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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Chenming Li Jiankun Wang Max Q.-H. Meng Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong SAR China Department of Electronic and Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Jiaxing Research Institute Southern University of Science and Technology Jiaxing China Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Department of Electrical and Computer Engineering University of Alberta Canada

Robot path planning in high-dimensional continuous space is of great significance in robotics. The conventional bidirectional fast marching method utilizes brute-force search to explore the space, which is computationally expensive. Though improving the planning efficiency, heuristic-based bidirectional fast marching search results in a suboptimal path under a predetermined number of random samples. To address these issues, we propose Focused Bidirectional Fast Marching Trees (FBFMT*), developing a novel vertex exploring sequence to constrain the forward and backward searches within the brute-force searched regions. It enables the search trees to meet in the middle of the solution path, which guarantees the optimality under the current set of samples and searches faster than the bidirectional brute-force strategy. Extensive simulations in different scenarios validate the superiority of the proposed method, showing that FBFMT* can speed up the planning process and find a better solution. We also extend the proposed method to general conditions so that the forward and backward searches of FBFMT* can meet at any portion of the path. FBFMT* reveals that the heuristic-based bidirectional fast marching search strategy can be a general and efficient technique for robot path planning in high-dimensional continuous spaces.

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UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER 10

UNDERSTANDING DOMAIN RANDOMIZATION FOR SIM-TO-REAL TRANSFER

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10th International Conference on Learning Representations, ICLR 2022

作者： Chen, Xiaoyu Hu, Jiachen Jin, Chi Li, Lihong Wang, Liwei Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University China Department of Electrical and Computer Engineering Princeton University United States Amazon Key Laboratory of Machine Perception MOE School of Artificial Intelligence Peking University International Center for Machine Learning Research Peking University China

Reinforcement learning encounters many challenges when applied directly in the real world. Sim-to-real transfer is widely used to transfer the knowledge learned from simulation to the real world. Domain randomization-one of the most popular algorithms for sim-to-real transfer-has been demonstrated to be effective in various tasks in robotics and autonomous driving. Despite its empirical successes, theoretical understanding on why this simple algorithm works is limited. In this paper, we propose a theoretical framework for sim-to-real transfers, in which the simulator is modeled as a set of MDPs with tunable parameters (corresponding to unknown physical parameters such as friction). We provide sharp bounds on the sim-to-real gap-the difference between the value of policy returned by domain randomization and the value of an optimal policy for the real world. We prove that sim-to-real transfer can succeed under mild conditions without any real-world training samples. Our theory also highlights the importance of using memory (i.e., history-dependent policies) in domain randomization. Our proof is based on novel techniques that reduce the problem of bounding the sim-to-real gap to the problem of designing efficient learning algorithms for infinite-horizon MDPs, which we believe are of independent interest. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

关键词： Learning algorithms

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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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research on Biomimetic Design Methods for Humanoid Robot Thigh*

Research on Biomimetic Design Methods for Humanoid Robot Thi...

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IEEE International Conference on robotics and Biomimetics

作者： Daming Nie Anhuan Xie Lingyu Kong Yu Zhang Gang Zheng Yili Fu Jason Gu Intelligent Robot Research Center Zhejiang Laboratory Hangzhou China School of Mechanical and Energy Engineering Zhejiang University of Science and Technology Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

Human bones have formed the preferred configuration for high-strength and lightweight after long-time evolution. Taking human’s longest and strongest bone - the femur - as an example, it is consist of two characteristic layers, i.e. the substantia compacta and the substantia spongiosd. This article innovatively imitates the structural characteristics of human femur, the thigh of humanoid robot is designed in form of "variable thickness shell + variable density lattice". The thickness of shell and the density of lattice are adjusted by the initial stress distribution individually. Results show that the weight of shell and lattice of the thigh structure can be reduced by 20% under reasonable mapping relationship of "stress - shell thickness" and "stress - lattice rod diameter", while the structural stiffness meets the application requirements. Finally, the limiting factors of the "variable thickness shell + variable density lattice" structure designing approach are analyzed, and potential measures for optimizing the design method of the humanoid robot thigh in the future are described.

