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IEEE international conference on Robotics and Biomimetics

作者： Gan Liu Ying Tan Lingfeng Chen Wenchuan Kuang Binghua Li Feng Duan Chi Zhu College of Artificial Intelligence Nankai University Tianjin P. R. China School of Mathematics and Statistics Xi’an Jiaotong University Xi’an P. R. China Maebashi Institute of Technology Maebashi Gunma Japan

ISBN: (纸本)9781665405362

Unmanned Aerial Vehicle (UAV) machine vision target tracking has a wide range of applications. The most important thing in machine vision target tracking is the real-time performance of the object detection algorithm. At present, the amount of computation of the mainstream target detection algorithms used is too large. The processing speed of the target detection algorithm to the image collected by the computer is too slow, which makes the UAV lack real-time tracking. Therefore, it is very necessary to use a fast object detection algorithm in machine vision target tracking in UAVs. YOLOv3-Tiny is an object detection method based on deep learning. YOLOv3-Tiny optimizes the network structure based on YOLOv3 and reduces the output of one scale. In this work, we use human images to train YOLOv3-Tiny to make it can recognize humans as tracking targets. Then, we use YOLOv3-Tiny to analyze the images collected by the UAV for target tracking. Finally, the control signal of the tracking system is output to the flight controller of the UAV. For the tracking effect of the system designed in the research, we tested the tracking system in a complex indoor environment. It is found that the UAV indoor target tracking system based on YOLOv3-Tiny can effectively achieve target tracking.

关键词： Deep learning Target tracking image recognition Target recognition Machine vision Object detection Autonomous aerial vehicles

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Gesture Recognition and Control based on Visible Light Communication

Gesture Recognition and Control based on Visible Light Commu...

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第43届中国控制会议

作者： Xiaojun Liang Jiarong Li Chihan Xu Zixuan Xie Chaobo Zhang Wenbo Ding Weihua Gui Department of Strategic and Advanced Interdisciplinary Research Peng Cheng Laboratory Shenzhen International Graduate School Tsinghua University School of Automation Central South University

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

The evolution of human-computer interaction(HCI),particularly in the era of the Internet of Things(IoT),demands innovative solutions that transcend traditional interfaces like keyboards and *** study introduces a visible light communication(VLC) based sensing system for non-contact gesture recognition in *** system utilizes a cost-effective VLC setup optimized for indoor environments,ensuring precise signal transmission and *** data acquisition and processing board can accurately capture and analyze light-based hand gesture *** process is supported by a comprehensive signal processing framework that includes data preprocessing steps and robust machine learning *** algorithms are designed to detect subtle light pattern variations due to gestures,ensuring an accurate and responsive recognition *** system's effectiveness is confirmed by its high gesture recognition accuracy of 95.7%.The study's key contributions include the development of a VLC-based system for gesture recognition and control,the integration of diverse signal processing techniques suitable for VLC-based data,and the successful demonstration of the system's practical application through extensive experimental *** evaluations have validated the system's efficacy in real-world scenarios,marking an advancement in VLC-enabled *** research highlights VLC's potential as a user-friendly and efficient interface in HCI,offering more intuitive and responsive interactions in smart environments.

关键词： Visible Light Communication Gesture Recognition Human-Computer Interaction Machine Learning

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DeCloud GAN: An advanced Generative Adversarial Network for Removing Cloud Cover in Optical Remote Sensing imagery 2021

DeCloud GAN: An Advanced Generative Adversarial Network for ...

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2021 The 4th international conference on Computational Intelligence and Intelligent Systems

作者： Krisha Bhambani Mukta Takalikar Computer Engineering Pune Institute of Computer Technology India

ISBN: (纸本)9781450385930

Optical Remote Sensing imagery has several applications in monitoring the states of natural and man-made features around the globe. However, due to clouds and other climatic conditions, information extracted from the imagery retrieved is very limited. Deep learning has often been used in several image processing and remote sensing tasks. In this work, we propose the usage of generative adversarial networks to remove clouds and other climatic interference from high-resolution remote sensing imagery. We have trained and tested upon the Remote sensing image Cloud rEmoving dataset (RICE). The novel network(DeCloud GAN) we propose, makes use of residual UNets and pixel shuffle layers in the generator, which yield high quality cloudless satellite images. We have tested 4 methods for comparison, and have found that DeCloudGAN achieves the best performance on two main metrics, peak signal to noise ratio (PSNR) and structural similarity index (SSIM), to measure similarity in visual perception of the produced and target images.

