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International Conference on Bio-engineering for Smart Technologies (BioSMART)

作者： Rihab Bkekri Anouar Benamor Hassani Messaoud Samer Mohammed Yacine Amirat Laboratory of Automatic Signal and Image Processing University of Tunis Hussein Montfleury Tunis Laboratory of Images Signals and Intelligent Systems (LISSI) University of Paris-Est Créteil Vitry-Sur-Seine France

The paper concerns the development of a adaptive sliding mode control based on super twisting (ASTW) and can be applied to a Human-Driven Knee Joint Orthosis. Therefore, a model of the shank-orthosis system is given considering the human effort as an external torque acting on the system. The main objective of this paper is to bring together two approaches to reduce the phenomenon of chattering, adaptation of gain and high-order sliding mode control. The obtained controller, based on the well known second order sliding mode algorithm, does not require any information on the limits of the perturbation and its gradient except for their existence. Simulation results provided demonstrate the effectiveness of the proposed approach.

关键词： Sliding mode control Trajectory Stability analysis Adaptation models Torque Uncertainty Mathematical model

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Coarse-to-Fine Learning for Single-image Super-Resolution

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IEEE Transactions on Neural Networks and Learning systems 2017年第5期28卷 1109-1122页

作者： Zhang, Kaibing Tao, Dacheng Gao, Xinbo Li, Xuelong Li, Jie School of Computer and Information Science Hubei Engineering University Xiaogan Hubei432000 China Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia State Key Laboratory of Integrated Services Networks School of Electronic Engineering Xidian University Xi'an Shaanxi710071 China State Key Laboratory of Transient Optics and Photonics Xi'An Institute of Optics and Precision Mechanics Chinese Academy of Sciences Xi'an Shaanxi710119 China Video and Image Processing System Laboratory School of Electronic Engineering Xidian University Xi'an Shaanxi710071 China

This paper develops a coarse-to-fine framework for single-image super-resolution (SR) reconstruction. The coarse-to-fine approach achieves high-quality SR recovery based on the complementary properties of both example learning-and reconstruction-based algorithms: example learning-based SR approaches are useful for generating plausible details from external exemplars but poor at suppressing aliasing artifacts, while reconstruction-based SR methods are propitious for preserving sharp edges yet fail to generate fine details. In the coarse stage of the method, we use a set of simple yet effective mapping functions, learned via correlative neighbor regression of grouped low-resolution (LR) to high-resolution (HR) dictionary atoms, to synthesize an initial SR estimate with particularly low computational cost. In the fine stage, we devise an effective regularization term that seamlessly integrates the properties of local structural regularity, nonlocal self-similarity, and collaborative representation over relevant atoms in a learned HR dictionary, to further improve the visual quality of the initial SR estimation obtained in the coarse stage. The experimental results indicate that our method outperforms other state-of-the-art methods for producing high-quality images despite that both the initial SR estimation and the followed enhancement are cheap to implement. © 2012 IEEE.

关键词： Optical resolving power

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An ACO-based online routing method for multiple order pickers with congestion consideration in warehouse

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Journal of intelligent Manufacturing 2016年第2期27卷 389-408页

作者： Chen, Fangyu Wang, Hongwei Xie, Yong Qi, Chao Key Laboratory of Image Processing and Intelligent Control of Education Ministry Institute of Systems Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China

One of the challenging problems in order picking is how to deal with the congestion happens in warehouse with multiple pickers. In this paper, we consider an ant colony optimization (ACO)-based online routing method to find picking routes for multiple order pickers under nondeterministic picking time. Here, a default route is formed by ACO for each single picker. Then, we coordinate these routes to alleviate congestion by dedicated rules based on indoor positioning and information sharing technologies, during order pickers serve the picking task. Our results indicate that the proposed method can achieve a reduction in the order service time primarily by coping with the congestion. We conclude that the new method is particularly effective in multiple-block picker-to-parts warehouses. © 2014, Springer Science+Business Media New York.

关键词： Ant colony optimization Congestion Multiple order pickers Routing method Warehouse management

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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Optimization Techniques in Computer Vision 1

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丛书名： Advances in Computer Vision and Pattern Recognition

2016年

作者： Mongi A. Abidi Andrei V. Gribok Joonki Paik

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Robust feature detection using particle keypoints and its application to video stabilization in a consumer handheld camera

Robust feature detection using particle keypoints and its ap...