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Exploring the Frontiers of Synthetic Image-Based Deep Learning Training in Digital X-ray Radiography.

e-Journal of Nondestructive Testing

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e-Journal of Nondestructive Testing 2024年第8期29卷

作者： Hena, Bata Wei, Ziang Perron, Luc Castanedo, Clemente Ibarra Maldague, Xavier Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Université Laval 1065 Ave de la Médecine Quebec CityQCG1V 0A6 Canada GI Lab Solutions 475 Pruneau Ave Québec CityQCG1M 2J8 Canada

Industrial radiography is a pivotal non-destructive testing (NDT) method that ensures quality and safety in a wide range of industrial sectors. Nevertheless, the conventional human-based approaches to carrying out industrial radiography are prone to challenges that negatively impact the accuracy and efficiency of defect detection. To solve this challenge, numerous computer-based alternatives have been developed, including Automated Defect Recognition (ADR) using deep learning algorithms. At the core of training, these ADR algorithms demand large volumes of qualitative data that should be representative of real-world cases to be expected during deployment. However, the availability of digital X-ray radiography data, especially for open research, is limited by non-disclosure contractual terms in the industry. In this study, we present a pipeline that is capable of modelling synthetic images based on real digital X-ray radiography images. This is achieved through a systematic analysis of the intensity distribution, considering grey value (GV) statistical uniqueness related to exposure conditions used during image acquisition, type of imaged component, material thickness variations, X-ray beam divergence, anode heel effect, scatter radiation, edge delineation, etc. The generated synthetic images were exclusively utilized to train a deep learning model, yielding an impressive model performance with mean intersection over union (IoU) of 0.93, and mean dice coefficient of 0.96 when tested on real unseen digital X-ray radiography images. The presented methodology is scalable and adaptable, making it suitable for diverse industrial applications. © 2024 The Authors.

关键词： X ray radiography

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Multi-label advertising image classification using traditional deep neural networks and vision language models: dataset and annotation agreement method

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Multimedia Tools and Applications 2025年 1-30页

作者： Mir, Tatheer Hussain Wei, Jui-Cheng Simanjuntak, Mutiara Cheng, Wan-Shu Lee, Yi-Hsun Zhang, You-Chen Chen, Jia-Bin Dai, Hong-Jie Intelligent System Laboratory Department of Electrical Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Computer Science and Information Management Providence University Taichung Taiwan Research and Development Department Linker AI Co. Ltd Taipei Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan

Effectively classifying advertising images is crucial in targeting the right audience and maximizing marketing performance. To address this problem, this paper presents a multi-label advertising image classification study using popular deep-learning architectures. First, we compile a dedicated dataset for this task and evaluate the performance of traditional deep learning-based models based on the convolutional neural network (CNN) and vision transformer architectures. To ensure the quality of dataset annotations, we introduce an extended Krippendorf’s Alpha (α) method based on the Jaccard index to provide a reliable measure of inter-annotation agreement which can address the missing annotations and multiple labels to establish the dataset’s annotation consistency. Our results demonstrate that transformer-based architectures like ViT and Swin outperform the CNN-based model’s baseline and differential learning rate settings. Through the visualization analysis of saliency maps, we gain insights into the model’s decision-making processes and identify the factors influencing their predictions. Furthermore, we assess the impact of annotation quality on model performance, comparing models trained on different annotation reliability levels. Our results indicate that higher annotation consistency, as quantified by α-Jaccard, leads to improved model performance, emphasizing the importance of high-quality datasets in advertising image classification. Beyond traditional deep learning models, we explore the effectiveness of vision language models (VLMs) in this task by employing prompt engineering and comparing their performance with fine-tuned deep learning models. Our findings indicate that while VLMs provide richer contextual annotations, they suffer from over-classification tendencies, subjective biases, and significantly higher computational costs. In contrast, deep learning models remain a more efficient and scalable solution for structured, large-scale advertising classi

关键词： Advertising image Convolution neural network Krippendorf’s Alpha Learning rate configuration Marketing effectiveness Multi-label classification vision Language Models vision transformer

