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IEEE International Joint Conference on Biometrics (IJCB)

作者： Emersic, Z. Ohki, T. Akasaka, M. Arakawa, T. Maeda, S. Okano, M. Sato, Y. George, A. Marcel, S. Ganapathi, I. I. Ali, S. S. Javed, S. Werghi, N. Isik, S. G. Saritas, E. Ekenel, H. K. Hudovernik, V. Kolf, J. N. Boutros, F. damer, N. Sharma, G. Kamboj, A. Nigam, A. Jain, d. K. Camara-Chavez, G. Peer, P. Struc, V. Univ Ljubljana Ljubljana Slovenia Shizuoka Univ Shizuoka Japan Idiap Res Inst Martigny Switzerland Khalifa Univ Elect Engn & Comp Sci Dept Abu Dhabi U Arab Emirates Istanbul Tech Univ Dept Comp Engn Istanbul Turkiye ASELSAN Microelect Guidance & Electroopt Grp Ankara Turkiye Fraunhofer Inst Comp Graph Res IGD Hamburg Germany Tech Univ Darmstadt Darmstadt Germany Indian Inst Technol Mandi Mandi Himachal Prades India HCL Imaging & Robot R&D Lab Bangalore Karnataka India Dalian Univ Technol Sch Artificial Intelligence Dalian Peoples R China Fed Univ Ouro Preto UFOP Ouro Preto Brazil

ISBN: (纸本)9798350337266

The paper provides a summary of the 2023 Unconstrained Ear recognition Challenge (UErC), a benchmarking effort focused on ear recognition from images acquired in uncontrolled environments. The objective of the challenge was to evaluate the effectiveness of current ear recognition techniques on a challenging ear dataset while analyzing the techniques from two distinct aspects, i.e., verification performance and bias with respect to specific demographic factors, i.e., gender and ethnicity. Seven research groups participated in the challenge and submitted a seven distinct recognition approaches that ranged from descriptor-based methods and deep-learning models to ensemble techniques that relied on multiple data representations to maximize performance and minimize bias. A comprehensive investigation into the performance of the submitted models is presented, as well as an in-depth analysis of bias and associated performance differentials due to differences in gender and ethnicity. The results of the challenge suggest that a wide variety of models (e.g., transformers, convolutional neural networks, ensemble models) is capable of achieving competitive recognition results, but also that all of the models still exhibit considerable performance differentials with respect to both gender and ethnicity. To promote further development of unbiased and effective ear recognition models, the starter kit of UErC 2023 together with the baseline model, and training and test data is made available from: http://***/.

关键词： deep learning

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Safety & efficacy of a robotic hip exoskeleton on outpatient stroke rehabilitation

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JOUrNAL OF NEUrOENGINEErING ANd rEHABILITATION 2024年第1期21卷 127-127页

作者： Macaluso, rebecca Giffhorn, Matt Prokup, Sara Cleland, Brice Lee, Jusuk Lim, Bokman Lee, Minhyung Lee, Hwang-Jae Madhavan, Sangeetha Jayaraman, Arun Shirley Ryan AbilityLab Max Nader Lab Rehabil Technol & Outcomes Res 355 E Erie St Chicago IL 60611 USA Univ Illinois Coll Appl Hlth Sci Chicago IL USA Univ Calif San Francisco Dept Radiol & Biomed Imaging San Francisco CA USA WIRobotics Inc Robot R&D Team Yongin South Korea Samsung Elect Samsung Res Robot Syst Team Suwon South Korea Samsung Elect Robot Business Team Suwon South Korea Northwestern Univ Dept Phys Med & Rehabil 710 N Lake Shore Dr Chicago IL 60611 USA

Objective The objective of this study was to analyze the safety and efficacy of using a robotic hip exoskeleton designed by Samsung Electronics Co., Ltd., Korea, called the Gait Enhancing and Motivating System-Hip (GEMS-H), in assistance mode only with the poststroke population in an outpatient-rehabilitation setting. Methods Forty-one participants with an average age of 60 and average stroke latency of 6.5 years completed this prospective, single arm, interventional, longitudinal study during the COVId-19 pandemic. Significant modifications to the traditional outpatient clinical environment were made to adhere to organizational physical distancing policies as well as guidelines from the Centers for disease Control. All participants received gait training with the GEMS-H in assistance mode for 18 training sessions over the course of 6-8 weeks. Performance-based and self-reported clinical outcomes were assessed at four time points: baseline, midpoint (after 9 training sessions), post (after 18 training sessions), and 1-month follow up. daily step count was also collected throughout the duration of the study using an ankle-worn actigraphy device. Additionally, corticomotor excitability was measured at baseline and post for 4 bilateral lower limb muscles using transcranial magnetic stimulation. results By the end of the training program, the primary outcome, walking speed, improved by 0.13 m/s (p < 0.001). Secondary outcomes of walking endurance, balance, and functional gait also improved as measured by the 6-Minute Walk Test (47 m, p < 0.001), Berg Balance Scale (2.93 points, p < 0.001), and Functional Gait Assessment (1.80 points, p < 0.001). daily step count significantly improved with and average increase of 1,750 steps per day (p < 0.001). There was a 35% increase in detectable lower limb motor evoked potentials and a significant decrease in the active motor threshold in the medial gastrocnemius (-5.7, p < 0.05) after training with the device. Conclusions Gait tra

关键词： Stroke rehabilitation Exoskeleton Gait training Outpatient

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The Unconstrained Ear recognition Challenge 2023: Maximizing Performance and Minimizing Bias*

The Unconstrained Ear Recognition Challenge 2023: Maximizing...

