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arXiv 2023年

作者： Kong, Lingtong Jiang, Boyuan Luo, Donghao Chu, Wenqing Tai, Ying Wang, Chengjie Yang, Jie The Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Youtu Lab Tencent Shanghai200233 China The School of Intelligence Science and Technology Nanjing University Suzhou215163 China

Video frame interpolation is an important low-level vision task, which can increase frame rate for more fluent visual experience. Existing methods have achieved great success by employing advanced motion models and synthesis networks. However, the spatial redundancy when synthesizing the target frame has not been fully explored, that can result in lots of inefficient computation. On the other hand, the computation compression degree in frame interpolation is highly dependent on both texture distribution and scene motion, which demands to understand the spatial-temporal information of each input frame pair for a better compression degree selection. In this work, we propose a novel two-stage frame interpolation framework termed WaveletVFI to address above problems. It first estimates intermediate optical flow with a lightweight motion perception network, and then a wavelet synthesis network uses flow aligned context features to predict multi-scale wavelet coefficients with sparse convolution for efficient target frame reconstruction, where the sparse valid masks that control computation in each scale are determined by a crucial threshold ratio. Instead of setting a fixed value like previous methods, we find that embedding a classifier in the motion perception network to learn a dynamic threshold for each sample can achieve more computation reduction with almost no loss of accuracy. On the common high resolution and animation frame interpolation benchmarks, proposed WaveletVFI can reduce computation up to 40% while maintaining similar accuracy, making it perform more efficiently against other state-of-the-arts. Code is available at https://***/ltkong218/WaveletVFI. Copyright © 2023, The Authors. All rights reserved.

关键词： Wavelet transforms

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Two-dimensional multi-fiber spectrum image correction based on machine learning techniques

arXiv

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arXiv 2020年

作者： Xu, Jiali Yin, Qian Guo, Ping Zheng, Xin Image Processing and Pattern Recognition Lab. School of Artificial Intelligence Beijing Normal University Beijing100875 China Image Processing and Pattern Recognition Lab School of Systems Science Beijing Normal University Beijing100875 China

Due to limited size and imperfect of the optical components in a spectrometer, aberration has inevitably been brought into two-dimensional multi-fiber spectrum image in LAMOST, which leads to obvious spacial variation of the point spread functions (PSFs). Consequently, if spatial variant PSFs are estimated directly, the huge storage and intensive computation requirements result in deconvolutional spectral extraction method become intractable. In this paper, we proposed a novel method to solve the problem of spatial variation PSF through image aberration correction. When CCD image aberration is corrected, PSF, the convolution kernel, can be approximated by one spatial invariant PSF only. Specifically, machine learning techniques are adopted to calibrate distorted spectral image, including Total Least Squares (TLS) algorithm, intelligent sampling method, multi-layer feed-forward neural networks. The calibration experiments on the LAMOST CCD images show that the calibration effect of proposed method is effectible. At the same time, the spectrum extraction results before and after calibration are compared, results show the characteristics of the extracted one-dimensional waveform are more close to an ideal optics system, and the PSF of the corrected object spectrum image estimated by the blind deconvolution method is nearly central symmetry, which indicates that our proposed method can significantly reduce the complexity of spectrum extraction and improve extraction accuracy. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical transfer function

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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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A new roadmap constitution method based on dividing grid and level-set

A new roadmap constitution method based on dividing grid and...

引用

International Symposium on intelligence Information processing and Trusted Computing

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Information Engineering of Hubei University for Nationalities Enshi Hubei China Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies HUST Wuhan China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China HUST Wuhan China

ISBN: (纸本)9780769541969

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid . The roadmap constituted with new method can be reused when the start or goal was changed;it is efficient for replanning problem. © 2010 IEEE.

关键词： Reusability

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Evaluating deformation patterns of the thoracic aorta in gated CTA sequences

Evaluating deformation patterns of the thoracic aorta in gat...

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IEEE International Symposium on Biomedical Imaging

作者： Ernst Schwartz Roman Gottardi Johannes Holfeld Christian Loewe Martin Czerny Georg Langs Computational Image Analysis and Radiology Lab (CIR) Medical University of Vienna Austria Pattern Recognition and Image Processing Group University of Technology Vienna Austria Division of Cardiothoracic Surgery Department of Surgery Medical University of Vienna Austria Division of Interventional Radiology Department of Radiology Medical University of Vienna Austria Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology USA

ISBN: (纸本)9781424441259;9781424441266

Cardiovascular interventions in the region of the aortic isthmus such as stent-grafting and vessel transposition introduce substantial changes in the deformation properties of the affected vessels. The changes play a fundamental role in the long-term prognosis for any such treatment, but are only poorly understood to date. We explore a fully automated method to quantify the deformation patterns of the thoracic aorta in gated computed tomography sequences. The aorta is segmented by a level set approach that accurately identifies the vessel lumen in each frame of the sequence. Consequently, landmarks on the vessel wall in each frame are registered using a probabilistic method. This allows for the measurement of global and local deformation properties. We evaluate our method on synthetic datasets and report first results of its application on real world data.

