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Dynamic View Synthesis from Small Camera Motion Videos

IEEE Transactions on Visualization and Computer Graphics 2025年 PP卷 PP页

作者： Sun, Huiqiang Li, Xingyi Peng, Juewen Shen, Liao Cao, Zhiguo Xian, Ke Lin, Guosheng Huazhong University of Science and Technology Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Wuhan 430074 China Nanyang Technological University (NTU) S-Lab 639798 Singapore Huazhong University of Science and Technology School of Electronic Information and Communications Wuhan 430074 China

Novel view synthesis for dynamic 3D scenes poses a significant challenge. Many notable efforts use NeRF-based approaches to address this task and yield impressive results. However, these methods rely heavily on sufficient motion parallax in the input images or videos. When the camera motion range becomes limited or even stationary (i.e., small camera motion), existing methods encounter two primary challenges: incorrect representation of scene geometry and inaccurate estimation of camera parameters. These challenges make prior methods struggle to produce satisfactory results or even become invalid. To address the first challenge, we propose a novel Distribution-based Depth Regularization (DDR) that ensures the rendering weight distribution to align with the true distribution. Specifically, unlike previous methods that use depth loss to calculate the error of the expectation, we calculate the expectation of the error by using Gumbel-softmax to differentiably sample points from discrete rendering weight distribution. Additionally, we introduce constraints that enforce the volume density of spatial points before the object boundary along the ray to be near zero, ensuring that our model learns the correct geometry of the scene. To demystify the DDR, we further propose a visualization tool that enables observing the scene geometry representation at the rendering weight level. For the second challenge, we incorporate camera parameter learning during training to enhance the robustness of our model to camera parameters. We conduct extensive experiments to demonstrate the effectiveness of our approach in representing scenes with small camera motion input, and our results compare favorably to state-of-the-art methods. © 1995-2012 IEEE.

关键词： Dynamic NeRF Neural radiance field Novel view synthesis Small camera motion

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Formation-containment control of multi-robot systems under a stochastic sampling mechanism

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Science China(Technological Sciences) 2020年第6期63卷 1025-1034页

作者： SU HouSheng ZHANG JinXin ZENG ZhiGang Key Laboratory of Image Processing and Intelligence Control of Ministry of Education School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

This paper studies the problem of formation-containment for multi-robot systems with stochastic ***,a stochastic sampling control protocol is proposed,in which information exchanging among robots only occurred at the sampling time and two different sampling periods randomly ***,both energy and controller updating frequencies can be ***,the protocol can be applied to the situation where the sampling period varies ***,sufficient conditions guaranteeing mean square formation-containment are *** stochastic sampling mechanism,the leaders reach a geometric formation shape and the followers are in the geometric formation shape formed by the ***,an example is shown to demonstrate the results.

关键词： formation-containment multi-robot systems stochastic sampling

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Dual-mode predictive control of a rotor suspension system

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Science China(Information Sciences) 2020年第1期63卷 178-189页

作者： Yue WU Gui-Ping REN Hai-Tao ZHANG Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and Technology State Key Laboratory of Digital Manufacturing Equipments and Technology

Rotor active magnetic bearing(rotor-AMB) systems are frequently used to alleviate vibrations for various applications such as in national defense, manufacturing industries, IC production, and aerospace engineering. One obstacle to improve machining efficiency and quality is the open-loop instability of rotorAMB systems during the machining process. We built a closed-loop processing platform using a spindle rotor installed with AMBs and thereby developed a rotor-AMB suspension system embedded with a dual-mode predictive controller(DMPC). The performance of the system is thus substantially improved. In the proposed DMPC, both model-based prediction and receding horizon optimization are utilized to guarantee the closedloop stability of the rotor-AMB suspension systems with input constraints. Finally, the effectiveness and superiority of the proposed method are examined through substantial levitation experiments on a machining platform with installed AMBs.

关键词： dual-mode predictive control system identification optimization control model predictive control suspension control

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Boundary-Guided Feature-Aligned Network for Colorectal Polyp Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Yue, Guanghui Wu, Shangjie Li, Gang Zhao, Cheng Hao, Yi Zhou, Tianwei Zhao, Baoquan National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School Shenzhen University Shenzhen518060 China The Marshall Laboratory of Biomedical Engineering Shenzhen University Shenzhen518060 China Baicheng Normal University School of Mechanical and Control Engineering Baicheng137000 China Shenzhen University Department of Ultrasound Medicine South China Hospital Shenzhen518060 China Shenzhen University College of Management Shenzhen518060 China Sun Yat-sen University School of Artificial Intelligence Guangzhou China

