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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Liu, Haoyu Pu, Yuanyuan Ni, Jun Wang, Linfei Wang, Xiongbin Tao, Dapeng Yang, Yanhua School Of Information Science And Engineering Yunnan University Kunming China The Second Affiliated Hospital Of Kunming Medical University Information Statistics Center Kunming China School Of Computer Science And Technology Xidian University Xi'an China

ISBN: (纸本)9798350361445

Deformable registration serves as the foundation for longitudinal and population-based medical image analysis. In cross-sectional studies of brain magnetic resonance imaging (MRI), accurately aligning the regions of interest (ROIs) of various subjects, especially in the context of specific disease analysis, poses a challenge due to the complexity of brain structures and individual variations. The existing research predominantly utilizes the convolutional neural networks (CNNs) for learning global deformable registration, with little attention paid to the independent performance of neural networks on specific anatomical tissues. To address above problem, this paper proposed an anatomy-aware deep learning approach from local optimization to global information for medical deformable registration. In the method, the information of special brain anatomical tissue structure is selected to optimize the registration accuracy for the target region, and the anatomical perception constraint is designed to capture the information of these structures, so as to ensure the physiological rationality of the final registration deformation field. Compared with the recently developed state-of-the-art approaches, the proposed registration method can achieve the highest registration accuracy on both specific anatomical tissues and the overall image texture, and maintain image smoothness. © 2024 IEEE.

关键词： Tissue

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DiLiGenT-Π: Photometric Stereo for Planar Surfaces with Rich Details - Benchmark Dataset and Beyond

DiLiGenT-Π: Photometric Stereo for Planar Surfaces with Ric...

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2023 IEEE/CVF International Conference on computer Vision, ICCV 2023

作者： Wang, Feishi Ren, Jieji Guo, Heng Ren, Mingjun Shi, Boxin Peking University National Key Laboratory for Multimedia Information Processing School of Computer Science China Peking University National Engineering Research Center of Visual Technology School of Computer Science China Peking University Ai Innovation Center School of Computer Science China Shanghai Jiao Tong University School of Mechanical Engineering China Beijing University of Posts and Telecommunications School of Artificial Intelligence China Osaka University Japan

ISBN: (纸本)9798350307184

Photometric stereo aims to recover detailed surface shapes from images captured under varying illuminations. However, existing real-world datasets primarily focus on evaluating photometric stereo for general non-Lambertian reflectances and feature bulgy shapes that have a certain height. As shape detail recovery is the key strength of photometric stereo over other 3D reconstruction techniques, and the near-planar surfaces widely exist in cultural relics and manufacturing workpieces, we present a new real-world dataset DiLiGenT-Π containing 30 near-planar scenes with rich surface details. This dataset enables us to evaluate recent photometric stereo methods specifically for their ability to estimate shape details under diverse materials and to identify open problems such as near-planar surface normal estimation from uncalibrated photometric stereo and surface detail recovery for translucent materials. To inspire future research, this dataset will open soruced at https://***/***. © 2023 IEEE.

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A Provable Decision Rule for Out-of-Distribution Detection 41

A Provable Decision Rule for Out-of-Distribution Detection

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41st International Conference on Machine Learning, ICML 2024

作者： Ma, Xinsong Zou, Xin Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Out-of-distribution (OOD) detection task plays the key role in reliable and safety-critical applications. Existing researches mainly devote to designing or training the powerful score function but overlook investigating the decision rule based on the proposed score function. Different from previous work, this paper aims to design a decision rule with rigorous theoretical guarantee and well empirical performance. Specifically, we provide a new insight for the OOD detection task from a hypothesis testing perspective and propose a novel generalized Benjamini Hochberg (g-BH) procedure with empirical p-values to solve the testing problem. Theoretically, the g-BH procedure controls false discovery rate (FDR) at pre-specified level. Furthermore, we derive an upper bound of the expectation of false positive rate (FPR) for the g-BH procedure based on the tailed generalized Gaussian distribution family, indicating that the FPR of g-BH procedure converges to zero in probability. Finally, the extensive experimental results verify the superiority of g-BH procedure over the traditional threshold-based decision rule on several OOD detection benchmarks. Copyright 2024 by the author(s)

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Devil is in Details: Locality-Aware 3D Abdominal CT Volume Generation for Self-Supervised Organ Segmentation 24

Devil is in Details: Locality-Aware 3D Abdominal CT Volume G...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Wang, Yuran Wan, Zhijing Qiu, Yansheng Wang, Zheng National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