关键词： image generation Optical imagery Remote Sensing Generative Adversarial Networks Deep Learning

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CroP: Color Constancy Benchmark Dataset Generator 2020

CroP: Color Constancy Benchmark Dataset Generator

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Proceedings of the 2020 4th international conference on Vision, image and signal processing

作者： Nikola Banić Karlo Koščević Marko Subašić Sven Lončarić Gideon Brothers Radnička Zagreb Croatia University of Zagreb Unska Zagreb Croatia

ISBN: (纸本)9781450389532

Implementing color constancy as a pre-processing step in contemporary digital cameras is of significant importance as it removes the influence of scene illumination on object colors. Several benchmark color constancy datasets have been created for the purpose of developing and testing new color constancy methods. However, they all have numerous drawbacks including a small number of images, erroneously extracted ground-truth illuminations, long histories of misuses, violations of their stated assumptions, etc. To overcome such and similar problems, in this paper a color constancy benchmark dataset generator is proposed. For a given camera sensor it enables generation of any number of realistic raw images taken in a subset of the real world, namely images of printed photographs. Datasets with such images share many positive features with other existing real-world datasets, while some of the negative features are completely eliminated. The generated images can be successfully used to train methods that afterward achieve high accuracy on real-world datasets. This opens the way for creating large enough datasets for advanced deep learning techniques. Experimental results are presented and discussed. The source code is available at http://***/ipg/resources/colorconstancy/.

关键词： image Dataset Illumination Estimation Data Augmentation Color Constancy White Balancing

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image Deep Steganography Detection based on Knowledge Distillation in Teacher-Student Network 2021

Image Deep Steganography Detection based on Knowledge Distil...

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2021 3rd international conference on Video, signal and image processing

作者： Huifang Hao Shangping Zhong Kaizhi Chen College of Mathematics and Computer Science Fuzhou University China

ISBN: (纸本)9781450385886

The current hot deep steganography can hide confidential information of an image into a carrier image of the same size. It also has the characteristics of a high steganography rate and is often used for private communication within specific organizations. However, the existing deep steganography detection network is complex and time-consuming. Thus, rapid steganography detection for massive image information is urgently needed. In this study, a typical distillation algorithm is applied to the existing classical deep steganography detection network (Ye-Net and Yedroudj-Net). We constructed a student network, taking Ye-Net and Yedroudj-Net as teacher networks respectively. Appropriate parameters T and α are also selected in accordance with the effect of knowledge distillation. With these parameters, a fast training model of deep steganography detection is established. The proposed deep steganography detection network is compared with Ye-Net and Yedroudj-Net through experiments. Three spatial steganography algorithms (Wow,S-uniward and Hill) and different steganography rates are used. The experimental results show that the proposed network can shorten the training and average detection time by about 70% and 24% when the detection accuracy is slightly lower. Overall, compared with Ye-Net and Yedroudj-Net, the proposed method can achieve fast detection of deep steganography. And code will be available at: https://***/hhfshiqi/KD-Ye-net

关键词： Model rapid detection Deep steganography detection Teacher-student network Knowledge distillation

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Feature Engineering and Stacked Echo State Networks for Musical Onset Detection

Feature Engineering and Stacked Echo State Networks for Musi...

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international conference on Pattern Recognition

作者： Peter Steiner Azarakhsh Jalalvand Simon Stone Peter Birkholz Institute for Acoustics and Speech Communication Technische Universität Dresden Dresden Germany Ghent University - imec IDLab Ghent Belgium

In music analysis, one of the most fundamental tasks is note onset detection - detecting the beginning of new note events. As the target function of onset detection is related to other tasks, such as beat tracking or tempo estimation, onset detection is the basis for such related tasks. Furthermore, it can help to improve Automatic Music Transcription (AMT). Typically, different approaches for onset detection follow a similar outline: An audio signal is transformed into an Onset Detection Function (ODF), which should have rather low values (i.e. close to zero) for most of the time but with pronounced peaks at onset times, which can then be extracted by applying peak picking algorithms on the ODF. In the recent years, several kinds of neural networks were used successfully to compute the ODF from feature vectors. Currently, Convolutional Neural Networks (CNNs) define the state of the art. In this paper, we build up on an alternative approach to obtain a ODF by Echo State Networks (ESNs), which have achieved comparable results to CNNs in several tasks, such as speech and image recognition. In contrast to the typical iterative training procedures of deep learning architectures, such as CNNs or networks consisting of Long-Short-Term Memory Cells (LSTMs), in ESNs only a very small part of the weights is easily trained in one shot using linear regression. By comparing the performance of several feature extraction methods, pre-processing steps and introducing a new way to stack ESNs, we expand our previous approach to achieve results that fall between a bidirectional LSTM network and a CNN with relative improvements of 1.8 % and -1.4 %, respectively. For the evaluation, we used exactly the same 8-fold cross validation setup as for the reference results.

