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11th International Conference on Graphics and image processing (ICGIP)

作者： Zhao, Lei Qiao, Peng Dou, Yong Natl Univ Def Technol Sci & Technol Parallel & Distributed Lab Changsha 410073 Peoples R China

ISBN: (纸本)9781510635241

image set annotation is an important task in the supervised training of the deep neural network. Manual and data-driven dataset annotation methods are commonly used approaches. Both of them have shortcomings, especially in the case of the dataset requiring professional knowledge, which leads to high cost with manual annotation methods and poorly diversified annotation samples with data-driven annotation methods. Although the recommendation annotation method based on cosine similarity using deep neural network features takes advantages of manual annotation and data-driven method, there are still problems such as low accuracy and click-through rate. In order to improve the recommendation accuracy and click-through rate, we propose a confusion graph recommendation annotation method, which builds a confusion graph based on the Largest Margin Nearest Neighbor (LMNN) distance among deep neural network features, to recommend the most confusing images to annotators. In this paper, we made ablation studies on the self-built child face dataset in terms of Precision, mAP (mean Average Precision), and CTR (click-through-rate). The experimental results show that the proposed method achieves superior performance, compared with the cosine similarity recommendation annotation method and the manual annotation method.

关键词： Machine-assisted manual annotating confusion graph largest margin nearest neighbor distance deep learning

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The Aria-S1-Gunw: The ARIA Sentinel-1 Geocoded Unwrapped Phase Product for Open Insar Science and Disaster Response

The Aria-S1-Gunw: The ARIA Sentinel-1 Geocoded Unwrapped Pha...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： David Bekaert Nicholas Arena M Grace Bato Brett Buzzanga Marin Govorcin Emre Havazli Kirk Hogenson Hook Hua Andrew Johnston Mohammed Karim Joseph H. Kennedy Zhong Lu Charlie Z. Marshak Franz Meyer Susan Owen Simran Sangha Gregory Short Jiahui Wang Robert Zinke Jet Propulsion Laboratory California Institute of Technology Pasadena CA USA Alaska Satellite Facility NASA Distributed Active Archive Center Fairbanks AK USA Geophysical Institute University of Alaska Fairbanks Fairbanks AK USA Department of Earth Sciences Southern Methodist University Dallas TX USA

NASA has committed to open-source science that enables Earth observation data transparency, inclusivity, accessibility, and reproducibility – all fundamental to the pace and quality of scientific progress. We have embraced this vision by producing standard InSAR science products that are freely available to the public through NASA Data Active Archive Centers (DAACs) and are generated using state-of-the-art open-source and openly-developed methods. The Advanced Rapid image Analysis (ARIA) project’s Sentinel-1 Geocoded Unwrapped Phase product (ARIA-S1-GUNW) is a 90 meter InSAR product that spans major, land-based fault systems, the US Coasts, and active volcanic regions through the complete Sentinel-1 record. The products enable the measurement of centimeter-scale surface displacement with applications across the solid earth, hydrology, and sea-level disciplines. The ARIA-S1-GUNW also enables rapid response mapping of surface motion after earthquakes, landslides, and subsidence. The ARIA-S1-GUNW products are freely available through the Alaska Satellite Facility (ASF) DAAC. In the last year, we have successfully grown the archive to over 1.1 million products, a 6 fold increase, through NASA ACCESS by improving our processing workflow and leveraging HyP3, an AWS-based cloud processing environment. We are continuing to partner with researchers to generate more products over relevant areas of scientific interest. All the processing software and cloud infrastructure are open-source to ensure reproducibility and enable other scientists to modify, improve upon, and scale their own cloud workflows for related InSAR analyses. We have, in parallel, developed and supported open-source, well-documented tools to further streamline time-series analysis from the ARIA-S1-GUNW into deformation analysis workflows.

