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Polyhedral Objects Metamorphosis Using Convex Decomposition and Morphology

Virtual Reality 2003年第4期6卷 196-204页

作者： Wen-Yu Liu Hua Li Fei Wang Guang-Xi Zhu Department of Electronics & Information Engineering State Education Commission Laboratory for Image Processing and Intelligent Control Huazhong University of Science & Technology Wuhan Hubei Peoples Republic of China Department of Electronics and Information Engineering Huazhong University of Science and Technology Wuhan Hubei 430074 China

A new technique is presented for computing continuous shape transformations between polyhedral objects. The polyhedron shape transformations can be divided into polyhedron metamorphosis and bi-directional local rigid body rotation transformation. By decomposing two objects into sets of individual convex sub-objects respectively, and establishing the matching between two subsets, the approach can solve the metamorphosis problem of two non-homotopic objects (including concave objects and holey objects). Compared with other methods, this metamorphosis algorithm can be executed automatically for arbitrary polyhedrons and no need user interaction. The user has the ability to choose an automatic matching or to select interactively pairs of corresponding matching convex subsets to obtain special effects. Experiments show that this method can generate natural, high-fidelity, eye-pleasing metamorphosis results with simple computation.

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Simulating evolutional symport/antiport by evolution-communication and vice versa in tissue p systems with parallel communication 18th

Simulating evolutional symport/antiport by evolution-communi...

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18th International Conference on Membrane Computing, CMC 1018

作者： Adorna, Henry Alhazov, Artiom Pan, Linqiang Song, Bosheng Key Laboratory of Image Information Processing and Intelligent Control of Education Ministry of China School of Automation Huazhong University of Science and Technology Wuhan430074 China University of the Philippines Diliman Quezon City1101 Philippines Institute of Mathematics and Computer Science Academy of Science of Moldova Academiei 5 Chişinău2028 Moldova School of Electric and Information Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

We aim to compare functionality of symport/antiport with embedded rewriting to that of symport/antiport accompanied by rewriting, by two-way simulation, in case of tissue P systems with parallel communication. A simul... 详细信息

ISBN: (纸本)9783319733586

关键词： Tissue

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Cell resistance slope combined with LVQ neural network for prediction of anode effect

Cell resistance slope combined with LVQ neural network for p...

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International Conference on intelligent control and Information processing (ICICIP)

作者： Kaibo Zhou Zhikai Lin Dengzhi Yu Bin Cao Ziqian Wang Sihai Guo School of Automation Key Laboratory of Ministry of Education for Image Processing and Intelligent Control Wuhan China Guiyang Aluminum Magnesium Design and Research Institute Guiyang China School of Automation Wuhan University of Technology Wuhan China

It is an important part in aluminum electrolysis production to control the anode effect (AE). Since there are some shortcomings in traditional methods of anode effect prediction in aluminum electrolysis, this paper combined two methods, the slope of cell resistance and learning vector quantization (LVQ) neural network, to predict anode effect. First of all, the first prediction of anode effect will be conducted based on the slope of cell resistance. Afterwards, the inaccurate data are supposed to be re-predicted. The second prediction consists of two steps, one is to estimate the power spectrum from the signal of cell resistance by means of periodogram, the other is to re-predict the anode effect with the LVQ neural network, since the energy of frequency bands are served as the input feature variables of neural network, so as to raise the accuracy of prediction. It turned out that the success rate of ten-minute in advance prediction for anode effect can be above 85%, though just cell resistance signal is studied.