ISBN: (纸本)9798400706868

In the realm of medical image analysis, self-supervised learning (SSL) techniques have emerged to alleviate labeling demands, while still facing the challenge of training data scarcity owing to escalating resource requirements and privacy constraints. Numerous efforts employ generative models to generate high-fidelity, unlabeled 3D volumes across diverse modalities and anatomical regions. However, the intricate and indistinguishable anatomical structures within the abdomen pose a unique challenge to abdominal CT volume generation compared to other anatomical regions. To address the overlooked challenge, we introduce the Locality-Aware Diffusion (Lad), a novel method tailored for exquisite 3D abdominal CT volume generation. We design a locality loss to refine crucial anatomical regions and devise a condition extractor to integrate abdominal priori into generation, thereby enabling the generation of large quantities of high-quality abdominal CT volumes essential for SSL tasks without the need for additional data such as labels or radiology reports. Volumes generated through our method demonstrate remarkable fidelity in reproducing abdominal structures, achieving a decrease in FID score from 0.0034 to 0.0002 on AbdomenCT-1K dataset, closely mirroring authentic data and surpassing current methods. Extensive experiments demonstrate the effectiveness of our method in self-supervised organ segmentation tasks, resulting in an improvement in mean Dice scores on two abdominal datasets effectively. These results underscore the potential of synthetic data to advance self-supervised learning in medical image analysis. © 2024 ACM.

关键词： Self-supervised learning

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PCA Lab's solution for RePSS 2024 by vision-based self-supervised remote heart rate sensing 3

PCA Lab's solution for RePSS 2024 by vision-based self-super...

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3rd Vision-Based Remote Physiological Signal Sensing Challenge and Workshop, RePSS 2024

作者： Shao, Hang Luo, Lei Qian, Jianjun Zhuo, Wei Hu, Chuanfei Yang, Jian PCA Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Automation Southeast University Nanjing211189 China Nanjing Center for Applied Mathematics Nanjing211135 China

Vision-based remote non-contact physiological measurements are crucial for detecting indicators (such as heart rate and blood pressure) that reflect important vital signs. This paper introduces the approach proposed by PCA Lab in the third challenge on Vision-based Remote Physiological Signal Sensing (RePSS) organized within IJCAI 2024. Specifically, we design an end-to-end self-supervised contrastive learning network for remote heart rate detection, which can generalize light reflection changes caused by cardiac activity in subcutaneous capillaries based on unlabeled facial videos. At the same time, we optimize the facial skin region of interest extraction method and remove most of the irrelevant content and redundant information. In addition, we construct the hybrid pipeline temporal attention module and spatiotemporal reconstruction pretraining paradigm to improve the network's ability to model long-distance sequence features. Our network is tested on 1000 samples from 200 participants provided by the Track 1 of this challenge, and it achieve the root mean squared error of 8.96941. The codes and model are publicly available at https://***/Sachiel0916/repss-track1-top3/. © 2024 Copyright for this paper by its authors.

关键词： contrastive learning remote heart rate sensing RePSS self-supervision framework spatiotemporal reconstruction

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Cloth-aware Augmentation for Cloth-generalized Person Re-identification 24

Cloth-aware Augmentation for Cloth-generalized Person Re-ide...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Liu, Fangyi Ye, Mang Du, Bo National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence School of Computer Science Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

ISBN: (纸本)9798400706868

Person re-identification (ReID) is crucial in video surveillance, aiming to match individuals across different camera views while cloth-changing person re-identification (CC-ReID) focuses on pedestrians changing attire. Many existing CC-ReID methods overlook generalization, crucial for universality across cloth-consistent and cloth-changing scenarios. This paper pioneers exploring the cloth-generalized person re-identification (CG-ReID) task and introduces the Cloth-aware Augmentation (CaAug) strategy. Comprising domain augmentation and feature augmentation, CaAug aims to learn identity-relevant features adaptable to both scenarios. Domain augmentation involves creating diverse fictitious domains and simulating various clothing scenarios. Supervising features from different cloth domains enhances robustness and generalization against clothing changes. Additionally, for feature augmentation, element exchange introduces diversity concerning clothing changes. Regularizing the model with these augmented features strengthens resilience against clothing change uncertainty. Extensive experiments on cloth-changing datasets demonstrate the efficacy of our approach, consistently outperforming state-of-the-art methods. © 2024 ACM.

关键词： Feature extraction

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Sequential Kernel Goodness-of-fit Testing 41

Sequential Kernel Goodness-of-fit Testing

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41st International Conference on Machine Learning, ICML 2024

作者： Zhou, Zhengyu Liu, Weiwei School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan China

Goodness-of-fit testing, a classical statistical tool, has been extensively explored in the batch setting, where the sample size is ***, practitioners often prefer methods that adapt to the complexity of a problem rather than fixing the sample size *** batch tests are generally unsuitable for streaming data, as valid inference after data peeking requires multiple testing corrections, resulting in reduced statistical *** address this issue, we delve into the design of consistent sequential goodness-of-fit *** the principle of testing by betting, we reframe this task as selecting a sequence of payoff functions that maximize the wealth of a fictitious bettor, betting against the null in a repeated *** conduct experiments to demonstrate the adaptability of our sequential test across varying difficulty levels of problems while maintaining control over type-I errors. Copyright 2024 by the author(s)

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Efficient Skeleton-Based Human Activity Recognition in Ambient Assisted Living Scenarios with Multi-View CNN

Efficient Skeleton-Based Human Activity Recognition in Ambie...

