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检索条件"任意字段=6th Indian Conference on Computer Vision, Graphics and Image Processing"

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Overlap training to mitigate inconsistencies caused by image tiling in CNNs 40th

Overlap training to mitigate inconsistencies caused by image...

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40th SGAI International conference on Innovative Techniques and Applications of Artificial Intelligence, AI 2020

作者： An, Yu Ye, Qing Guo, Jiulin Dong, Ruihai Insight Centre for Data Analytics University College Dublin Dublin Ireland Key Laboratory of Tectonics and Petroleum Resources Ministry of Education China University of Geosciences Wuhan China C&C Reservoirs Brunel House Reading United Kingdom

ISBN: (纸本)9783030637989

this paper focuses on the problem of inconsistent predictions of modern convolutional neural networks (CNN) at patch (i.e. sub-image) boundaries. Limited by the graphics processing unit (GPU) resources, image tiling and stitching countermeasure have been applied for most megapixel images, that is, cutting images into overlapping tiles as CNN input, and then stitching CNN outputs together. However, we found that stitched (i.e. recovered) predictions have discontinuous grid-like noise. We propose a simple yet efficient overlap training framework to mitigate the inconsistent prediction at patch boundaries without changing the model architecture while improving the stability, robustness of the model. We have applied our solution to various CNNs (such as U-Net, DeepLab, RCF) and tested them on two real-world datasets. Extensive experiments suggest that the new framework is sufficient in reducing inconsistency and outperform these countermeasures. the source code and coloured figures are made publicly available online at: https://***/anyuzoey/***. © Springer Nature Switzerland AG 2020.

关键词： computer vision

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Adversarial examples generation system based on gradient shielding of restricted region 6th

Adversarial examples generation system based on gradient shi...

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6th International conference on Artificial Intelligence and Security,ICAIS 2020

作者： Hu, Weixiong Gu, Zhaoquan Zhang, Chuanjing Wang, Le Tang, Keke Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou510006 China

ISBN: (纸本)9789811581007

In recent years, deep neural networks have greatly facilitated machine learning tasks. However, emergence of adversarial examples revealed the vulnerability of neural networks. As a result, security of neural networks is drawing more research attention than before and a large number of attack methods have been proposed to generate adversarial examples to evaluate the robustness of neural networks. Furthermore, adversarial examples can be widely adopted in machine vision, natural language processing, and video recognition applications. In this paper, we study adversarial examples against image classification networks. Inspired by the method of detecting key regions in object detection tasks, we built a restrict region-based adversarial example generation system for image classification. We proposed a novel method called gradient mask to generate adversarial examples that have a high attack success rate and small disturbances. the experimental results also validated the method’s performance. © Springer Nature Singapore Pte Ltd 2020.

关键词： image classification

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AIM 2020 Challenge on image Extreme Inpainting 16th

AIM 2020 Challenge on Image Extreme Inpainting

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Workshops held at the 16th European conference on computer vision, ECCV 2020

作者： Ntavelis, Evangelos Romero, Andrés Bigdeli, Siavash Timofte, Radu Hui, Zheng Wang, Xiumei Gao, Xinbo Shin, Chajin Kim, Taeoh Son, Hanbin Lee, Sangyoun Li, Chao Li, Fu He, Dongliang Wen, Shilei Ding, Errui Bai, Mengmeng Li, Shuchen Zeng, Yu Lin, Zhe Yang, Jimei Zhang, Jianming Shechtman, Eli Lu, Huchuan Zeng, Weijian Ni, Haopeng Cai, Yiyang Li, Chenghua Xu, Dejia Wu, Haoning Han, Yu Nadim, Uddin S. M. Jang, Hae Woong Ahmed, Soikat Hasan Yoon, Jungmin Jung, Yong Ju Li, Chu-Tak Liu, Zhi-Song Wang, Li-Wen Siu, Wan-Chi Lun, Daniel P. K. Suin, Maitreya Purohit, Kuldeep Rajagopalan, A.N. Narang, Pratik Mandal, Murari Chauhan, Pranjal Singh Computer Vision Lab ETH Zürich Zürich Switzerland CSEM Neuchâtel Switzerland School of Electronic Engineering Xidian University Xi’an China Image and Video Pattern Recognition Laboratory School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of Baidu Inc. Beijing China Beijing China Dalian University of Technology Dalian China Adobe San Jose United States Rensselaer Polytechnic Institute Troy United States Peking University Beijing China Lab Gachon University Seongnam Korea Republic of Centre for Multimedia Signal Processing Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong China Indian Institute of Technology Madras Chennai India BITS Pilani Pilani India MNIT Jaipur Jaipur India

ISBN: (纸本)9783030670696

this paper reviews the AIM 2020 challenge on extreme image inpainting. this report focuses on proposed solutions and results for two different tracks on extreme image inpainting: classical image inpainting and semantically guided image inpainting. the goal of track 1 is to inpaint large part of the image with no supervision. Similarly, the goal of track 2 is to inpaint the image by having access to the entire semantic segmentation map of the input. the challenge had 88 and 74 participants, respectively. 11 and 6 teams competed in the final phase of the challenge, respectively. this report gauges current solutions and set a benchmark for future extreme image inpainting methods. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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Single image dehazing using image boundary constraint and nearest neighborhood optimization 18

Single image dehazing using image boundary constraint and ne...