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EFaR 2023: Efficient Face Recognition Competition

arXiv

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arXiv 2023年

作者： Kolf, Jan Niklas Boutros, Fadi Elliesen, Jurek Theuerkauf, Markus Damer, Naser Alansari, Mohamad Hay, Oussama Abdul Alansari, Sara Javed, Sajid Werghi, Naoufel Grm, Klemen Štruc, Vitomir Alonso-Fernandez, Fernando Diaz, Kevin Hernandez Bigun, Josef George, Anjith Ecabert, Christophe Shahreza, Hatef Otroshi Kotwal, Ketan Marcel, Sébastien Medvedev, Iurii Jin, Bo Nunes, Diogo Hassanpour, Ahmad Khatiwada, Pankaj Toor, Aafan Ahmad Yang, Bian Fraunhofer Institute for Computer Graphics Research IGD Germany TU Darmstadt Germany Department of Electrical and Computer Engineering Khalifa University Abu Dhabi United Arab Emirates Laboratory for Machine Intelligence Faculty of Electrical Engineering University of Ljubljana Slovenia Halmstad University Sweden Idiap Research Institute Martigny Switzerland Lausanne Switzerland Lausanne Switzerland Institute of Systems and Robotics University of Coimbra Coimbra Portugal eHealth and Welfare Security Group Department of Information Security and Communication Technology Norwegian University of Science and Technology Norway

This paper presents the summary of the Efficient Face Recognition Competition (EFaR) held at the 2023 International Joint Conference on Biometrics (IJCB 2023). The competition received 17 submissions from 6 different teams. To drive further development of efficient face recognition models, the submitted solutions are ranked based on a weighted score of the achieved verification accuracies on a diverse set of benchmarks, as well as the deployability given by the number of floating-point operations and model size. The evaluation of submissions is extended to bias, cross-quality, and large-scale recognition benchmarks. Overall, the paper gives an overview of the achieved performance values of the submitted solutions as well as a diverse set of baselines. The submitted solutions use small, efficient network architectures to reduce the computational cost, some solutions apply model quantization. An outlook on possible techniques that are underrepresented in current solutions is given as well. © 2023, CC BY-NC-SA.

关键词： Face recognition

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GWAS identifies candidate genes controlling adventitious rooting in Populus trichocarpa

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Horticulture research 2023年第8期10卷 97-108页

作者： Michael F.Nagle Jialin Yuan Damanpreet Kaur Cathleen Ma Ekaterina Peremyslova Yuan Jiang Bahiya Zahl Alexa Niño de Rivera Wellington Muchero Li Fuxin Steven H.Strauss Department of Forest Ecosystems and Society Oregon State University3180 SW Jefferson WayCorvallisOR97331United States Department of Electrical Engineering and Computer Science Oregon State University110 SW Park TerraceCorvallisOR97331United States Statistics Department Oregon State University103 SW Memorial PlaceCorvallisOR97331United States Biosciences Division Oak Ridge National Laboratory1 Bethel Valley RdOak RidgeTN37830United States Center for Bioenergy Innovation Oak Ridge National Laboratory1 Bethel Valley RdOak RidgeTN37830United States Bredesen Center for Interdisciplinary Research University of Tennessee821 Volunteer Blvd.KnoxvilleTN37996United States

Adventitious rooting(AR)is critical to the propagation,breeding,and genetic engineering of *** capacity for plants to undergo this process is highly heritable and of a polygenic nature;however,the basis of its genetic variation is largely *** identify genetic regulators of AR,we performed a genome-wide association study(GWAS)using 1148 genotypes of Populus *** are often limited by the abilities of researchers to collect precise phenotype data on a high-throughput scale;to help overcome this limitation,we developed a computer vision system to measure an array of traits related to adventitious root development in poplar,including temporal measures of lateral and basal root length and *** was performed using multiple methods and significance thresholds to handle non-normal phenotype statistics and to gain statistical *** analyses yielded a total of 277 unique associations,suggesting that genes that control rooting include regulators of hormone signaling,cell division and structure,reactive oxygen species signaling,and other processes with known roles in root *** genes with uncharacterized functions and/or cryptic roles were also *** candidates provide targets for functional analysis,including physiological and epistatic analyses,to better characterize the complex polygenic regulation of AR.

关键词： GWAS breeding overcome

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Organ Segmentation with Recursive Data Augmentation for Deep Models 25th

Organ Segmentation with Recursive Data Augmentation for Deep...

