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2023 IEEE/CVF Conference on computer vision and Pattern Recognition Workshops, CVPRW 2023

作者： Ancuti, Codruta O. Ancuti, Cosmin Vasluianu, Florin-Alexandru Timofte, Radu Zhou, Han Dong, Wei Liu, Yangyi Chen, Jun Liu, Huan Li, Liangyan Wu, Zijun Dong, Yubo Li, Yuyan Qiu, Tian He, Yu Lu, Yonghong Wu, Yinwei Jiang, Zhenxiang Liu, Songhua Yang, Xingyi Jing, Yongcheng Benjdira, Bilel Ali, Anas M. Koubaa, Anis Yang, Hao-Hsiang Chen, I-Hsiang Chen, Wei-Ting Huang, Zhi-Kai Chen, Yi-Chung Hsieh, Chia-Hsuan Chang, Hua-En Chiang, Yuan-Chun Kuo, Sy-Yen Guo, Yu Gao, Yuan Liu, Ryan Wen Lu, Yuxu Qu, Jingxiang He, Shengfeng Ren, Wenqi Hoang, Trung Zhang, Haichuan Yazdani, Amirsaeed Monga, Vishal Yang, Lehan Wu, Alex Jiahao Mai, Tiancheng Cong, Xiaofeng Yin, Xuemeng Yin, Xuefei Emad, Hazim Abdallah, Ahmed Yasser, Yahya Elshahat, Dalia Elbaz, Esraa Li, Zhan Kuang, Wenqing Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Zhang, Zhao Wei, Yanyan Wang, Junhu Zhao, Suiyi Zheng, Huan Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Jiang, Kui Sarvaiya, Anjali Prajapati, Kalpesh Patra, Ratnadeep Barik, Pragnesh Rathod, Chaitanya Upla, Kishor Raja, Kiran Ramachandra, Raghavendra Busch, Christoph ETcTI Universitatea Politehnica Timisoara Romania ICTEAM UCL Belgium Computer Vision Lab University of Wuerzburg Germany Computer Vision Lab ETH Zurich Switzerland Department of Electrical and Computer Engineering McMaster University Canada Department of Electrical and Computer Engineering University of Alberta Canada McMaster University Canada Xidian University China Research Institute Singapore National University of Singapore Singapore University of Sydney Australia Robotics and Internet-of-Things Laboratory Prince Sultan University Riyadh12435 Saudi Arabia Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan Wuhan University of Technology China Singapore Management University Singapore Singapore Sun Yat-sen University China Electrical Engineering Department Pennsylvania State University United States The University of Sydney Australia Southeast University China University of California Los Angeles United States Beijing Jiaotong University China Mansoura Univeristy Egypt College of Information Science and Technology Jinan University China Department of Information Technology Uppsala University Sweden Hefei University of Technology China Zhejiang Dahua Technology China Sardar Vallabhbhai National Institute of Technology India Norwegian University of Science and Technology Norway

ISBN: (纸本)9798350302493

This study assesses the outcomes of the NTIRE 2023 Challenge on Non-Homogeneous Dehazing, wherein novel techniques were proposed and evaluated on new image dataset called HD-NH-HAZE. The HD-NH-HAZE dataset contains 50 high resolution pairs of real-life outdoor images featuring nonhomogeneous hazy images and corresponding haze-free images of the same scene. The nonhomogeneous haze was simulated using a professional setup that replicated real-world conditions of hazy scenarios. The competition had 246 participants and 17 teams submitted solutions for the final testing phase. The proposed solutions demonstrated the cutting-edge in image dehazing technology. © 2023 IEEE.

关键词： Demulsification

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Low-cost Mars Terrain Classification System Based on Coarse-grained Annotation

Low-cost Mars Terrain Classification System Based on Coarse-...

