

A novel panoramic image stitching algorithm based on ORB

引用

2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Wang, Maosen Niu, Shaozhang Yang, Xuan Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications Beijing100876 China Institute of Image Processing and Pattern Recognition North China University of Technology No.5 Jinyuanzhuang Road Shijingshan District Beijing China

ISBN: (纸本)9781509048977

image stitching technique is to integrate multiple images with overlapping regions into a complete image with a wide viewing angle, less distortion, and no obvious suture. image stitching could be used for global positioning and robot autonomous navigation without changing the hardware. SIFT feature and SURF feature are the classical algorithm in the image stitching. But they have the long time-consuming problem. Facing this problem, a novel fast image stitching algorithm based on ORB (Oriented FAST and Rotated BRIEF) features is proposed in this paper. The algorithm firstly selects ORB algorithm which adds the direction information to the FAST detector for image feature extracting and matching. Secondly, RASANC (Random Sample Consensus) algorithm is used to eliminate false matching points. Finally, weighted average method is used to speed up the image fusion. Experimental results show that the stitching effect of the algorithm in this paper is the same as that of SIFT algorithm and SURF algorithm. The two obvious advantages of our algorithm include: (1) stitching speed is much faster than that of Shift and Surf;(2)The algorithm is robust to external parameters of camera. So the proposed algorithm is hopefully applied to some handed electrical devices. © 2017 IEEE.

关键词： image fusion

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BUAA-iCC at imageCLEF 2015 scalable concept image annotation challenge 16

BUAA-iCC at ImageCLEF 2015 scalable concept image annotation...

引用

16th Conference and labs of the Evaluation Forum, CLEF 2015

作者： Wang, Yunhong Chen, Jiaxin Liu, Ningning Zhang, Li Intelligent Recognition and Image Processing Lab Beihang University Beijing100191 China School of Information Technology and Management University of International Business and Economics Beijing100029 China

In this working note, we mainly focus on the image annotation subtask of imageCLEF 2015 challenge that BUAA-iCC research group participated. For this task, we firstly explore textual similarity information between each test sample and predefined concept. Subsequently, two different kinds of semantic information are extracted from visual images: visual tags using generic object recognition classifiers and visual tags relevant to human being related concepts. For the former information, the visual tags are predicted by using deep convolutional neural network (CNN) and a set of support vector machines trained on imageNet, and finally transferred to textual information. For the latter visual information, human related concepts are extracted via face and facial attribute detection, and finally transferred to similarity information by using manually designed mapping rules, in order to enhance the performance of annotating human related concepts. Meanwhile, a late fusion strategy is developed to incorporate aforementioned various kinds of similarity information. Results validate that the combination of the textual and visual similarity information and the adopted late fusion strategy could yield significantly better performance.

关键词： image annotation

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Binary pattern flavored feature extractors for Facial Expression recognition: An overview

Binary pattern flavored feature extractors for Facial Expres...

引用

Proceedings of the International Convention MIPRO

作者： Rasmus Lyngby Kristensen Zheng-Hua Tan Zhanyu Ma Jun Guo Section of Image Analysis and Computer Graphics Technical University of Denmark Kgs. Lyngby Denmark Signal and Information Processing section (SIP) Aalborg University Aalborg Denmark Pattern Recognition and Intelligent System Lab. Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781479981748

This paper conducts a survey of modern binary pattern flavored feature extractors applied to the Facial Expression recognition (FER) problem. In total, 26 different feature extractors are included, of which six are selected for in depth description. In addition, the paper unifies important FER terminology, describes open challenges, and provides recommendations to scientific evaluation of FER systems. Lastly, it studies the facial expression recognition accuracy and blur invariance of the Local Frequency Descriptor. The paper seeks to bring together disjointed studies, and the main contribution is to provide a solid overview for future research.

关键词： Feature extraction Face Face recognition Databases Three-dimensional displays Accuracy Gold

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Benchmarking asymmetric 3D-2D face recognition systems

Benchmarking asymmetric 3D-2D face recognition systems

引用

2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture recognition, FG 2013

作者： Zhao, Xi Zhang, Wuming Evangelopoulos, Georgios Huang, Di Shah, Shishir K. Wang, Yunhong Kakadiaris, Ioannis A. Chen, Liming Computational Biomedicine Lab Department of Computer Science University of Houston Houston TX 77204-3010 United States LIRIS Lab Ecole Centrale de Lyon 69134 cedex France Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781467355452

Asymmetric 3D-2D face recognition (FR) aims to recognize individuals from 2D face images using textured 3D face models in the gallery (or vice versa). This new FR scenario has the potential to be readily deployable in field applications while still keeping the advantages of 3D FR solutions of being more robust to pose and lighting variations. In this paper, we propose a new experimental protocol based on the UHDB11 dataset for benchmarking 3D-2D FR algorithms. This new experimental protocol allows for the study of the performance of a 3D-2D FR solution under pose and/or lighting variations. Furthermore, we also benchmarked two state of the art 3D-2D FR algorithms. One is based on the Annotated Deformable Model (using manually labeled landmarks in this paper) using manually labeled landmarks whereas the other makes use of Oriented Gradient Maps along with an automatic pose estimation through random forest. © 2013 IEEE.

关键词： Face recognition

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pNovo+: De Novo Peptide Sequencing and Assembly Using Complementary HCD and ETD Tandem Mass Spectra

pNovo+: De Novo Peptide Sequencing and Assembly Using Comple...

引用

第八届中国蛋白质组学大会

作者： Hao Chi Haifeng Chen Kun He Long Wu Bing Yang Rui-Xiang Sun Jianyun Liu Wen-Feng Zeng Chun-Qing Song Si-Min He Meng-Qiu Dong Key Lab of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China National Institute of Biological SciencesBeijingBeijing 102206China Laboratory of Intelligent Recognition and Image ProcessingBeijing Key Laboratory of Digital MediaBeihang UniversityBeijing100191China

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Benchmarking asymmetric 3D-2D face recognition systems

Benchmarking asymmetric 3D-2D face recognition systems

引用

International Conference on Automatic Face and Gesture recognition

作者： Xi Zhao Wuming Zhang Georgios Evangelopoulos Di Huang Shishir K. Shah Yunhong Wang Ioannis A. Kakadiaris Liming Chen Computational Biomedicine Lab Department of Computer Science University of Houston Houston TX USA LIRIS Lab Ecole Centrale de Lyon France Laboratory of Intelligent Recognition and Image Processing School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9781467355452

Asymmetric 3D-2D face recognition (FR) aims to recognize individuals from 2D face images using textured 3D face models in the gallery (or vice versa). This new FR scenario has the potential to be readily deployable in field applications while still keeping the advantages of 3D FR solutions of being more robust to pose and lighting variations. In this paper, we propose a new experimental protocol based on the UHDB11 dataset for benchmarking 3D-2D FR algorithms. This new experimental protocol allows for the study of the performance of a 3D-2D FR solution under pose and/or lighting variations. Furthermore, we also benchmarked two state of the art 3D2D FR algorithms. One is based on the Annotated Deformable Model (using manually labeled landmarks in this paper) using manually labeled landmarks whereas the other makes use of Oriented Gradient Maps along with an automatic pose estimation through random forest.

关键词： Face Lighting Probes Pipelines Solid modeling Estimation Face recognition

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