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Slim-RFFNet: Slim deep convolution random Fourier feature network for image classification

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KNOWLEDGE-BASED SYSTEMS 2022年 237卷 107878-107878页

作者： Wang, Tingting Dong, Bo Zhang, Kaichun Li, Junbao Xu, Lei Harbin Inst Technol Fac Comp Harbin 150001 Heilongjiang Peoples R China Harbin Inst Technol Dept Control Sci & Engn Harbin 150001 Heilongjiang Peoples R China Second Acad CASIC X Lab Beijing 100854 Peoples R China Osaka Electrocommun Univ Neyagawa Osaka 5728530 Japan Harbin Inst Technol Sch Elect & Informat Engn Harbin 150001 Heilongjiang Peoples R China

The standard kernel method is computationally expensive because it needs to store and compute the inverse of the Gram matrix. Furthermore, the classification accuracy of the single kernel method is not effective or efficient as the method's ability to extract features. The random Fourier feature method establishes a connection between the kernel method and deep learning to solve the above problems. In this paper, we propose a novel slim deep random Fourier feature network named Slim-RFFNet, which introduces convolution into kernel learning. We use the hierarchical strategy and skip connection to construct a deep network structure and lighten the model by using quantization. Experiments conducted on classification benchmarks MNIST and CIFAR10 demonstrate that the proposed SlimRFFNet significantly outperforms current state-of-the-art deep kernel learning methods. Our algorithm also achieves a trade-off between accuracy and latency. The proposed network can be applied to resource-constrained embedded AI devices. The experimental results on the edge computing system show that our algorithm has a small memory footprint and fast inference speed on small edge devices, and thus meets the requirements for practical applications. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Deep kernel learning Random Fourier features Convolution Quantization Classification

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QUBIQ: Uncertainty Quantification for Biomedical image Segmentation Challenge

arXiv

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arXiv 2024年

作者： Li, Hongwei Bran Navarro, Fernando Ezhov, Ivan Bayat, Amirhossein Das, Dhritiman Kofler, Florian Shit, Suprosanna Waldmannstetter, Diana Paetzold, Johannes C. Hu, Xiaobin Wiestler, Benedikt Zimmer, Lucas Amiranashvili, Tamaz Prabhakar, Chinmay Berger, Christoph Weidner, Jonas Alonso-Basanta, Michelle Rashid, Arif Baid, Ujjwal Adel, Wesam Alis, Deniz Baheti, Bhakti Bai, Yingbin Bhat, Ishaan Cetindag, Sabri Can Chen, Wenting Cheng, Li Dutande, Prasad Dular, Lara Elattar, Mustafa A. Feng, Ming Gao, Shengbo Huisman, Henkjan Hu, Weifeng Innani, Shubham Ji, Wei Karimi, Davood Kuijf, Hugo J. Kwak, Jin Tae Le, Hoang Long Li, Xiang Lin, Huiyan Liu, Tongliang Ma, Jun Ma, Kai Ma, Ting Oksuz, Ilkay Holland, Robbie Oliveira, Arlindo L. Pal, Jimut Bahan Pei, Xuan Qiao, Maoying Saha, Anindo Selvan, Raghavendra Shen, Linlin Silva, Joao Lourenco Spiclin, Ziga Talbar, Sanjay Wang, Dadong Wang, Wei Wang, Xiong Wang, Yin Xi, Ruiling Xu, Kele Yang, Yanwu Yergin, Mert Yu, Shuang Zeng, Lingxi Zhang, YingLin Zhao, Jiachen Zheng, Yefeng Zukovec, Martin Do, Richard Becker, Anton Simpson, Amber Konukoglu, Ender Jakab, Andras Bakas, Spyridon Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Harvard Medical School United States Department of Quantitative Biomedicine University of Zurich Switzerland University Children’s Hospital Zurich University of Zurich Switzerland Department of Radioncology and Radiation Theraphy Klinikum rechts der Isar Technical University of Munich Germany Department of Information Technology and Electrical Engineering ETH-Zurich Switzerland Department of Radiology Memorial Sloan Kettering Cancer Center New York City United States Department of Biomedical and Molecular Sciences Queen’s University Canada TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany McGovern Institute Massachusetts Institute of Technology United States Institute for Diagnostic and Interventional Radiology Unveristy Zurich Hospital Switzerland BioMedIA Imperial College London United Kingdom Department of Radiation Oncology University of Pennsylvania PA United States University of Pennsylvania PA United States Department of Radiation Oncology Winship Cancer Institute of Emory University Georgia United States Nile University Cairo Egypt Department of Medical Sciences Acibadem University Istanbul Turkey Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Trustworthy Machine Learning Lab University of Sydney Australia Image Sciences Institute University Medical Center Utrecht Netherlands Computer Engineering Department Istanbul Technical University Istanbul Turkey School of Computer Science Shenzhen University Shenzhen China University of Alberta United States University of Ljubljana Faculty of Electrical Engineering Ljubljana Slovenia Tongji University Shanghai China OPPO Research Institute Shanghai China School of Biological and Medical Engineering Beihang University Beijing China Harvard Medical School Boston