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IEEE International Conference on Consumer Electronics, ICCE 2016

作者： Jeon, Semi Yoon, Inhye Yang, Seung'I Kim, Bongmo Kim, Jisung Paik, Joonki Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science Multimedia and Film Chung-Ang University Seoul Korea Republic of Video Technology Lab. Future Technology RandD Division SK Telecom Korea Republic of

ISBN: (纸本)9781467383646

Compact, portable digital cameras have been popular to consumers. This paper presents a robust feature detection method using particle keypoints and its application to video stabilization. The proposed video stabilization algorithm consists of three steps: i) generation of a flat region map based on the Gaussian filter, ii) robust feature point detection using particle keypoints, and iii) camera paths estimation for enhancing a shaky video. As a result, the proposed algorithm can estimate optimal homography by redefining important feature points using particle keypoints in the flat region map. The proposed robust feature detection algorithm is suitable for enhancing the quality of video acquired by consumer handheld imaging devices. © 2016 IEEE.

关键词： Stabilization

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Hand detection using facial information

Hand detection using facial information

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IEEE International Conference on Consumer Electronics - Berlin (ICCE-Berlin)

作者： Sangpil Han Seokmok Park Min-jae Kim Joonki Paik Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul Korea

This paper presents a robust hand detection algorithm using the facial information. The proposed algorithm consists of four steps: (i) detection of a face, (ii) generation of regions of interest (ROI) to detect hands, (iii) skin color extraction from the face region, and (iv) detection of hands using the face skin color in the ROI. The proposed algorithm can reduce false detection caused by a similar skin color, and provides a successful detection rate up to 92 percent.

关键词： Face Skin Color Detection algorithms image color analysis Robustness Face detection

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Robust feature detection using particle keypoints and its application to video stabilization in a consumer handheld camera

Robust feature detection using particle keypoints and its ap...

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IEEE International Conference on Consumer Electronics (ICCE)

作者： Semi Jeon Inhye Yoon Seung'i Yang Bongmo Kim Jisung Kim Joonki Paik Image Processing and Intelligent Systems Laboratory Multimedia and Film Chung-Ang University Seoul Korea Future Technology R&D Division Video Technology Lab.

关键词： Conferences Consumer electronics

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New corner detector using non-cornerness measure

New corner detector using non-cornerness measure

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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Woon Cho Seokmok Park Jacob D'avy Joonki Paik The University of Tennessee Knoxville TN USA Image Processing and Intelligent Systems Laboratory Graduate School of Advanced Imaging Science and Film Chung-Ang University Seoul Korea University of Tennessee Knoxville Knoxville TN US

In this paper, we present a novel corner detection method based on a new non-cornerness measure. The non-cornerness function is comprised of two steps: 1) eliminate any pixel located in a flat region and 2) remove a pixel that is positioned along an edge in any orientation. The experimental results show that our proposed method outperforms previous corner detectors.

关键词： Detectors image edge detection Indexes Boundary conditions Computer vision Real-time systems Information and communication technology

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Imaging inverse problem using sparse representation with adaptive dictionary learning 5

Imaging inverse problem using sparse representation with ada...

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2015 5th IEEE International Advance Computing Conference, IACC 2015

作者： Mittu George, P. Vivek, M. Paik, Joonki Oxford College of Engineering Bangalore India Image Processing and Intelligent Systems Laboratory Chung-Ang University Seoul Korea Republic of

ISBN: (纸本)9781479980475

Sparse representation is one of the powerful emerging statistical techniques for modeling images. This representation approximates the image as a combination of the codewords within a dictionary which is over complete. Recent years have seen a tremendous growth in the field of sparse representation. Here a method is proposed for image inversing by using iterative deblurring based on sparse representation of an image which is uniformly blurred. The key idea behind this methodology is the sparseness of natural images in some domain. The quality of recovered image majorly depends on the domain or the dictionaries that are used to represent it. In this paper, the K-SVD algorithm is used to train thegroup of codewords from a set of quality natural image patches. For each of the local patch within the blurred image, the best suited sub-dictionary from the trained dictionary data base is selected. In addition, a smoothness regularization constraint is added that prevents the reblurring of the image edges. For numerical stability the sparsity weight is adaptively computed which also improves the reconstructed image quality. Comparative study on some of the existing restoration algorithm proves the proposed method outperforms them all. © 2015 IEEE.

关键词： image reconstruction

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