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IEEE International Conference on robotics and Biomimetics

作者： Jian Zhang Yeheng Chen Shiqiang Zhu Yuehua Li Tian Xie Jason Gu Zhejiang Laboratory Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Due to communication latency with remote ground sites, automatic recognition of Mars terrain is essential for the path-planning of rovers. Currently, most vision-based terrain classification require thousands of fine-grained training samples, while the undefined terrains on Mars are difficult to be classified or fine-grained labeled. Actually, most of the terrain categories can only be coarse-grained labeled due to several limitations, such as overlapped sub-regions, blurred borders, etc. To solve this problem, CACMT (Coarse-grained Annotation-based Classification for Mars Terrain) is proposed to generate the global fine-grained classification map from the local coarse-grained data. Specifically, the complete pipeline is decomposed into (i) annotation rules with unique design, (ii) hierarchical feature fusion network for predicting sub-features of terrain (iii) and a generator for outputting dense terrain categories of Mars. Finally, the results of actual data on Mars demonstrate that the terrain sub-features can be successfully recognized and a dense terrain classification map can be generated applying only coarse-grained labeled images.

关键词： Training Image recognition Annotations Biomimetics Pipelines Generators Robots

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HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re-Identification Implemented on Tour-Guide Robot

HSMR: A Head-Shoulder Mask Aided ResNet to Guide Focus of Re...

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IEEE International Conference on robotics and Biomimetics

作者： Zheyuan Lin Min Huang Wen Wang Mengjie Qin Te Li Minhong Wan Jason Gu Shiqiang Zhu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

For service robots, person re-identification (ReID) and multi-pedestrian tracking (MPT) are vital to get a person's location and link identities across frames. Though their accuracy keeps improving, most work lacks consideration of the application scenario, haunted by limited space, constrained power supply, and demands of the real-time response during human-robot interaction. Some ReID models learn trivial or unrelated features, inhibiting the downstream tasks. To solve these issues, the efficient and light-weighted Head-Shoulder Mask aided ResNet (HSMR) is proposed. This model applies multi-task learning to enhance the feature extraction performance in the training stage without extra computational load during inference. The auxiliary task fully uses head-shoulder information to guide the network and focuses on the head region, which contains the identity information. In experiments on the Tour-Guide Robot Data Base (TGRDB), HSMR earned results better than ResNet-18 on the ReID task and was superior to the recent two-stream method on the MPT task. On the mobile hardware, inference reaches an average of 15.2 FPS, three times faster than the two-stream method. The code is released at https://***/ZhYLin99/HSMR.

关键词： Training Service robots Computational modeling Robot vision systems Human-robot interaction Multitasking Real-time systems

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Adaptive Robust Invariant Extended Kalman filtering for Biped Robot

Adaptive Robust Invariant Extended Kalman filtering for Bipe...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Gao, Chengzhi Xie, Ye Zhu, Shiqiang Huang, Guanyu Kong, Lingyu Xie, Anhuan Gu, Jason Zhang, Dan Shao, Jun Qian, Haofu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Zhejiang University Zhejiang Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University HalifaxNSB3H 4R2 Canada York University North YorkM3J 1P3 Canada School of Mechanical Engineering Zhejiang University Hangzhou310007 China

ISBN: (纸本)9781665481090

The precise estimation of system states is essential for the locomotion control of biped robots to maintain balance. Currently, the estimation of system states is either based on vision data that is susceptible to the environment, or fusing data from Inertial Measurement Unit (IMU) and the kinematic calculation. Invariant Extended Kalman Filtering (IEKF) is one of the most successful fusing algorithms to estimate system state. Generally, in IEKF, the noise covariance of system state is assumed to be known. However, the noise covariance of contact point for biped is not available since the ground-contact situation normally varies and not previously known. This paper presents a new fusing algorithm-Adaptive Robust Invariant Extended Kalman Filtering (ARIEKF) to adaptively adjust the noise parameter of contact point. The proposed algorithm applied the principle of robust estimation to resist outlier effects of state, and introduced an adaptive factor for the noise covariance of state to control its outlying disturbance influences. This paper firstly completed the full state estimation of biped robot using the theory of Lie groups and invariant observer. Then, the adaptive scale factor evaluated by three-segment approach was adopted to adjust the noise covariance of contact point. Finally, both IEKF and proposed ARIEKF are applied to our biped robot-Cosmos and the accuracy of two algorithms are compared. The mean square errors of the velocity of two algorithms were evaluated using the measurements from motion capture system. Experiments demonstrated that the mean square errors of the velocity are decreased 50 percent when compared with IEKF. © 2022 IEEE.