关键词： Resistance Decision support systems Neural networks Spectral analysis Data acquisition Vector quantization

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Learning from Untrimmed Videos: Self-Supervised Video Representation Learning with Hierarchical Consistency

arXiv

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arXiv 2022年

作者： Qing, Zhiwu Zhang, Shiwei Huang, Ziyuan Xu, Yi Wang, Xiang Tang, Mingqian Gao, Changxin Jin, Rong Sang, Nong Key Laboratory of Image Processing Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China ARC National University of Singapore Singapore Dalian Unversity of Technology China

Natural videos provide rich visual contents for self-supervised learning. Yet most existing approaches for learning spatio-temporal representations rely on manually trimmed videos, leading to limited diversity in visual patterns and limited performance gain. In this work, we aim to learn representations by leveraging more abundant information in untrimmed videos. To this end, we propose to learn a hierarchy of consistencies in videos, i.e., visual consistency and topical consistency, corresponding respectively to clip pairs that tend to be visually similar when separated by a short time span and share similar topics when separated by a long time span. Specifically, a hierarchical consistency learning framework HiCo is presented, where the visually consistent pairs are encouraged to have the same representation through contrastive learning, while the topically consistent pairs are coupled through a topical classifier that distinguishes whether they are topic-related. Further, we impose a gradual sampling algorithm for proposed hierarchical consistency learning, and demonstrate its theoretical superiority. Empirically, we show that not only HiCo can generate stronger representations on untrimmed videos, it also improves the representation quality when applied to trimmed videos. This is in contrast to standard contrastive learning that fails to learn appropriate representations from untrimmed videos. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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CLIP-guided Prototype Modulating for Few-shot Action Recognition

arXiv

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arXiv 2023年

作者： Wang, Xiang Zhang, Shiwei Cen, Jun Gao, Changxin Zhang, Yingya Zhao, Deli Sang, Nong Key Laboratory of Ministry of Education for Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology China Alibaba Group China The Hong Kong University of Science and Technology Hong Kong

Learning from large-scale contrastive language-image pre-training like CLIP has shown remarkable success in a wide range of downstream tasks recently, but it is still under-explored on the challenging few-shot action recognition (FSAR) task. In this work, we aim to transfer the powerful multimodal knowledge of CLIP to alleviate the inaccurate prototype estimation issue due to data scarcity, which is a critical problem in low-shot regimes. To this end, we present a CLIP-guided prototype modulating framework called CLIP-FSAR, which consists of two key components: a video-text contrastive objective and a prototype modulation. Specifically, the former bridges the task discrepancy between CLIP and the few-shot video task by contrasting videos and corresponding class text descriptions. The latter leverages the transferable textual concepts from CLIP to adaptively refine visual prototypes with a temporal Transformer. By this means, CLIP-FSAR can take full advantage of the rich semantic priors in CLIP to obtain reliable prototypes and achieve accurate few-shot classification. Extensive experiments on five commonly used benchmarks demonstrate the effectiveness of our proposed method, and CLIP-FSAR significantly outperforms existing state-of-the-art methods under various settings. The source code and Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Displacement prediction of landslide based on PSOGSA-ELM with mixed kernel

Displacement prediction of landslide based on PSOGSA-ELM wit...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Cheng Lian Zhigang Zeng Wei Yao Huiming Tang School of Automation Key Laboratory of Image Processing and Intelligent Control of Education Ministry China School of Computer Science South-Central University for Nationalities Wuhan China Faculty of Engineering China University of Geosciences Wuhan China

The prediction of landslide displacement is essential for carrying out to improve the disaster warning system and reduce casualties and property losses. This study applies a novel neural network technique, extreme learning machine (ELM) with kernel function, to landslide displacement prediction problem. However, the generalization performance of ELM with kernel function depends closely on the kernel types and the kernel parameters. In this paper, we use a convex combination of Gaussian kernel function and polynomial kernel function in ELM, which may use these two types of kernel functions' advantages. In order to avoid blindness and inaccuracy in parameter selection, a novel hybrid optimization algorithm based on the combination of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA) is used to optimize the regularization parameter C, the Gaussian kernel parameter γ, the polynomial kernel parameter q and the mixing weight coefficient η. The performance of our model is verified through two case studies in Baishuihe landslide and Yuhuangge landslide.

