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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Xinwen Liu Jing Wang S. Kevin Zhou Craig Engstrom Shekhar S. Chandra School of Electrical Engineering and Computer Science The University of Queensland Brisbane Australia The Commonwealth Scientific and Industrial Research Organisation Canberra Australia Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) School of Biomedical Engineering & Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Human Movement and Nutrition Sciences The University of Queensland Brisbane Australia

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

Deep neural networks have been widely studied to predict a medical condition, such as total knee replacement (TKR). It has shown that data of different modalities, such as imaging data, clinical variables, and demographic information, provide complementary information and thus can improve the prediction accuracy together. However, the data sources of various modalities may not always be of high quality, and each modality may have only partial information of medical condition. Thus, predictions from different modalities can be in conflict, and the final prediction may fail in the presence of such a conflict. Therefore, it is important to account for the reliability of each source data and the prediction output when making a final decision. In this paper, we propose an evidence-aware multimodal data fusion framework based on the Dempster-Shafer theory (DST). The backbone models contain an image branch, a non-image branch and a fusion branch. For each branch, there is an evidence network that takes the extracted features as input and outputs an evidence score, which is designed to represent the reliability of the output from the current branch. The output probabilities along with the evidence scores from multiple branches are combined with the Dempster's combination rule to make a final prediction. Experimental results on the public OA initiative (OAI) dataset for the TKR prediction task show that the proposed method has better performance by accounting for conflicts from various modalities.

关键词： Medical conditions Soft sensors Digital images Data integration Imaging Artificial neural networks Reliability theory Feature extraction Reliability engineering Knee replacement

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A Sparse-Point Guided Photometric Stereo Method for the Metal Complex Surfaces Measurement and High-Fidelity Geometry Recovery

SSRN

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SSRN 2023年

作者： Sun, Li Jian Cao, Weiqiang Bian, Yong Ren, Jie Ji Xu, Xiao Gang Intelligent Computing Platform Research Center Zhejiang Laboratory Zhejiang311121 China School of Computer Science Hangzhou Dianzi University Zhejiang310018 China School of Mechanical Engineering Institute of Robotics Shanghai Jiao Tong University Shanghai200240 China School of Computer and information Engineering Zhejiang Gongshang University Zhejiang310018 China

Accurate reconstruction of complex surfaces often requires informative measurement data with high accuracy, which poses a great challenge for measurement. In this paper, a contact sensor and a photometric stereo (PS) vision device are integrated together to recover the geometry with high-fidelity details. Then a two-branch deep PS neural network is developed and the Gaussian process regression (GPR) is used to reconstruct a global geometric shape from some sparse points captured by the contact probe. This prior geometry is sent to the proposed cross-convolution attention residual block (CCARB) based network to guide the PS for better normal recovery. The resulting normals are further fused with the sparse points by a normal integral model to obtain dense and high-accuracy points. Synthetic datasets are constructed to train the network. Experiments are conducted on both synthetic and real data, and show the superiority in the high-fidelity reconstruction for the measurement of complex surfaces. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust 3D Human Mesh Recovery

PostoMETRO: Pose Token Enhanced Mesh Transformer for Robust ...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wendi Yang Zihang Jiang Shang Zhao S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advance Research USTC Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

With the recent advancements in single-image-based 3D human pose and shape estimation (3DHPSE), there is a growing amount of works that can achieve good results on standard benchmarks but struggle to yield accurate hu-man mesh in extreme scenarios like occlusion. Previous works propose to leverage 2D poses to help 3D HPSE model improve performance under occlusion, but usually rely on manual design to integrate 2D poses and only aim for specific kinds of occlusion. In this paper, we present PostoMETRO (Pose token enhanced MEsh TRansfOrmer), which integrates 2D pose prior knowledge as tokens into transformers to improve model's performance under occlusion. Using a VQ- VAE-based pose tokenizer; we efficiently represent 2D poses as tokens andfeed them to transformers together with image tokens. Subsequently, these tokens are queried by vertex andJoint tokens to decode 3D coordinates of mesh vertices and human Joints. Our proposed 2D poses integration strategy is manual-design-free and suitable for various kinds of occlusion. Experiments on both standard and occlusion-specific benchmarks demonstrate the effectiveness of PostoMETRO. Code will be made available 1 1 https://***/PostoMETRO/PostoMETRO-Paper.