关键词： Mean square error

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Adaptive Block Elevation Mapping for Large-scale Scene

Adaptive Block Elevation Mapping for Large-scale Scene

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IEEE International Conference on robotics and Biomimetics

作者： Yang Zhou Shiqiang Zhu Huang Huang Yuehua Li Jason Gu Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Beijing Institute of Control Engineering Beijing China Beijing Research Institute of Zhejiang Laboratory Beijing China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada

ISBN: (纸本)9781665481106

Dense map that contains the surrounding geometry and vision information of a robot is widely used for path planning, navigation, obstacle avoidance and other applications. Considering the performance of the processing unit mounted on the robot is limited, mapping algorithm has to make compromise by sacrificing speed and precision. It will be more challenging when the dense mapping scene is very large because the memory consumption will be greatly increased and the map is difficult to be extended if beyonding the initial map. To suppress the negative impact from the increased map scale, we proposed a novel block mapping approach to generate the dense map in large scale of scene. In this work, the elevation map is selected as the base dense map. The entire elevation map is segmented into numerous block maps of which size is much smaller than that of the entire map. The present moment of lidar and vision measurements are used to generate the local elevation map. The local elevation map is used to update block maps which are adaptively generated along the motion trajectory. A memory-disk interaction mechanism, which the block maps will be loaded to memory or saved to local disk when needed, is introduced. Our block mapping approach is tested on the KITTI datasets, and the results demonstrate that the mapping approach can stably operate in a large scale of scene with a much smaller consumption of memory.

关键词： Geometry Laser radar Navigation Motion segmentation Memory management Robot sensing systems Trajectory

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Classifying Cover Crop Residue from RGB Images: A Simple SVM versus a SVM Ensemble

Classifying Cover Crop Residue from RGB Images: A Simple SVM...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Parth C. Upadhyay Lokesh Karanam John A. Lory Guilherme N. DeSouza Vision-Guided and Intelligent Robotics (ViGIR) Laboratory Department of Electrical Engineering and Computer Science (EECS) Division of Plant Sciences University of Missouri Columbia MO

ISBN: (纸本)9781728190495

Plant-derived crop residue on soil surface provides many important advantages including preventing erosion and conserving soil moisture. In that sense, making accurate determination on the percent of crop residue cover using RGB images can be a fundamental tool in protecting the soil. In our research, we approach the determination of such percentages as a classification problem, and in this paper, we compare two of these approaches. Both approaches relied on support vector machines (SVM) as the classifier of choice, and the same set of features, which were selected in our previous studies on the same topic. In this paper we developed a SVM ensemble with a hierarchical structure and compared it against a single, multi-class SVM classifier. In the SVM ensemble framework, four two-class SVMs and one five-class SVM were combined in sequence to better separate adjacent levels of residue cover. The rationale of the ensemble was to allow each of the two-class SVMs to find the hyperplanes that maximize the margin between the corresponding two consecutive classes. Then, based on the distance of the samples to these hyperplanes, probabilistic estimates of the data-point belonging to the class were computed and added as extra inputs for the last SVM. In order to enhance the performance of the ensemble, other considerations such as the use of Grid Search method for optimizing the hyperparameters were employed in the tuning of the SVMs. Numerical experiments were conducted over a dataset of 4,400 images, which were collected from 88 locations in 40 row crop fields in five Missouri counties between mid-April and early July in 2018 and 2019. The images were collected using a camera mounted on a tripod, with a spatial resolution of 0.014 cm pixel- 1 GSD (Ground Sampling Distance). The experiments highlighted the better performance of the proposed hierarchical ensemble classifier, which achieved a cross-validation accuracy of 86.3 % vs an accuracy of 80.4 % for the single SVM, whil

关键词： Support vector machines Measurement Training Search methods Soil moisture Crops Probabilistic logic

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Adaptive Robust Invariant Extended Kalman filtering for Biped Robot*

Adaptive Robust Invariant Extended Kalman filtering for Bipe...