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IEEE International Joint Conference on Biometrics (IJCB)

作者： Ž. Emeršić T. Ohki M. Akasaka T. Arakawa S. Maeda M. Okano Y. Sato A. George S. Marcel I. I. Ganapathi S. S. Ali S. Javed N. Werghi S. G. Işık E. Sarıtaş H. K. Ekenel V. Hudovernik J. N. Kolf F. Boutros N. damer G. Sharma A. Kamboj A. Nigam d. K. Jain G. Cámara-Chávez P. Peer V. Štruc University of Ljubljana (UL SI) Shizuoka University (SU JP) Idiap Research Institute (IDIAP CH) Electrical Engineering and Computer Science Department Khalifa University (KU UAE) Department of Computer Engineering (ITU TR) Istanbul Technical University Microelectronic Guidance and Electro-Optical Group ASELSAN Fraunhofer Institute for Computer Graphics Research IGD (IGD DE) Technische Universität Darmstadt (TUD DE) Indian Institute of Technology Mandi (IIT IN) HCL Imaging and Robotics R&D Lab (HCL IN) School of Artificial Intelligence Dalian University of Technology (DLUT CN) Federal University of Ouro Preto (UFOP BR)

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CS²-Net: deep learning segmentation of curvilinear structures in medical imaging

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MEdICAL IMAGE ANALYSIS 2021年 67卷 101874-101874页

作者： Mou, Lei Zhao, Yitian Fu, Huazhu Liu, Yonghuai Cheng, Jun Zheng, Yalin Su, Pan Yang, Jianlong Chen, Li Frangi, Alejandro F. Akiba, Masahiro Liu, Jiang Chinese Acad Sci Ningbo Inst Mat Technol & Engn Cixi Inst Biomed Engn Ningbo Peoples R China Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Incept Inst Artificial Intelligence Abu Dhabi U Arab Emirates Edge Hill Univ Dept Comp Sci Ormskirk England UBTech Robot Corp Ltd UBTech Res Shenzhen Peoples R China Univ Liverpool Dept Eye & Vis Sci Liverpool Merseyside England Wuhan Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Univ Leeds Sch Comp Ctr Computat Imaging & Simulat Technol Biomed CIS Leeds W Yorkshire England Univ Leeds Sch Med Leeds W Yorkshire England Univ Leeds Leeds Inst Cardiovasc & Metab Med Sch Med Leeds W Yorkshire England Southern Univ Sci & Technol Dept Comp Sci & Engn Guangdong Prov Key Lab Brain Inspired Intelligent Shenzhen Peoples R China Katholieke Univ Leuven Univ Hosp Gasthuisberg Cardiovasc Sci Dept Med Imaging Res Ctr MIRC Leuven Belgium Katholieke Univ Leuven Univ Hosp Gasthuisberg Elect Engn Dept Med Imaging Res Ctr MIRC Leuven Belgium Topcon Corp R&D Div Tokyo Japan

Automated detection of curvilinear structures, e.g., blood vessels or nerve fibres, from medical and biomedical images is a crucial early step in automatic image interpretation associated to the management of many diseases. Precise measurement of the morphological changes of these curvilinear organ structures informs clinicians for understanding the mechanism, diagnosis, and treatment of e.g. cardiovascular, kidney, eye, lung, and neurological conditions. In this work, we propose a generic and unified convolution neural network for the segmentation of curvilinear structures and illustrate in several 2d/3d medical imaging modalities. We introduce a new curvilinear structure segmentation network (CS2-Net), which includes a self-attention mechanism in the encoder and decoder to learn rich hierarchical representations of curvilinear structures. Two types of attention modules spatial attention and channel attention are utilized to enhance the inter-class discrimination and intra-class responsiveness, to further integrate local features with their global dependencies and normalization, adaptively. Furthermore, to facilitate the segmentation of curvilinear structures in medical images, we employ a 1x3 and a 3x1 convolutional kernel to capture boundary features. Besides, we extend the 2d attention mechanism to 3d to enhance the network's ability to aggregate depth information across different layers/slices. The proposed curvilinear structure segmentation network is thoroughly validated using both 2d and 3d images across six different imaging modalities. Experimental results across nine datasets show the proposed method generally outperforms other state-of-the-art algorithms in various metrics. (C) 2020 Published by Elsevier B.V.