关键词： Biomedical imaging image segmentation Radiology Cardiology Surgery Computed tomography Level set Electrocardiography Pathology Deformable models

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A fast rich information-based stereo matching framework

A fast rich information-based stereo matching framework

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2009 Chinese Conference on Pattern recognition, CCPR 2009 and the 1st CJK Joint Workshop on Pattern recognition, CJKPR

作者： Yuhang, Zhao Junge, Sun Xiao, Bai Yunhong, Wang Intelligence Recognition and Image Processing Lab School of Computer Science and Engineering Beihang University Beijing 100191 China

ISBN: (纸本)9781424441990

With the recent development on image affine region descriptors, we can extract more salient and useful local information from images. That information can be used to help us to better solve a fundamental problem in computer vision - stereo vision. In this paper we propose a framework for stereo matching problems in order to give a rich-information based, high-precision and fast solution. Affine regions based SIFT are chosen as features rather than point features to extract more information. In the matching period, a search algorithm with incremental dissimilarity approximations is used for efficient computing. For correctness, MLESAC(Maximum Likelihood Estimation Sample Consensus) method is used to eliminate outliers. In the experiment part, we evaluate different combinations on the performance of speed, correctness and transformations. ©2009 IEEE.

关键词： Maximum likelihood estimation

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A comparative study of radial basis function neural networks in dynamic clustering algorithm

A comparative study of radial basis function neural networks...

引用

MIPPR 2009 - Pattern recognition and Computer Vision: 6th International Symposium on Multispectral image processing and Pattern recognition

作者： Zhou, Peng Li, Dehua Wu, Hong Zeng, Jun Chen, Feng State Key Lab. for Image Processing and Intelligent Control Institute for Pattern Recognition and Intelligence Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478078

This paper developed two learning procedure, respectively, based on the orthogonal least squares (OLS) method and the "Innovation- Contribution" criterion (ICc) proposed newly. The orthogonal use of the stepwise-regression algorithm of the ICc mages the model structure independent of the selected term sequence and reduces the cluster region further as compared with orthogonal least squares (OLS). as the Bayesian information criteria (BIC) method is incorporate into the clustering process of the ICc, except for the widths of Gaussian functions, it has no other parameter that need tuning ,but the user is required to specify the tolerance ρ, which is relevant to noises and will be difficult to implement in the real system, for the OLS algorithm. The two algorithms are employed to the Radial Basis Function Neural Networks (RBFNN) to compare its performance for different noise nonlinear dynamic systems. Experimental results show that they provide an efficient approximation to the required results for fitting models, but the clustering procedures of the ICc is substantially better solutions than does the OLS. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Radial basis function networks

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Skeleton-based fast path planning for UAV

Skeleton-based fast path planning for UAV

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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, Xin Zhou, Chengping Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478092

A new path planning method for UAV in static workspace is presented. The method can find a nearly optimal path in short time which satisfies the UAV kinematic constraints. The method makes use of the skeletons to construct the graph of the planning space considering the configuration of the obstacles and utilizes the graph to find a shortest collision-free path, and a novel technique is utilized to convert the free path into a feasible path. The method can be applied to different applications and easy to be implemented. Experimental results showed that the path planning can be done in a fraction of second on a contemporary workstation (2-3 seconds) under the condition of satisfying the kinematic constraints. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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A novel path planning method based on path network

A novel path planning method based on path network

引用

MIPPR 2009 - Medical Imaging, Parallel processing of images, and Optimization Techniques: 6th International Symposium on Multispectral image processing and Pattern recognition

作者： Li, Shidong Ding, Mingyue Cai, Chao Institute for Pattern Recognition and Artificial Intelligence National Lab. for Multi-spectral Information Processing Technologies Huazhong University of Science and Technology Wuhan 430074 China Hubei University for Nationalities Enshi Hubei 445000 China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9780819478085

Traditional path planning methods are too slow to meet the real-time requirement in practical applications. In order to solve this problem, an idea of path net was proposed in this paper. The path planning procedure is divided into two steps: network segment planning and segment assembling. The first step was done with Fast Marching Method, including port selection of segment and network segment planning. Second step, the A* searching method was chosen to select segments for assembling. Experiments demonstrated that our method can obtain an optimal route in a few of seconds after the start and goal are given while several minutes are needed for traditional methods. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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Path planning using FMM with direction and curvature constrained

Path planning using FMM with direction and curvature constra...

引用

MIPPR 2009 - Remote Sensing and GIS Data processing and Other Applications: 6th International Symposium on Multispectral image processing and Pattern recognition

ISBN: (纸本)9780819478092

It is difficult to meet both direction and curvature constraints for traditional Fast Marching (FM) method in path planning. Based on adjusting the cost function in Eiknoal equation-the control equation for FM, a new model for computing the integrated cost function was presented in this paper. A relationship formula about curvature radius was obtained and three kinds of adjusting strategies were given;two of them were used to modify the route to meet with the requires of turning constraint in this paper. Experiments showed that the improved model can be used to plan the path with FMM for agent such as unmanned aerial vehicle (UAV) or robot, which is limited to pass through the scene matching areas. And our preliminary experiments demonstrated that the strategies are feasible and efficient to obtain path with certain curvature radius. The model can also be used to represent the problem such as an aircraft flying in a flow field. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Motion planning

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