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25th International Conference on Pattern Recognition Workshops, ICPR 2020

作者： Akbar, Muhammad Usman Yamin, Muhammad Abubakar Murino, Vittorio Sona, Diego Pattern Analysis and Computer Vision Istituto Italiano di Tecnologia Genova Italy Department of Electrical Electronics and Telecommunication Engineering Università di Genova Genova Italy Huawei Technologies Ltd. Ireland Research Center Dublin Ireland Department of Computer Science Università di Verona Verona Italy Neuroinformatics Laboratory Fondazione Bruno Kessler Trento Italy

ISBN: (纸本)9783030687625

The precise segmentation of organs from computed tomography is a fundamental and pivotal task for correct diagnosis and proper treatment of diseases. Neural network models are widely explored for their promising performance in the segmentation of medical images. However, the small dimension of available datasets is affecting the biomedical imaging domain significantly and has a huge impact in training of deep learning models. In this paper we try to address this issue by iteratively augmenting the dataset with auxiliary task-based information. This is obtained by introducing a recursive training approach, where a new set of segmented images is generated at each iteration and then concatenated with the original input data as organ attention maps. In the experimental evaluation two different datasets were tested and the results produced from the proposed approach have shown significant improvements in organ segmentation as compared to a standard non-recursive approach. © 2021, Springer Nature Switzerland AG.

关键词： Deep learning

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Screening cognitive impairment in patients with atrial fibrillation: A deep learning model based on retinal fundus photographs

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Heart Rhythm O2 2025年第5期6卷 678-686页

作者： Wang, Zhen Li, Mingxiao Xia, Peng Jiang, Chao Shen, Ting Ma, Jiaming Bai, Yu Zhang, Suhui Lai, Yiwei Li, Sitong Xu, Hui Xu, Yang Ma, Tong Ju, Lie He, Liu Dong, Li Sang, Caihua Long, Deyong Chen, Yuzhong Du, Xin Ge, Zongyuan Dong, Jianzeng Wei, Wen-Bin Ma, Changsheng Department of Cardiology Beijing Anzhen Hospital Capital Medical University Beijing China Engineering Research Center of Medical Devices for Cardiovascular Diseases Ministry of Education Capital Medical University Beijing China National Clinical Research Center for Cardiovascular Diseases Beijing China Beijing Airdoc Technology Co. Ltd. Beijing China Department of Electrical and Computer Systems Engineering Faculty of Engineering Monash University Clayton Australia Yale School of Medicine New Haven Connecticut United States School of Clinical Medicine Peking Union Medical College and Chinese Academy of Medical Science Beijing China Beijing Tongren Eye Center Beijing Key Laboratory of Intraocular Tumor Diagnosis and Treatment Beijing Ophthalmology & Visual Sciences Key Lab Medical Artificial Intelligence Research and Verification Key Laboratory of the Ministry of Industry and Information Technology Beijing Tongren Hospital Capital Medical University Beijing China Monash e-Research Center Monash University Clayton Australia

Background: Patients with atrial fibrillation (AF) have a higher risk of cognitive impairment (CI). However, complexity of CI diagnosis and lack of simple screening approaches limited early screening and intervention of CI in AF patients. Objective: Our study aimed to develop deep learning models based on fundus photographs for easy screening of CI in AF patients. Methods: From May 2021 to April 2023, patients who completed fundus examination and cognitive function evaluation in the Chinese Atrial Fibrillation Registry Study were included. The training and validation sets were randomly split at an 8:2 ratio. Participants from the Beijing Eye Study served as the external validation set. Different deep learning models were trained, and their CI detection ability was validated. Results: A total of 899 patients in the Chinese Atrial Fibrillation Registry Study were included. In the validation set, the vision-ensemble model based on fundus images alone had an area under the receiver-operating characteristic curve (AUROC) of 0.855 (95% confidence interval 0.816–0.894) for CI screening. The multimodal model (AUROC 0.861, 95% confidence interval 0.823–0.898), based on fundus photographs and 4 clinical variables, performed comparably to the vision-ensemble model. The AUROC of the vision-ensemble model for CI screening achieved 0.773 (95% confidence interval 0.709–0.837) in the external test set. In the saliency map, the vision-ensemble model focused on areas around retinal vessels and the optic disc. Conclusion: A vision-ensemble model based on fundus images might be practical for preliminary screening of CI in AF patients. © 2025 Heart Rhythm Society

关键词： Artificial intelligence Atrial fibrillation Cognitive impairment Deep learning Fundus photography

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