关键词： Curvilinear structure Blood vessel Nerve fiber Segmentation Attention mechanism deep neural network

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Long-term ecological assessment of intracranial electrophysiology synchronized to behavioral markers in obsessive-compulsive disorder

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NATUrE MEdICINE 2021年第12期27卷 2154-+页

作者： Provenza, Nicole r. Sheth, Sameer A. dastin-van rijn, Evan M. Mathura, raissa K. ding, Yaohan Vogt, Gregory S. Avendano-Ortega, Michelle ramakrishnan, Nithya Peled, Noam Gelin, Luiz Fernando Fracassi Xing, david Jeni, Laszlo A. Ertugrul, Itir Onal Barrios-Anderson, Adriel Matteson, Evan Wiese, Andrew d. Xu, Junqian Viswanathan, Ashwin Harrison, Matthew T. Bijanki, Kelly r. Storch, Eric A. Cohn, Jeffrey F. Goodman, Wayne K. Borton, david A. Brown Univ Sch Engn Providence RI 02912 USA Charles Stark Draper Lab Cambridge MA USA Baylor Coll Med Dept Neurosurg Houston TX 77030 USA Univ Pittsburgh Intelligent Syst Program Pittsburgh PA USA Baylor Coll Med Menninger Dept Psychiat & Behav Sci Houston TX 77030 USA MGH HST Martinos Ctr Biomed Imaging Charlestown MA USA Harvard Med Sch Cambridge MA USA Brown Univ Ctr Computat & Visualizat Providence RI 02912 USA Carnegie Mellon Univ Robot Inst Pittsburgh PA 15213 USA Tilburg Univ Dept Cognit Sci & Artificial Intelligence Tilburg Netherlands Brown Univ Warren Alpert Med Sch Providence RI 02912 USA Univ Missouri Dept Psychol Kansas City MO 64110 USA Baylor Coll Med Dept Radiol Houston TX 77030 USA Brown Univ Div Appl Math Providence RI 02912 USA Univ Pittsburgh Dept Psychol Pittsburgh PA 15260 USA Brown Univ Carney Inst Brain Sci Providence RI 02912 USA Dept Vet Affairs Rehabil R&D Serv Ctr Neurorestorat & Neurotechnol Providence RI 02908 USA

detection of neural signatures related to pathological behavioral states could enable adaptive deep brain stimulation (dBS), a potential strategy for improving efficacy of dBS for neurological and psychiatric disorders. This approach requires identifying neural biomarkers of relevant behavioral states, a task best performed in ecologically valid environments. Here, in human participants with obsessive-compulsive disorder (OCd) implanted with recording-capable dBS devices, we synchronized chronic ventral striatum local field potentials with relevant, disease-specific behaviors. We captured over 1,000 h of local field potentials in the clinic and at home during unstructured activity, as well as during dBS and exposure therapy. The wide range of symptom severity over which the data were captured allowed us to identify candidate neural biomarkers of OCd symptom intensity. This work demonstrates the feasibility and utility of capturing chronic intracranial electrophysiology during daily symptom fluctuations to enable neural biomarker identification, a prerequisite for future development of adaptive dBS for OCd and other psychiatric disorders. The identification of candidate neural biomarkers of obsessive-compulsive disorder symptom intensity in ecologically valid environments.

关键词： Biomedical engineering Neurological disorders Translational research Biomedicine general Cancer research Metabolic diseases Infectious diseases Molecular Medicine Neurosciences

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Preface

Signals and Communication Technology

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Signals and Communication Technology 2012年 vii-x页

作者： das, Apurba Technical Lead Imaging Tech Lab Engineering and R&D HCL Technologies Ltd Chennai India

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Multiview Foreground Extraction and Composition to Multiview Background Using Trimap Sharing for Natural 3d Scene Generation

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INTErNATIONAL JOUrNAL OF imaging SYSTEMS ANd TECHNOLOGY 2010年第3期20卷 285-293页

作者： Hyun, Myung-Han Kim, Sung-Yeol Kang, Yun-Suk Ho, Yo-Sung Agcy Def Dev R&D Inst 4 1 Taejon 305600 South Korea Univ Tennessee Imaging Robot & Intelligent Syst Lab Knoxville TN 37996 USA Gwangju Inst Sci & Technol Dept Informat & Communicat Kwangju 500712 South Korea

digital matting for extracting foreground objects from an image is an important process to generate special effects in the movie industry and the broadcasting center. recently, a digital matting algorithm has been developed to create an alpha matte using a well-focused image generated from multiview images. However, this method could generate only a single-view alpha matte, even though it used multiple cameras. In this article, we propose a new estimation scheme for multiview alpha mattes by sharing the trimap of the reference view. Furthermore, we use the motion vector to update the trimap for video matting. After we extract foreground objects from all view images, we composite the foreground objects with the corresponding background images captured in the same multiview camera arrangement. Experimental results demonstrate that multiview composite images can generate reasonably natural 3d views through the stereoscopic monitor. (C) 2010 Wiley Periodicals, Inc. Int J imaging Syst Technol, 20, 285-293, 2010;View this article online at *** dOI 10.1002/ima.20251

关键词： multiview foreground extraction multiview camera system trimap sharing

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