Uncertainty in medical image segmentation tasks, especially inter-rater variability, arising from differences in interpretations and annotations by various experts, presents a significant challenge in achieving consistent and reliable image segmentation. This variability not only reflects the inherent complexity and subjective nature of medical image interpretation but also directly impacts the development and evaluation of automated segmentation algorithms. Accurately modeling and quantifying this variability is essential for enhancing the robustness and clinical applicability of these algorithms. We report the set-up and summarize the benchmark results of the Quantification of Uncertainties in Biomedical image Quantification Challenge (QUBIQ), which was organized in conjunction with International Conferences on Medical image Computing and Computer-Assisted Intervention (MICCAI) 2020 and 2021. The challenge focuses on the uncertainty quantification of medical image segmentation which considers the omnipresence of inter-rater variability in imaging datasets. The large collection of images with multi-rater annotations features various modalities such as MRI and CT;various organs such as the brain, prostate, kidney, and pancreas;and different image dimensions 2D-vs-3D. A total of 24 teams submitted different solutions to the problem, combining various baseline models, Bayesian neural networks, and ensemble model techniques. The obtained results indicate the importance of the ensemble models, as well as the need for further research to develop efficient 3D methods for uncertainty quantification methods in 3D segmentation tasks. © 2024, CC BY-NC-ND.

关键词： image segmentation

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Object Pose Estimation for Robotic Grasping based on Multi-view Keypoint Detection

Object Pose Estimation for Robotic Grasping based on Multi-v...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Zheyuan Hu Renluan Hou Jianwei Niu Xiaolong Yu Tao Ren Qingfeng Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China Zhengzhou University Research Institute of Industrial Technology School of Information Engineering Zhengzhou University Zhengzhou China

Industrial robots can replace human labour to perform a variety of tasks. Among these tasks, robotic grasping is the most primary industrial robot operation. However, conventional robotic grasping methods could become inapplicable for cluttered and occluded objects. To address the issue, we adopt object pose estimation (OPE) to facilitate robotic grasping of cluttered and occluded objects and propose an object detection model based on 2D-RGB multi-view features. The proposed model is built by adding four transpose convolution layers into the Resnet backbone to obtain desirable 2D feature maps of object keypoints in each image. In addition, we design a feature-fusion model to produce 3D coordinates of keypoints from 2D multi-view features based on the volumetric aggregation method, along with a keypoint-detection confidence of each view to assist the optimality judgment of the robotic grasping. Extensive experiments are conducted to verify the accuracy of OPE, and the experimental results indicate the substantial performance improvements of the proposed approach over conventional methods in various scenarios.

关键词： Solid modeling Three-dimensional displays Service robots image color analysis Robot kinematics Pose estimation Robot vision systems

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Standard Deviation Based Adaptive Gradient Compression For distributed Deep Learning 20

Standard Deviation Based Adaptive Gradient Compression For D...