关键词： Solid modeling Computer vision Three-dimensional displays Shape Estimation Manuals Benchmark testing Transformers Robustness Standards

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Toward learning context-dependent tasks from demonstration for tendon-driven surgical robots

arXiv

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arXiv 2021年

作者： Huang, Yixuan Bentley, Michael Hermans, Tucker Kuntz, Alan The Robotics Center School of Computing University of Utah Salt Lake CityUT84112 United States NVIDIA

Tendon-driven robots, a type of continuum robot, have the potential to reduce the invasiveness of surgery by enabling access to difficult-to-reach anatomical targets. In the future, the automation of surgical tasks for these robots may help reduce surgeon strain in the face of a rapidly growing population. However, directly encoding surgical tasks and their associated context for these robots is infeasible. In this work we take steps toward a system that is able to learn to successfully perform context-dependent surgical tasks by learning directly from a set of expert demonstrations. We present three models trained on the demonstrations conditioned on a vector encoding the context of the demonstration. We then use these models to plan and execute motions for the tendon-driven robot similar to the demonstrations for novel context not seen in the training set. We demonstrate the efficacy of our method on three surgery-inspired tasks. Copyright © 2021, The Authors. All rights reserved.

关键词： Demonstrations

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Planning sensing sequences for subsurface 3D tumor mapping

arXiv

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arXiv 2021年

作者： Cho, Brian Y. Hermans, Tucker Kuntz, Alan Robotics Center and School of Computing University of Utah Salt Lake CityUT84112 United States NVIDIA

— Surgical automation has the potential to enable increased precision and reduce the per-patient workload of overburdened human surgeons. An effective automation system must be able to sense and map subsurface anatomy, such as tumors, efficiently and accurately. In this work, we present a method that plans a sequence of sensing actions to map the 3D geometry of subsurface tumors. We leverage a sequential Bayesian Hilbert map to create a 3D probabilistic occupancy model that represents the likelihood that any given point in the anatomy is occupied by a tumor, conditioned on sensor readings. We iteratively update the map, utilizing Bayesian optimization to determine sensing poses that explore unsensed regions of anatomy and exploit the knowledge gained by previous sensing actions. We demonstrate our method’s efficiency and accuracy in three anatomical scenarios including a liver tumor scenario generated from a real patient’s CT scan. The results show that our proposed method significantly outperforms comparison methods in terms of efficiency while detecting subsurface tumors with high accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Tumors

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Quadruped Guidance Robot for the Visually Impaired: A Comfort-Based Approach

arXiv

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arXiv 2022年

作者： Chen, Yanbo Xu, Zhengzhe Jian, Zhuozhu Tang, Gengpan Yang, Liyunong Xiao, Anxing Wang, Xueqian Liang, Bin The School of Mechanical Engineering and Automation Harbin Institute of Technology Shenzhen518055 China The Center for Artificial Intelligence and Robotics Shenzhen International Graduate School Tsinghua University Shenzhen518055 China The School of Computing National University of Singapore Singapore117417 Singapore

Guidance robots that can guide people and avoid various obstacles, could potentially be owned by more visually impaired people at a fairly low cost. Most of the previous guidance robots for the visually impaired ignored the human response behavior and comfort, treating the human as an appendage dragged by the robot, which can lead to imprecise guidance of the human and sudden changes in the traction force experienced by the human. In this paper, we propose a novel quadruped guidance robot system with a comfort-based concept. We design a controllable traction device that can adjust the length and force between human and robot to ensure comfort. To allow the human to be guided safely and comfortably to the target position in complex environments, our proposed human motion planner can plan the traction force with the force-based human motion model. To track the planned force, we also propose a robot motion planner that can generate the specific robot motion command and design the force control device. Our system has been deployed on Unitree Laikago quadrupedal platform and validated in real-world scenarios. (Video1). Copyright © 2022, The Authors. All rights reserved.

关键词： Robots

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Visually-Guided Sound Source Separation with Audio-Visual Predictive Coding

arXiv

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arXiv 2023年

作者： Song, Zengjie Zhang, Zhaoxiang The School of Mathematics and Statistics Xi’an Jiaotong University Xi’an710049 China The Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China The Center for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Hong Kong

The framework of visually-guided sound source separation generally consists of three parts: visual feature extraction, multimodal feature fusion, and sound signal processing. An ongoing trend in this field has been to tailor involved visual feature extractor for informative visual guidance and separately devise module for feature fusion, while utilizing U-Net by default for sound analysis. However, such divide-and-conquer paradigm is parameter inefficient and, meanwhile, may obtain suboptimal performance as jointly optimizing and harmonizing various model components is challengeable. By contrast, this paper presents a novel approach, dubbed audio-visual predictive coding (AVPC), to tackle this task in a parameter efficient and more effective manner. The network of AVPC features a simple ResNet-based video analysis network for deriving semantic visual features, and a predictive coding-based sound separation network that can extract audio features, fuse multimodal information, and predict sound separation masks in the same architecture. By iteratively minimizing the prediction error between features, AVPC integrates audio and visual information recursively, leading to progressively improved performance. In addition, we develop a valid self-supervised learning strategy for AVPC via co-predicting two audio-visual representations of the same sound source. Extensive evaluations demonstrate that AVPC outperforms several baselines in separating musical instrument sounds, while reducing the model size significantly. Code is available at: https://***/zjsong/Audio-Visual-Predictive-Coding. Copyright © 2023, The Authors. All rights reserved.

