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IEEE Congress on Evolutionary Computation

作者： Bingxin Xu Ping Guo Beijing Key Laboratory of Information Service Engineering Beijing Union University Beijing China Image Processing and Pattern Recognition Laboratory Beijing Normal University Beijing 100875 China

Sparse autoencoder is one approach to automatically learn features from unlabeled data and received significant attention during the development of deep neural networks. However, the learning algorithm of sparse autoencoder suffers from slow learning speed because of gradient descent based algorithms have many drawbacks. In this paper, a fast learning algorithm for sparse autoenceder is proposed which based on pseudoinverse learning algorithm (PIL). The proposed method calculates encoder weight matrix by truncating the pseudoinverse matrix of input data. The pseudoinverse truncation matrix is used as the weights of encoder, and then the input data is mapped to the hidden layer space through the biased ReLU activation function. The decoder weights are also can computed by the PIL. Unlike the gradient descent based algorithm, the proposed method does not require a time-consuming iterative optimization process and select many user-dependent parameters such as learning rate or momentum constant too. The experimental results indicate the superiority of proposed method which is very efficient and also can learned the sparsity of samples.

关键词： sparse autoencoder pseudoinverse learning algorithm fast learning data representation

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Maneuvering target tracking algorithm based on CDKF in observation bootstrapping strategy

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High Technology Letters 2017年第2期23卷 149-155页

作者：胡振涛 Zhang Jin Fu Chunling Li Xian Instituteof Image Processing and Pattern Recognition Henan University Kaifeng 475004 P. R. China School of Physics and Electronics Henan University Kaiteng 475004 P. R. Ghina

The selection and optimization of model filters affect the precision of motion pattern identification and state estimation in maneuvering target tracking *** at improving performance of model filters,a novel maneuvering target tracking algorithm based on central difference Kalman filter in observation bootstrapping strategy is *** framework of interactive multiple model(IMM) is used to realize identification of motion pattern,and a central difference Kalman filter(CDKF) is selected as the model filter of *** the advantage of multi-sensor fusion method in improving the stability and reliability of observation information,the hardware cost of the observation system for multiple sensors is adopted,meanwhile,according to the data assimilation technique in Ensemble Kalman filter(En KF),a bootstrapping observation set is constructed by integrating the latest observation and the prior information of observation *** that basis,these bootstrapping observations are reasonably used to optimize the filtering performance of CDKF by means of weight fusion *** object of new algorithm is to improve the tracking precision of observed target by the multi-sensor fusion method without increasing the number of physical *** theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： maneuvering target tracking interacting multiple model(IMM) central difference Kalman filter(CDKF) bootstrapping observation

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A New Method of Shredded Paper image Stitching and Restoration 3

A New Method of Shredded Paper Image Stitching and Restorati...

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3rd International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration, ICIICII 2017

作者： Pan, Zixiao Wang, Mei School of Automation Wuhan University of Technology Wuhan China Laboratory of Image Processing and Pattern Recognition Yantai Vocational College Yan China

ISBN: (纸本)9781538624340

For the image restoration problem of shredded paper broken by shredder with the same marginal feature, a new method based on spatial feature classification is proposed in this paper. Because no obvious marginal feature for rule shredder paper can be used to split, the feature similarity classification processing can greatly reduce the solution scale and improve the algorithm efficiency. The horizontal projection position of shredded paper image can be used to distinguish the same line for the shredded paper with words. Then the marginal moment invariants distance are defined by the left and right part spatial correlation information. The original order of adjacent shredded paper in the same line can be ensured by the minimum marginal distance. Similarly ordering up and down can be completed by the minimum marginal distance among up-down part stitching images. For the image stitching and restoration of large-scale rule shredded paper, the actual experimental result shows the good stability, efficiency and strong robustness. © 2017 IEEE.

关键词： Restoration

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Super-resolution reconstruction of color image based on microarray lens

Super-resolution reconstruction of color image based on micr...