关键词： Kernel Terrain factors Optimization Hazards Reservoirs Training Support vector machines

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Grey Wolf Optimization Based Self-Organizing Fuzzy Multi-Objective Evolution Algorithm

Research Square

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Research Square 2021年

作者： Xie, Jialiang Zhang, Shanli Wang, Honghui Wu, Dongrui School of Science Jimei University Xiamen361021 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of the Ministry of Education for Image Processing and Intelligent Control China

Two goals of multi-objective evolutionary algorithms are effectively improving its convergence and diversity, and making the Pareto set evenly distributed and close to the real Pareto Front. This paper proposes a grey wolf optimization based self-organizing fuzzy multi-objective evolutionary algorithm. Grey wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto Sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on fifteen test problems were conducted to verify its effectiveness. Results show that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. © 2021, CC BY.

关键词： Self organizing maps

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Global coordinated tracking of multi-agent systems with disturbance uncertainties via bounded control inputs

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Nonlinear Dynamics 2015年第4期82卷 2059-2068页

作者： Su, Housheng Chen, Michael Z. Q. Wang, Xiaofan School of Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Wuhan China Department of Mechanical Engineering The University of Hong Kong Pokfulam Hong Kong Department of Automation Shanghai Jiao Tong University Shanghai China

This paper investigates the problem of global coordinated tracking of a multi-agent system with input additive uncertainties and disturbances via bounded control inputs. Scheduled low-and-high gain feedback-based distributed coordinated tracking protocols are developed. It is shown that, under the assumptions that each agent is asymptotically null controllable with bounded controls and the network is connected, global coordinated tracking of the multi-agent system can be achieved. We finally show some numerical simulations to verify and illustrate the theoretical results.

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Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair

arXiv

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arXiv 2023年

作者： Luo, Xianrui Peng, Juewen Zhao, Weiyue Xian, Ke Lu, Hao Cao, Zhiguo The Key Laboratory of Image Processing and Intelligent Control Ministry of Education The School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The S-Lab for Advanced Intelligence Nanyang Technological University Singapore

All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal stack compositing, which is unfriendly to ordinary people. To achieve point-and-shoot AIF photography with a smartphone, we expect that an AIF photo can be generated from one shot of the scene, instead of from multiple photos captured by the same camera. Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras. The goal is to recover sharp details from defocused regions in the main-camera photo with the help of the ultra-wide-camera one. The camera setting poses new challenges such as parallax-induced occlusions and inconsistent color between cameras. To overcome the challenges, we introduce a predict-and-refine network to mitigate occlusions and propose dynamic frequency-domain alignment for color correction. To enable effective training and evaluation, we also build an AIF dataset with 2686 unique scenes. Each scene includes two photos captured by the main camera, one photo captured by the ultra-wide camera, and a synthesized AIF photo. Results show that our solution, termed EasyAIF, can produce high-quality AIF photos and outperforms strong baselines quantitatively and qualitatively. For the first time, we demonstrate point-and-shoot AIF photo synthesis successfully from main and ultra-wide cameras. Copyright © 2023, The Authors. All rights reserved.

关键词： Cameras

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Quantitative Study on Despeckle Methods of Medical Ultrasound images

Quantitative Study on Despeckle Methods of Medical Ultrasoun...

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International Conference on intelligent Computation and Bio-Medical Instrumentation (ICBMI)

作者： Runxia Ma Xuming Zhang Mingyue Ding Qi Liu Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China Life College of Science and Technology Huazhong University of Science and Technology Wuhan China School of Electrical Information Engineering Sichuan University Chengdu China

Speckle is a granular noise that inherently exists in all types of coherent imaging systems. This paper presents a quantitative study on five despeckling methods such as frost filter, kuan filter, speckle reducing an isotropic diffusion, homomorphic filter and wavelet filter. We select six objective evaluation parameters, such as signal-to-ratio, contrast signal-to-noise ratio, figure of merit, least absolute error, edge protection factor, signal-to-minimum mean square error ratio, to quantify the performance of these filters. The comparative study will provide some parameter comparative reference for selecting a suitable filter in the ultrasound image processing.

关键词： Nonlinear filters Maximum likelihood detection Ultrasonic imaging Noise Speckle Gabor filters Digital filters

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