关键词： Training Target tracking image recognition Neural networks Linear regression Music Speech recognition

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Why is the winner the best?

Why is the winner the best?

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IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Eisenmann, M. Reinke, A. Weru, V. Tizabi, M. D. Isensee, F. Adler, T. J. Ali, S. Andrearczyk, V. Aubreville, M. Baid, U. Bakas, S. Balu, N. Bano, S. Bernal, J. Bodenstedt, S. Casella, A. Cheplygina, V. Daum, M. de Bruijne, M. Depeursinge, A. Dorent, R. Egger, J. Ellis, D. G. Engelhardt, S. Ganz, M. Ghatwary, N. Girard, G. Godau, P. Gupta, A. Hansen, L. Harada, K. Heinrich, M. Heller, N. Hering, A. Huaulme, A. Jannin, P. Kavur, A. E. Kodym, O. Kozubek, M. Li, J. Li, H. Ma, J. Martin-Isla, C. Menze, B. Noble, A. Oreiller, V. Padoy, N. Pati, S. Payette, K. Raedsch, T. Rafael-Patino, J. Bawa, V. Singh Speidel, S. Sudre, C. H. van Wijnen, K. Wagner, M. Wei, D. Yamlahi, A. Yap, M. H. Yuan, C. Zenk, M. Zia, A. Zimmerer, D. Aydogan, D. Bhattarai, B. Bloch, L. Bruengel, R. Cho, J. Choi, C. Dou, Q. Ezhov, I. Friedrich, C. M. Fuller, C. Gaire, R. R. Galdran, A. Faura, A. Garcia Grammatikopoulou, M. Hong, S. Jahanifar, M. Jang, I. Kadkhodamohammadi, A. Kang, I. Kofler, F. Kondo, S. Kuijf, H. Li, M. Luu, M. Martincic, T. Morais, P. Naser, M. A. Oliveira, B. Owen, D. Pang, S. Park, J. Park, S. Plotka, S. Puybareau, E. Rajpoot, N. Ryu, K. Saeed, N. Shephard, A. Shi, P. Stepec, D. Subedi, R. Tochon, G. Torres, H. R. Urien, H. Vilaca, J. L. Wahid, K. A. Wang, H. Wang, J. Wang, L. Wang, X. Wiestler, B. Wodzinski, M. Xia, F. Xie, J. Xiong, Z. Yang, S. Yang, Y. Zhao, Z. Maier-Hein, K. Jaeger, P. F. Kopp-Schneider, A. Maier-Hein, L. German Canc Res Ctr Div Intelligent Med Syst Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany Heidelberg Univ Fac Math & Comp Sci Heidelberg Germany German Canc Res Ctr Div Biostat Heidelberg Germany German Canc Res Ctr Div Med Image Comp Heidelberg Germany Univ Leeds Fac Engn & Phys Sci Sch Comp Leeds W Yorkshire England HES SO Valais Wallis Univ Appl Sci & Arts Western Inst Informat Sch Management Sierre Switzerland Lausanne Univ Hosp Dept Nucl Med & Mol Imaging Lausanne Switzerland Tech Hsch Ingolstadt Ingolstadt Germany Univ Penn Ctr Artificial Intelligence & Data Sci Integrated Philadelphia PA USA Univ Penn Ctr Biomed Image Comp & Analyt Philadelphia PA USA Univ Penn Dept Pathol & Lab Med Perelman Sch Med Philadelphia PA USA Univ Penn Dept Radiol Perelman Sch Med Philadelphia PA USA Univ Washington Dept Radiol Seattle WA USA UCL Wellcome EPSRC Ctr Intervent & Surg Sci WEISS London England UCL Dept Comp Sci London England Univ Autonoma Barcelona Barcelona Spain Comp Vis Ctr Barcelona Spain Natl Ctr Tumor Dis NCT UCC Div Translat Surg Oncol Dresden Germany Ist Italiano Tecnol Dept Adv Robot Genoa Italy Politecn Milan Dept Elect Informat & Bioengn Milan Italy IT Univ Copenhagen Copenhagen Denmark Heidelberg Univ Hosp Dept Gen Visceral & Transplantat Surg Heidelberg Germany Erasmus MC Dept Radiol & Nucl Med Biomed Imaging Grp Rotterdam Rotterdam Netherlands Univ Copenhagen Dept Comp Sci Copenhagen Denmark Univ Appl Sci Western Switzerland HES SO Inst Informat Syst Sierre Switzerland Harvard Med Sch Brigham & Womens Hosp Boston MA USA Kings Coll London Sch Biomed Engn & Imaging Sci London England Univ Hosp Essen AoR Inst Artificial Intelligence Med IKIM Essen Germany Univ Nebraska Med Ctr Omaha NE USA Heidelberg Univ Hosp Dept Internal Med 3 Heidelberg Germany Rigshosp Copenhagen Univ Hosp Neurobiol Res Unit Copenhagen Denmark Arab Acad Sci & Technol Cairo Egypt CIBM Ctr Biomed Imaging L