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IEEE/RAS-EMBS International Conference on Biomedical robotics and Biomechatronics (BioRob)

作者： Mohamad Reza Shahabian Alashti Mohammad Hossein Bamorovat Abadi Patrick Holthaus Catherine Menon Farshid Amirabdollahian Robotics Research Group School of Engineering and Computer Science University of Hertfordshire Hatfield United Kingdom

ISBN: (数字)9798350386523

ISBN: (纸本)9798350386530

Human activity recognition (HAR) plays a critical role in diverse applications and domains, from assessments of ambient assistive living (AAL) settings and the development of smart environments to human-robot interaction (HRI) scenarios. However, using mobile robot cameras in such contexts has limitations like restricted field of view and possible noise. Therefore, employing additional fixed cameras can enhance the field of view and reduce susceptibility to noise. Never-theless, integrating additional camera perspectives increases complexity, a concern exacerbated by the number of real-time processes that robots should perform in the AAL scenario. This paper introduces our methodology that facilitates the combination of multiple views and compares different aspects of fusing information at low, medium and high levels. Their comparison is guided by parameters such as the number of training parameters, floating-point operations per second (FLOPs), training time, and accuracy. Our findings uncover a paradigm shift, challenging conventional beliefs by demonstrating that simplistic CNN models outperform their more complex counterparts using this innovation. Additionally, the pivotal role of pipeline and data combination emerges as a crucial factor in achieving better accuracy levels. In this study, integrating the additional view with the Robot-view resulted in an accuracy increase of up to 25 %. Ultimately, we have successfully attained a streamlined and efficient multi-view HAR pipeline, which will now be incorporated into AAL interaction scenarios.

关键词： Training Technological innovation Accuracy Robot vision systems Pipelines Noise Human-robot interaction Cameras Real-time systems Human activity recognition

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Decoding White Matter Fiber ODFs: A Mixture Learning Framework in x-q Space

Decoding White Matter Fiber ODFs: A Mixture Learning Framewo...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Ma, Jiquan Deng, Chengdong Chen, Geng Jiang, Haotian Huang, Shijie Kim, Jaeil Wen, Xuyun Shen, Dinggang Heilongjiang University Department of Computer Science and Technology Harbin China Northwestern Polytechnical University School of Computer Science and Engineering Xi'an China Kyungpook National University School of Computer Science and Engineering Daegu Korea Republic of Nanjing University of Aeronautics and Astronautics School of Computer Science and Technology Nanjing China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai China Shanghai United Imaging Intelligence Co. Ltd. Shanghai China Shanghai Clinical Research and Trial Center Shanghai China

ISBN: (纸本)9798350386226

Diffusion magnetic resonance imaging (dMRI), as a powerful non-invasive white matter imaging technology, plays an important role in studying brain white matter. The fiber orientation distribution functions (fODFs) derived from dMRI data provide the key directional information of fiber tracts for revealing the 3D geometric structure of brain white matter. The estimation of fODFs faces two challenges, including (i) the demand for dMRI data densely sampled in q-space and (ii) the joint consideration of x-q space. To address these challenges, we propose a mixture learning framework with q-space sparely sampled dMRI data as input. Specifically, we propose an x-space learning module based on 3D U-Net to learn x-space features and a q-space learning module based on spherical convolutional neural networks to learn q-space features. Two kinds of features are then fused with a mixture learning fusion module for fODFs estimation. The whole framework is supervised with an x-q space loss function. Our framework makes full use of joint x-q space information for fODFs estimation with clinically available q-space sparsely sampled dMRI data. Extensive experiments on three public datasets show that our framework is effective in fODFs estimation and outperforms cutting-edge models. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Toward a Low-Cost Perception System in Autonomous Vehicles: A Spectrum Learning Approach

arXiv

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arXiv 2025年

作者： Alsakabi, Mohammed Erickson, Aidan Dolan, John M. Tonguz, Ozan K. Department of Electrical and Computer Engineering College of Engineering The Robotics Institute School of Computer Science Carnegie Mellon University PittsburghPA United States

We present a cost-effective new approach for generating denser depth maps for Autonomous Driving (AD) and Autonomous Vehicles (AVs) by integrating the images obtained from deep neural network (DNN) 4D radar detectors with conventional camera RGB images. Our approach introduces a novel pixel positional encoding algorithm inspired by Bartlett’s spatial spectrum estimation technique. This algorithm transforms both radar depth maps and RGB images into a unified pixel image subspace called the Spatial Spectrum, facilitating effective learning based on their similarities and differences. Our method effectively leverages high-resolution camera images to train radar depth map generative models, addressing the limitations of conventional radar detectors in complex vehicular environments, thus sharpening the radar output. We develop spectrum estimation algorithms tailored for radar depth maps and RGB images, a comprehensive training framework for data-driven generative models, and a camera-radar deployment scheme for AV operation. Our results demonstrate that our approach also outperforms the state-of-the-art (SOTA) by 27.95% in terms of Unidirectional Chamfer Distance (UCD). © 2025, CC BY-NC-ND.

关键词： Radar imaging

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