Colorectal polyp segmentation in endoscopic images is very important for the prevention and treatment of colorectal cancer. Because of the high similarity between polyps and their surrounding tissues, most deep neural network (DNN) based methods often struggle with blurry boundaries and result in inaccurate segmentation. In this paper, we propose a Boundary-guided Feature-aligned Network (BFNet) for polyp segmentation by taking a boundary prediction task as an auxiliary. Firstly, BFNet aggregates multi-layer features extracted from the backbone to mine boundary cues. Secondly, a flexible feature aggregation (FFA) module is used at each layer to adaptively fuse cross-layer features for coarse polyp localization. In the FFA module, considering the spatial misalignment between features at different layers, the feature of the high layer is aligned to and fused with that of the current layer using the deformable convolution and flexible merge block. After that, a boundary-guided feature enhancement (BFE) module is applied to refine the localization at boundary areas. In the BFE module, the boundary information is extracted and highlighted in both channel and spatial dimensions using the attention mechanisms with the assistance of boundary cues. By applying deep supervision to the BFE modules, BFNet can produce accurate polyp segmentation. Experimental results show that our BFNet outperforms 14 state-of-the-art DNN-based polyp segmentation methods on both in-domain and out-of-domain tests. © 1991-2012 IEEE.

关键词： Deep neural networks

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Neural-Network-Based Nonlinear Model Predictive Tracking control of a Pneumatic Muscle Actuator-Driven Exoskeleton

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IEEE/CAA Journal of Automatica Sinica 2020年第6期7卷 1478-1488页

作者： Yu Cao Jian Huang IEEE Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China

Pneumatic muscle actuators(PMAs)are compliant and suitable for robotic devices that have been shown to be effective in assisting patients with neurologic injuries,such as strokes,spinal cord injuries,etc.,to accomplish rehabilitation ***,because PMAs have nonlinearities,hysteresis,and uncertainties,etc.,complex mechanisms are rarely involved in the study of PMA-driven robotic *** this paper,we use nonlinear model predictive control(NMPC)and an extension of the echo state network called an echo state Gaussian process(ESGP)to design a tracking controller for a PMA-driven lower limb *** dynamics of the system include the PMA actuation and mechanism of the leg orthoses;thus,the system is represented by two nonlinear uncertain *** facilitate the design of the controller,joint angles of leg orthoses are forecasted based on the universal approximation ability of the ESGP.A gradient descent algorithm is employed to solve the optimization problem and generate the control *** stability of the closed-loop system is guaranteed when the ESGP is capable of approximating system *** and experiments are conducted to verify the approximation ability of the ESGP and achieve gait pattern training with four healthy subjects.

关键词： Echo state Gaussian process model predictive control neural network pneumatic muscle actuators-driven exoskeleton

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A novel image distance based on high-order manifold approximation

International Journal of Digital Content Technology and its ...

引用

International Journal of Digital Content Technology and its Applications 2011年第11期5卷 126-133页

作者： Zhou, Hua Ding, Mingyue Cai, Chao The Institute for Pattern Recognition and Artificial Intelligence National Key Laboratory of Science and Technology on Multi-spectral Information Processing Huazhong University of Science and Technology China School of Life Science and Technology Key Laboratory of Education Ministry for Imaging Processing and Intelligence Control Huazhong University of Science and Technology China

Tangent distance measures image similarity in a manifold way and is specific for handwritten digit recognition. However, in tangent distance metric the transformation should be known a priori and nonlinear manifolds are only approximated by first-order tangent hyperplanes. We propose a new image distance metric - the high-order approximated manifold distance (HMD) which can overcome these defects. The intrinsic variables of image transformation are learned by a special manifold learning algorithm - Maximum Variance Unfolding (MVU). Then nonlinear manifold is approximated by curve surface based on higher-order Taylor expansion with respect to intrinsic variables. HMD is defined as the minimum distance between the approximated curved surfaces of manifolds, and can be directly utilized in distance-based classifiers for image recognition. A series of face recognition and handwritten digit recognition experiments demonstrate that HMD not only achieves higher recognition accuracy but also has more stability of classification than several state-of-the-art distance metrics.

关键词： Face recognition

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Image matching navigation based on fuzzy information

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Journal of Harbin Institute of Technology(New Series) 2003年第4期10卷 447-449页

作者：田玉龙吴伟仁田金文柳健 State Key Laboratory for Image Processing and Intelligence Control Institute for Pattern Recognition and Artificial Intelligence Huazhong University of Science and Technology

In conventional image matching methods, the image matching process is mostly based on image statistic information. One aspect neglected by all these methods is that there is much fuzzy information contained in these images. A new fuzzy matching algorithm based on fuzzy similarity for navigation is presented in this paper. Because the fuzzy theory is of the ability of making good description of the fuzzy information contained in images, the image matching method based on fuzzy similarity would look forward to producing good performance results. Experimental results using matching algorithm based on fuzzy information also demonstrate its reliability and practicability.