ISBN: (纸本)9798350301298

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The "typical" lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work. [GRAPHICS] .

关键词： cell microscopy Medical and biological vision

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Digital image Recovery Based on Lifting Wavelet Transform

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Journal of Physics: conference Series 2021年第1期1962卷

作者： Taha Basheer Taha Dujan B. Taha Ruzelita Ngadiran Phaklen Ehkhan IT Department Faculty of Sciences TISHK International University Erbil Iraq. Software Engineering Department College of Computer Science and Mathematics Mosul University Iraq Centre of Excellence Advanced Computing (AdvComp) Faculty of Electronic Engineering Technology University Malaysia Perlis Perlis Malaysia

Recently, with the wide distribution of digital media, the need for authenticating digital images was increased. Therefore, many image tamper detection and recovery algorithms were introduced in literature to detect malicious modifications and retrieve the original images. The process of detection and recovery, however, used to have complex operation which requires long processing time. In this paper, a simplified image recovery algorithm is presented by using lifting wavelet transform. In the proposed method, the approximation band is hidden inside the bits of the original image and to be retrieved without relying on source image. For images with hidden data, the average PSNR and SSIM values were 31.22 and 0.977 respectively, and images were successfully retrieved after block attack.

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GAMM Annual Meeting – Kassel 2021 Overview of the Sections

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PAMM 2021年第1期21卷

Volume 21 (2021), Issue 1 of PAMM “Proceedings in Applied Mathematics and Mechanics” assembles the contributions to the 92nd Annual Meeting of the Gesellschaft für Angewandte Mathematik und Mechanik (GAMM), organized in 2021 by Universität Kassel, Germany, and held as a virtual conference. The contributions are grouped according to the minisymposia and sessions of the conference. Overview of the Sections GAMM related DFG Priority Programmes DFG-PP 1886 Polymorphic Uncertainty Modelling for the Numerical Design of Structures DFG-PP 1897 Calm, Smooth and Smart – Novel Approaches for Influencing Vibrations by Means of Deliberately Introduced Dissipation DFG-PP 2020 Cyclic Deterioration of High-Performance Concrete in an Experimental-Virtual Lab DFG-PP 2256 Variational Methods for Predicting Complex Phenomena in Engineering Structures and Materials Minisymposia MS 1 Computational Photonics MS 2 Symbolic computation methods for differential equations, dynamical systems, and control theory MS 4 advanced experimental and computational techniques in polymer mechanics Sections 1–24 S1 Multi-body dynamics S2 Biomechanics S3 Damage and fracture mechanics S4 Structural mechanics S5 Nonlinear oscillations S6 Material modelling in solid mechanics S7 Coupled problems S8 Multiscales and homogenization S9 Laminar flows and transition S10 Turbulence and reactive flows S11 Interfacial flows S12 Waves and acoustics S13 Flow control S14 Applied analysis S15 Uncertainty quantification S16 Optimization S17 Applied and numerical linear algebra S18 Numerical methods of differential equations S19 Optimization of differential equations S20 Dynamics and control S21 Mathematical signal and image processing S22 Scientific computing S24 History of mechanics Young Researchers' Minisymposia YRM1 algorithms for coupled multi-physics problems YRM3 Recent developments in isogeometric analysis for flow problemes YRM6 Novel discretization methods for phase-field modeling in fracture mechanics

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Why is the Winner the Best?

Why is the Winner the Best?

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conference on Computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

international benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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