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2017 IEEE International Conference on Applied System Innovation, ICASI 2017

作者： Zou, Jiancheng Zhang, Honggen Institute of Image Processing and Pattern Recognition North China University of Technology No. 5 Jinyuanzhuang Road Shijingshan District Beijing China

ISBN: (纸本)9781509048977

In this paper, we propose a novel method which combines with color microarray cameras and super-resolution algorithms. Compared with ordinary cameras, the array camera can capture more details of the image, so better quality low resolution images can be obtained. Array camera has been a great application in depth map extraction and image super-resolution reconstruction [3,4,5]. Pelican and Columbia University present Picam(4 × 4 microarray camera) synthesizes high resolution images along with a range monochrome image[3]. The images produced by most popular camera is color images, so the super resolution algorithm of the Picam can't be applied directly. According to the symmetry of the lens, we use 3 × 3 microarray light field camera which can capture color images. The color images component is extracted for super-resolution reconstruction. The algorithm more efficient compared with the traditional super-resolution algorithm. The advantages of hardware and algorithms can be applied to mobile devices such as mobile phone cameras. © 2017 IEEE.

关键词： Cameras

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Face recognition with Convolutional Neural Networks and subspace learning 2

Face Recognition with Convolutional Neural Networks and subs...

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2nd International Conference on image, Vision and Computing, ICIVC 2017

作者： Wan, Lihong Liu, Na Huo, Hong Tao, Fang Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China

ISBN: (纸本)9781509062379

Deep learning is widely used in computer vision. In this study, we present a new method based on Convolutional Neural Networks (CNN) and subspace learning for face recognition under two circumstances. A very deep CNN architecture called VGG-Face, which learned on a large scale database, is used as feature extractor to extract the activation vector of the fully connected layer in the CNN architecture. Then, two types of subspace learning methods, namely, linear discriminate analysis (LDA) and whitening principal component analysis (WPCA), are respectively introduced to learn the subspace of the activation vectors for face recognition under multiple samples per subject and single sample per subject circumstances. The goals of applying subspace learning to the activation vectors are obtaining compact representation (dimensionality reduction) and performance improvement. Experiments on two face databases (CMU PIE and FERET) demonstrate the effectiveness of VGG-Face + LDA and VGG-Face + WPCA, compared with state-of-the-art methods. © 2017 IEEE.

关键词： Principal component analysis

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

arXiv

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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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Distributed cubature Kalman filter based on observation bootstrap sampling

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High Technology Letters 2016年第2期22卷 142-147页

作者：胡振涛 Hu Yumei Zheng Shanshan Li Xian Guo Zhen Institute of Image Processing and Pattern Recognition Henan University

Aiming at the adverse effect caused by observation noise on system state estimation precision,a novel distributed cubature Kalman filter(CKF) based on observation bootstrap sampling is ***,combining with the extraction and utilization of the latest observation information and the prior statistical information from observation noise modeling,an observation bootstrap sampling strategy is *** objective is to deal with the adverse influence of observation uncertainty by increasing observations ***,the strategy is dynamically introduced into the cubature Kalman filter,and the distributed fusion framework of filtering realization is *** filtering precision is obtained by promoting observation reliability without increasing the hardware cost of observation *** analysis and simulation results show the proposed algorithm feasibility and effectiveness.

关键词： state estimation cubature Kalman filter （CKF） observation bootstrap sampling distributed weighted fusion

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CNN-based invertible wavelet scattering for the investigation of diffusion properties of the in vivo human heart in diffusion tensor imaging

arXiv

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arXiv 2019年

作者： Deng, Zeyu Wang, Lihui Kuai, Zixiang Chen, Qijian Cheng, Xinyu Yang, Feng Yang, Jie Zhu, Yuemin Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province College of Computer Science and Technology Guizhou University Guiyang550025 China Imaging Center Harbin Medical University Cancer Hospital Harbin150081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai200240 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 LyonF-69621 France