关键词： image matching fuzzy information similarity

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High performance reconfigurable hardware system for real-time image processing

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Journal of Systems Engineering and Electronics 2005年第3期16卷 502-509页

作者：赵广州张天序王岳环曹治国左峥嵘 State Key Laboratory for I mage Processing and Intelligent Control Inst .for Pattern Recognition and Artificial Intelligence Huazhong Univ .of Science and Technology

A novel reconfigurable hardware system which uses both muhi-DSP and FPGA to attain high performance and real-time image processing are presented. The system structure and working principle of mainly processing multi-BSP board, extended multi-DSP board are analysed. The outstanding advantage is that the communication among different board components of this system is supported by high speed link ports ＆ serial ports for increasing the system performance and computational power. Then the implementation of embedded real-time operating systems （RTOS） by us is discussed in detail. In this system, we adopt two kinds of parallel structures controlled by RTOS for parallel processing of algorithms. The experimental results show that exploitive period of the system is short, and maintenance convenient. Thus it is suitable for real-time image processing and can get satisfactory effect of image recognition.

关键词： multi-DSP fidd programmable gate arrays real-time image processing real time operating systems parallel structure.

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Deep Learning-Based Diffusion MRI Tractography: Integrating Spatial and Anatomical Information

arXiv

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arXiv 2025年

作者： Yang, Yiqiong Yuan, Yitian Ren, Baoxing Wu, Ye Feng, Yanqiu Zhang, Xinyuan School of Biomedical Engineering Southern Medical University Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou510515 China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence & Key Laboratory of Mental Health The Ministry of Education Southern Medical University Guangzhou China Department of Radiology Shunde Hospital Southern Medical University The First People's Hospital of Shunde Foshan Foshan China

Diffusion MRI tractography technique enables non-invasive visualization of the white matter pathways in the brain. It plays a crucial role in neuroscience and clinical fields by facilitating the study of brain connectivity and neurological disorders. However, the accuracy of reconstructed tractograms has been a longstanding challenge. Recently, deep learning methods have been applied to improve tractograms for better white matter coverage, but often comes at the expense of generating excessive false-positive connections. This is largely due to their reliance on local information to predict long-range streamlines. To improve the accuracy of streamline propagation predictions, we introduce a novel deep learning framework that integrates image-domain spatial information and anatomical information along tracts, with the former extracted through convolutional layers and the later modeled via a Transformer-decoder. Additionally, we employ a weighted loss function to address fiber class imbalance encountered during training. We evaluate the proposed method on the simulated ISMRM 2015 Tractography Challenge dataset, achieving a valid streamline rate of 66.2%, white matter coverage of 63.8%, and successfully reconstructing 24 out of 25 bundles. Furthermore, on the multi-site Tractoinferno dataset, the proposed method demonstrates its ability to handle various diffusion MRI acquisition schemes, achieving a 5.7% increase in white matter coverage and a 4.1% decrease in overreach compared to RNN-based methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Image enhancement

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Taming Consistency Distillation for Accelerated Human Image Animation

arXiv

引用

arXiv 2025年

作者： Wang, Xiang Zhang, Shiwei Yuan, Hangjie Wei, Yujie Zhang, Yingya Gao, Changxin Wang, Yuehuan Sang, Nong Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China Fudan University China

Recent advancements in human image animation have been propelled by video diffusion models, yet their reliance on numerous iterative denoising steps results in high inference costs and slow speeds. An intuitive solution involves adopting consistency models, which serve as an effective acceleration paradigm through consistency distillation. However, simply employing this strategy in human image animation often leads to quality decline, including visual blurring, motion degradation, and facial distortion, particularly in dynamic regions. In this paper, we propose the DanceLCM approach complemented by several enhancements to improve visual quality and motion continuity at low-step regime: (1) segmented consistency distillation with an auxiliary light-weight head to incorporate supervision from real video latents, mitigating cumulative errors resulting from single full-trajectory generation;(2) a motion-focused loss to centre on motion regions, and explicit injection of facial fidelity features to improve face authenticity. Extensive qualitative and quantitative experiments demonstrate that DanceLCM achieves results comparable to state-of-the-art video diffusion models with a mere 2-4 inference steps, significantly reducing the inference burden without compromising video quality. The code and models will be made publicly available. Copyright © 2025, The Authors. All rights reserved.

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