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20th IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing (CCGrid)

作者： Chen, Mengqiang Yan, Zijie Ren, Jiangtao Wu, Weigang Sun Yat Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China

ISBN: (纸本)9781728160955

distributed deep learning has been proposed to train large scale neural network models with huge amounts of datasets using multiple workers. Since workers need to frequently communicate with each other to exchange gradients for updating parameters, communication overhead is always a major challenge in distributed deep learning. To cope with this challenge, gradient compression has been used to reduce the amount of data to be exchanged. However, existing compression methods, including both gradient quantization and gradient sparsification, either hurt model performance significantly or suffer from inefficient compression. In this paper, we propose a novel approach, called Standard Deviation based Adaptive Gradient Compression (SDAGC), which can simultaneously achieve model training with low communication overhead and high model performance in synchronous training. SDAGC uses the standard deviation of gradients in each layer of the neural network to dynamically calculate a suitable threshold according to the training process. Moreover, several associated methods, including residual gradient accumulation, local gradient clipping, adaptive learning rate revision and momentum compensation, are integrated to guarantee the convergence of the model. We verify the performance of SDAGC on various machine learning tasks: image classification, language modeling and speech recognition. The experiment results show that, compared with other existing works, SDAGC can achieve a gradient compression ratio from 433x to 2021x with similar or even better accuracy.

关键词： distributed deep learning gradient compression standard deviation neural networks parallel processing

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一种基于Spark的公路裂缝图像处理方法

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计算机与数字工程 2022年第8期50卷 1795-1801页

作者：李杨刘庆华刘东华黄凯枫贾叙文江苏科技大学镇江212000

随着公路修建里程增长,为了更高效率地解决路面养护问题,对路面裂缝检测技术进行研究。论文将传统的图像分割处理与Spark结合,提出了一种基于Spark的路面裂缝图像分割处理方法。先将图像预处理转化为二进制文件,读入到分布式文件系统;... 详细信息

随着公路修建里程增长,为了更高效率地解决路面养护问题,对路面裂缝检测技术进行研究。论文将传统的图像分割处理与Spark结合,提出了一种基于Spark的路面裂缝图像分割处理方法。先将图像预处理转化为二进制文件,读入到分布式文件系统;然后通过传递函数,对裂缝进行图像分割来获得裂缝区域;最后,以局部自适应阈值算法结合Spark平台来进行图像的并行处理,完成裂缝的高效率提取。以在校园拍摄的路面图像数据为实验数据,分别在处理时间和检测准确率这两方面进行实验。由结果可知:利用Spark对数千张图像数据进行计算时,在确定千余张图像数据规模时,双节点比单节点处理时间更少,最多减少了20.4%。在图像分割的准确率方面,已经由之前方法的47.5%提高到了86.3%。实验结果表明,基于Spark的裂缝图像处理,结合局部自适应阈值分割算法进行数千张裂缝图像分割,能有效提升传统方法的处理效率,能实现大规模图像的并行处理。

关键词：公路裂缝图像处理 Spark 并行处理大数据局部自适应阈值分割

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A Raster Data Framework Based on distributed Heterogeneous Cluster

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JOURNAL OF THE INDIAN SOCIETY OF REMOTE SENSING 2019年第4期47卷 715-723页

作者： Bhushan, R. Phani Somayajulu, D. V. L. N. Venkatraman, S. Subramanyam, R. B. V. ADRIN Dept Space Hyderabad Telangana India NIT Dept Comp Sci & Engn Warangal Andhra Pradesh India

Advancements in satellite imaging and sensor technologies result in capturing of large amount of spatial data. Many parallel processing techniques based on data or control parallelism have been attempted during the past 2 decades to provide performance improvement in image processing applications such as urban sprawl, weather prediction and crop estimation. These techniques have used block-based distributed file processing or the more modern MapReduce-based programming for implementation which still have gaps between optimal and best processing in terms of resource scheduling, data distribution and ease of programming. In this paper, we present a layered framework for parallel data processing to improve storage, retrieval and processing performance of spatial data on an underlying distributed file system. The paper presents a data placement strategy across a distributed HDFS cluster in a way to optimize spatial data retrieval and processing. The presence of neighborhood pixels local to the processing node in a distributed environment reduces network latencies and improves the efficiency of applications such as object recognition, change detection and site selection. We evaluate the data placement strategy on a four-node HDFS cluster and show that it can deliver good performance benefits by way of reading blocks of data at almost 10-12 times the default, which contributes to the improvement in efficiency of the various applications that use region growing methods.