关键词： Supervised learning

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EDA: Enhanced Domain-Adversarial Training for Anatomical Landmark Detection

EDA: Enhanced Domain-Adversarial Training for Anatomical Lan...

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IEEE International Symposium on Biomedical Imaging

作者： Fan Yang S. Kevin Zhou School of Biomedical Engineering Division of Life Sciences and Medicine University of Science and Technology of China Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou Jiangsu China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Manually annotating anatomical landmarks in medical images requires experienced clinicians and is a labor-intensive process. However, recent AI-assisted methods for landmark detection often rely on the training and test data originating from the same domain. This work introduces a novel unsupervised domain adaptation (UDA) framework aimed at anatomical landmark detection from a medical image, designed to bridge the gap between a source domain with labels and an unlabeled target domain. Specifically, we have developed a new Domain-Adversarial Network that incorporates skip connections to transfer and fuse high-resolution feature maps. Additionally, we proposed Dynamic Gaussian Learning, which allows the model to escape from local error regions. We carry out experiments on landmark detection for both head and chest, and the results demonstrate that our method achieves state-of-the-art performances in each experiment.

关键词： Training Measurement Adaptation models Three-dimensional displays Smoothing methods Head Kernel Biomedical imaging

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Towards Accurate Unified Anomaly Segmentation

Towards Accurate Unified Anomaly Segmentation

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Wenxin Ma Qingsong Yao Xiang Zhang Zhelong Huang Zihang Jiang S. Kevin Zhou Division of Life Sciences and Medicine School of Biomedical Engineering University of Science and Technology of China (USTC) Hefei Anhui China Center for Medical Imaging Robotics Analytic Computing & Learning (MIRACLE) USTC Suzhou Institute for Advance Research Suzhou Jiangsu China Stanford University Palo Alto California United State School of Medicine Shanghai University Shanghai China Key Laboratory of Precision and Intelligent Chemistry USTC Hefei Anhui China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Unsupervised anomaly detection (UAD) from images strives to model normal data distributions, creating discriminative representations to distinguish and precisely localize anomalies. Despite recent advancements in the efficient and unified one-for-all scheme, challenges persist in accurately segmenting anomalies for further monitoring. Moreover, this problem is obscured by the widely-used AUROC metric under imbalanced UAD settings. This motivates us to emphasize the significance of precise segmentation of anomaly pixels using pAP and DSC as metrics. To address the unsolved segmentation task, we introduce the Unified Anomaly Segmentation (UniAS). UniAS presents a multi-level hybrid pipeline that progressively enhances normal information from coarse to fine, incorporating a novel multi-granularity gated CNN (MGG-CNN) into Transformer layers to explicitly aggregate local details from different granularities. UniAS achieves state-of-the-art anomaly segmentation performance, attaining 65.12/59.33 and 40.06/32.50 in pAP/DSC on the MVTec-AD and VisA datasets, respectively, surpassing previous methods significantly. The codes are shared at https://***/Mwxinnn/UniAS.

关键词： Measurement Image segmentation Computer vision Codes Pipelines Logic gates Transformers Data models Monitoring Anomaly detection

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“I'm a little less inclined to do it”: How Afterschool Programs’ Culture Impact Co-Design Processes and Outcomes 23

“I'm a little less inclined to do it”: How Afterschool Pro...

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Proceedings of the 2023 ACM Designing Interactive Systems Conference

作者： Judith Odili Uchidiuno Jaemarie Solyst Erik Harpstead Ross Higashi School of Interactive Computing Georgia Institute of Technology United States and Human Computer Interaction Institute Carnegie Mellon University United States Human-Computer Interaction Institute Carnegie Mellon University United States National Robotics Engineering Center Carnegie Mellon University United States

ISBN: (纸本)9781450398930

The importance of considering local context and partnering with target users is well established in co-design. Less common is an examination of the adaptations needed when deploying the same co-design program across heterogenous settings to maximize program efficacy and equity. We report on our experience co-designing educational games with six culturally and socioeconomically diverse afterschool sites over two years, and insights from interviewing ten program administrators across all sites. We found that even within the same afterschool program network, site differences in organizational culture and resources impacted the effectiveness of co-design programs, the co-design output, and expectations for student engagement. We characterize our afterschool partners into different archetypes – Safe Havens, Recreation centers, Homework Helpers, and STEM Enrichment centers. We provide recommendations for conducting co-design at each archetype and reflect on strategies for increasing equitable partnerships between researchers and afterschool centers.

关键词： Participatory Design Cooperative Inquiry Organizational Culture Afterschool Programs Informal Learning Codesign Children Design Methods

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