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IEEE International Conference on robotics and Biomimetics

作者： Chengzhi Gao Ye Xie Shiqiang Zhu Guanyu Huang Lingyu Kong Anhuan Xie Jason Gu Dan Zhang Jun Shao Haofu Qian Research Center for Intelligent Robotics Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou Zhejiang China Department of Electrical and Computer Engineering Dalhousie University Halifax NS Canada B3H 4R2 York University North York Toronto M3J 1P3 Canada School of Mechanical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9781665481106

关键词： Measurement units Filtering Mean square error methods Resists Observers Motion capture Kalman filters

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Simultaneous tactile exploration and grasp refinement for unknown objects

arXiv

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arXiv 2021年

作者： de Farias, Cristiana Marturi, Naresh Stolkin, Rustam Bekiroglu, Yasemin The Extreme Robotics Laboratory School of Metallurgy and Materials University of Birmingham Birmingham United Kingdom Chalmers University of Technology Department of Electrical Engineering Automatic Control research group Sweden University College London Department of Computer Science Centre for Artificial Intelligence United Kingdom

This paper addresses the problem of simultaneously exploring an unknown object to model its shape, using tactile sensors on robotic fingers, while also improving finger placement to optimise grasp stability. In many situations, a robot will have only a partial camera view of the near side of an observed object, for which the far side remains occluded. We show how an initial grasp attempt, based on an initial guess of the overall object shape, yields tactile glances of the far side of the object which enable the shape estimate and consequently the successive grasps to be improved. We propose a grasp exploration approach using a probabilistic representation of shape, based on Gaussian Process Implicit Surfaces. This representation enables initial partial vision data to be augmented with additional data from successive tactile glances. This is combined with a probabilistic estimate of grasp quality to refine grasp configurations. When choosing the next set of finger placements, a bi-objective optimisation method is used to mutually maximise grasp quality and improve shape representation during successive grasp attempts. Experimental results show that the proposed approach yields stable grasp configurations more efficiently than a baseline method, while also yielding improved shape estimate of the grasped object. Copyright © 2021, The Authors. All rights reserved.

关键词： Machine learning

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MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

arXiv

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arXiv 2022年

作者： Li, Lei Wu, Fuping Wang, Sihan Luo, Xinzhe Martín-Isla, Carlos Zhai, Shuwei Zhang, Jianpeng Liu, Yanfei Zhang, Zhen Ankenbrand, Markus J. Jiang, Haochuan Zhang, Xiaoran Wang, Linhong Arega, Tewodros Weldebirhan Altunok, Elif Zhao, Zhou Li, Feiyan Ma, Jun Yang, Xiaoping Puybareau, Elodie Oksuz, Ilkay Bricq, Stephanie Li, Weisheng Punithakumar, Kumaradevan Tsaftaris, Sotirios A. Schreiber, Laura M. Yang, Mingjing Liu, Guocai Xia, Yong Wang, Guotai Escalera, Sergio Zhuang, Xiahai School of Data Science Fudan University Shanghai China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China Departament de Matemàtiques & Informàtica Universitat de Barcelona Barcelona Spain Computer Vision Center Universitat Autònoma de Barcelona Spain School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China Chengdu China School of Computer Science and Engineering Northwestern Polytechnical University Xi'An China College of Electrical and Information Engineering Hunan University Changsha China National Engineering Laboratory for Robot Visual Perception and Control Technology Changsha China College of Physics and Information Engineering Fuzhou University Fuzhou China Comprehensive Heart Failure Center Wuerzburg University Hospitals Wuerzburg Germany School of Engineering University of Edinburgh Edinburgh United Kingdom School of Robotics Xi'an Jiaotong-Liverpool University Suzhou China Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Radiology and Diagnostic Imaging University of Alberta Edmonton Canada Chongqing Key Laboratory of Image Cognition Chongqing University of Posts and Telecomm-Unications Chongqing China ImViA Laboratory Université Bourgogne Franche-Comté Dijon France Computer Engineering Department Istanbul Technical University Istanbul Turkey Le Kremlin-Bicêtre France Department of Mathematics Nanjing University of Science and Technology Nanjing China Department of Mathematics Nanjing University Nanjing China

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (***/zhuangxiahai/0/myops20/). © 2022, CC BY-NC-SA.

关键词： Magnetic resonance imaging

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Efficient MedSAMs: Segment Anything in Medical Images on Laptop

arXiv

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

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