关键词： Big data Remote sensing Spatial MapReduce HDFS RecordReader Raster data Vector data Resource scheduler Retrieval Engine

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Study of large axial space rebinning algorithm in γ-photon industrial tomography image reconstruction

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JOURNAL OF INSTRUMENTATION 2021年第12期16卷

作者： Wang, M. Zhao, M. Yao, M. Liu, J. Guo, R. Nanjing Univ Aeronaut & Astronaut Coll Automat Engn 29 Gen Rd Nanjing 211106 Peoples R China

The accuracy of the existing single slice and Fourier rebinning algorithms depends on the projection angle of the line of response. The increase of such projection angle with the detector size, typical in the large axial space of gamma-photon industrial detection, and the loss of some projection data after rebinning, result in the degradation of the image quality. In addition, those algorithms consider the probability of positron annihilation equally distributed along the line of response, which prevents to estimate accurately the positions of the annihilation point, and can originate artifacts and noise in the reconstructed image. In this work, we propose an alternative large axial space rebinning algorithm. In that algorithm, initially the line of response is divided into transverse and axial components. Then, each line of response is uniformly rebinned into all the 2D sinogram data intersecting with it. To improve the accuracy of the estimate of the annihilation point location and suppress the noise effectively, we assign a Gaussian weight coefficient to the projection data, and optimise the rebinning algorithm with it. Finally, we reconstruct the image on the basis of the 2D sinograms with the optimised weights. On the computational side, the algorithm is also accelerated by making use of parallel computing. Both simulation and experimental results show that the proposed method improves the contrast and spatial resolution of 2D reconstructed images. Furthermore, the reconstruction time is not affected by the new method, which is therefore expected to meet the demand of gamma-photon industrial inspection imaging.

关键词： Data processing methods Detection of defects Inspection with gamma rays

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Evaluation of suturing performance in general surgery and ocular microsurgery by combining computer vision-based software and distributed fiber optic strain sensors: a proof-of-concept

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INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY 2020年第8期15卷 1359-1367页

作者： Handelman, Amir Keshet, Yariv Livny, Eitan Barkan, Refael Nahum, Yoav Tepper, Ronnie Holon Inst Technol Fac Engn Dept Elect Engn Holon Israel Clalit Hlth Serv Simultech Ctr Med Simulat Meir Hosp Med Ctr Kefar Sava Israel Rabin Med Ctr Dept Ophthalmol Petah Tiqwa Israel Tel Aviv Univ Sackler Fac Med Tel Aviv Israel

Purpose Improper suturing may cause an inadequate wound healing process and wound dehiscence as well as infection and even graft rejection in case of corneal transplantation. Hence, training surgeons in correct suturing procedures and objectively assessing their surgical skills is desirable. methods Two complementary methods for assessment of suturing skills in two medical fields (general surgery and ocular microsurgery) were demonstrated. Suturing quality is assessed by computer vision software. Evaluation of stitching flow of operation is based on measuring strain induced in an optical fiber that is placed in proximity to the wound and parallel thereto and is pressed and passed by wound stitches. Results Our software generated a score for suturing outcome in both general surgery and ocular microsurgery when the stitching was done on a patch. Every trainee received a score in the range 0-100 that describes his/her performance. Strain values were recognized when using a patch in general surgery and a rubber patch in ocular microsurgery, but were less distinct in (disqualified) human cornea. Conclusions We proved a concept of an objective scoring method (based on various image processing algorithms) for assessment of suturing performance. It was also shown that fiber optic strain sensors are sensitive to the flow of stitching operation on a patch but are less sensitive to the flow of stitching operation on a human cornea. By combining these two methods, we can comprehensively evaluate the suturing performance objectively.

关键词： Sutures Fiber optic sensors Computer vision Ocular microsurgery Surgical education

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