In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium structures in DTI with free-breathing acquisitions. The method is based on an invertible Wavelet Scattering achieved by means of Convolutional Neural Network (WSCNN). It consists of first extracting translation-invariant wavelet scattering features from DW images acquired at different trigger delays and then mapping the fused scattering features into motion-compensated spatial DW images by performing an inverse wavelet scattering transform achieved using CNN. The results on both simulated and acquired in vivo cardiac DW images showed that the proposed WSCNN method effectively compensates for motion-induced signal loss and produces in vivo cardiac DW images with better quality and more coherent fiber structures with respect to existing methods, which makes it an interesting method for measuring correctly the diffusion properties of the in vivo human heart in DTI under free breathing. Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion tensor imaging

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MMFNet: A Multi-modality MRI Fusion Network for Segmentation of Nasopharyngeal Carcinoma

arXiv

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arXiv 2018年

作者： Chen, Huai Qi, Yuxiao Yin, Yong Li, Tengxiang Liu, Xiaoqing Li, Xiuli Gong, Guanzhong Wang, Lisheng Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shandong Cancer Hospital Affiliated to Shandong University Jinan250117 China Deepwise AI Lab China

Segmentation of nasopharyngeal carcinoma (NPC) from Magnetic Resonance images (MRI) is a crucial prerequisite for NPC radiotherapy. However, manually segmenting of NPC is time-consuming and labor-intensive. Additionally, single-modality MRI generally cannot provide enough information for its accurate delineation. Therefore, a multi-modality MRI fusion network (MMFNet), which is a novel framework to fuse information from multi-modality medical images, is proposed to utilize MRI of T1, T2 and contrast-enhanced T1 to complete accurate segmentation of NPC. The backbone of MMFNet is designed as a multi-encoder-based network, consisting of several encoders to capture modality-specific features and one decoder to obtain fused features for NPC segmentation. A fusion block is presented to effectively fuse multi-source features. It contains a 3D Convolutional Block Attention Module (3D-CBAM), recalibrating low-level features captured from modality-specific encoders to highlight both informative features and regions of interest (ROIs), and a residual fusion block (RFBlock), which fuses re-weighted features to keep balance between fused ones and high-level features from decoder. Moreover, in order to make full mining of individual information from multi-modality MRI, a training strategy named self-transfer is proposed to utilize pre-trained modality-specific encoders to initialize multi-encoder-based network. The proposed method based on multi-modality MRI can effectively segment NPC and its advantages are validated by extensive experiments. Copyright © 2018, The Authors. All rights reserved.

关键词： Convolution

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Author Correction: Microridge-like structures anchor motile cilia

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Nature communications 2024年第1期15卷 8252页

作者： Takayuki Yasunaga Johannes Wiegel Max D Bergen Martin Helmstädter Daniel Epting Andrea Paolini Özgün Çiçek Gerald Radziwill Christina Engel Thomas Brox Olaf Ronneberger Peter Walentek Maximilian H Ulbrich Gerd Walz Department of Medicine IV University Freiburg Medical Center Faculty of Medicine University of Freiburg Hugstetter Strasse 55 79106 Freiburg Germany. Faculty of Biology University of Freiburg Schaenzlestrasse 1 79104 Freiburg Germany. Pattern Recognition and Image Processing Department of Computer Science University of Freiburg Georges-Köhler-Allee 52 79110 Freiburg Germany. BIOSS Centre for Biological Signalling Studies University of Freiburg Schänzlestrasse 18 79104 Freiburg Germany. CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Schänzlestrasse 18 79104 Freiburg Germany. Department of Medicine IV University Freiburg Medical Center Faculty of Medicine University of Freiburg Hugstetter Strasse 55 79106 Freiburg Germany. gerd.walz@uniklinik-freiburg.de. BIOSS Centre for Biological Signalling Studies University of Freiburg Schänzlestrasse 18 79104 Freiburg Germany. gerd.walz@uniklinik-freiburg.de. CIBSS Centre for Integrative Biological Signalling Studies University of Freiburg Schänzlestrasse 18 79104 Freiburg Germany. gerd.walz@uniklinik-freiburg.de.

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