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Proceedings of the 11th indian conference on computer vision, graphics and image processing

作者： Sidharth Gautam Tapan Kumar Gandhi B. K. panigrahi Indian Institute of Technology New Delhi India

ISBN: (纸本)9781450366151

Single image dehazing is an ill-posed problem that requires assumptions, priors and constraints to solve. In this paper, boundary constraint utilizing median filter has been proposed on the image radiance for the rough estimation of transmission-map in haze images. Furthermore, for the refinement of estimated transmission-map, the proposed method use a multi-dimensional feature space that uses nearest neighborhood optimization under the framework of non-local principle. Experimental results manifest that proposed method is effective and results in visually appealing dehaze images that can be useful for subsequent user or computer based application.

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Detection of ethnic Minority's symbols based on deep learning 6

Detection of ethnic Minority's symbols based on deep learnin...

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6th Annual International conference on Network and Information Systems for computers, ICNISC 2020

作者： Feng, Ye Shi, Zhuo Kong, Qian Li, Mingrui Yang, Ming Zhang, Mengxue Zeng, Shuzhen School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China School of Arts and Design Guilin University of Electronic Technology Guilin China Institute for Artificial Intelligence Interdisciplinary Guilin University of Electronic Technology Guilin China

Symbols are the embodiments of the ideology of ethnic minorities, the detection of ethnic minority's symbols is an important part of the protection and inheritance of ethnic cultural heritage. Nevertheless, the ethnic symbols are miscellaneous and referred to some special combination style, it is time-consuming and inaccurate to detect them only by traditional detection methods based on machine learning. therefore, in this paper, we propose a deep learning method based on Faster R-CNN to detect various kinds of Zhuang minority's symbols from the Z-S dataset. Firstly, the original images of Zhuang minority's symbols are prepocessed. Secondly, the processed images are sent to the ResNet-50 with FPN to extract feature maps. Moreover, RPN processes those feature maps to generate bounding boxes. Finally, ROI pooling layer in R-CNN converts those bounding boxes into fixed-length feature vectors, which fed into two sibling fully-connected layers for further detection tasks. through the ablation experiments based on a certain amount of ethnic symbol images, the results indicate that the proposed method has higher detection quality (mAP), and can effectively reduce the training cost. © 2020 Journal of Physics: conference Series.

关键词： Deep learning

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Automobile Cluster Pointer Defect Detection System using Adaptive Intensity Adjustment 6

Automobile Cluster Pointer Defect Detection System using Ada...

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6th IEEE International conference on Industrial Engineering and Applications (ICIEA)

作者： Trakulwaranont, Donnaphat Cooharojananone, Nagul Kruachottikul, Pravee Pitak, Panupong Gongsri, Nanthachot Aitphawin, Sermpong Chulalongkorn Univ Fac Sci Dept Math & Comp Sci Bangkok Thailand Solitech Innovat & Technol Co Ltd Bangkok Thailand Thai Nippon Seiki Co Ltd Bangkok Thailand

ISBN: (纸本)9781728108513

In the manufacturing, inspection process always facing the problem of time and consuming number of staff consuming. Because companies want to maintain high quality of their products. Introducing a technology such as autonomous machine vision and computer-based algorithms would solve a problem. Typically, this detection requires human to inspect the pointer, however it takes time and sometimes the errors are caused by human eyes judgement. So, we can apply image processing and robotics to reduce number of staff, human error, time and cost in this inspection. therefore, this paper presents an automatic system to detect, analyze and measure size of the defect on automobile cluster pointer. We introduced an adaptive intensity adjustment algorithm to solve a variation of intensity from taking image of various color of cluster pointer by a fix aperture camera. In the experimental result, our defection size measurement passed a bias test. then, our system was tested in the real production line over 66,160 pieces within two months period. the result shown that the accuracy was 99.4%.

关键词： industrial 4.0 automatic inspection system automobile pointer inspection image processing machine vision

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

4th Workshop on Document Analysis and Recognition, DAR 2018,...

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4th Workshop on Document Analysis and Recognition, DAR 2018, held in Conjunction with the 11th indian conference on vision, graphics, and image processing, ICVGIP 2018

ISBN: (纸本)9789811393600

the proceedings contain 14 papers. the special focus in this conference is on Document Analysis and Recognition. the topics include: Word-Wise Handwriting Based Gender Identification Using Multi-Gabor Response Fusion;a Secure and Light Weight User Authentication System Based on Online Signature Verification for Resource Constrained Mobile Networks;benchmark Datasets for Offline Handwritten Gurmukhi Script Recognition;benchmark Dataset: Offline Handwritten Gurmukhi City Names for Postal Automation;attributed Paths for Layout-Based Document Retrieval;textual Content Retrieval from Filled-in Form images;A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition;symbol Spotting in Offline Handwritten Mathematical Expressions;online Handwritten Bangla Character Recognition Using Frechet Distance and Distance Based Features;an Efficient Multi Lingual Optical Character Recognition System for indian Languages through Use of Bharati Script;telugu Word Segmentation Using Fringe Maps;an Efficient Character Segmentation Algorithm for Connected Handwritten Documents.

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Teaching the NAO Robot to Play a Human-Robot Interactive Game 6

Teaching the NAO Robot to Play a Human-Robot Interactive Gam...

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6th Annual conference on Computational Science and Computational Intelligence (CSCI)

作者： Li, Cen Imeokparia, Ebosehon Ketzner, Michael Tsahai, Tsega Middle Tennessee State Univ Dept Comp Sci Murfreesboro TN 37130 USA

ISBN: (纸本)9781728155845

this paper presents the background and methodologies used in programming and teaching the humanoid robot NAO to play the game of "Simon Says" with human players. Choreographe programming was used to provide the overall game logic and incorporate NAO's sensory capabilities. OpenPose pose detection and OpenCV APIs were used to develop the image processing components to convert the raw images captured by NAO for pose classification, and the Keras APIs were used to build the Convolutional Neural Network to classify and recognize the player poses.

关键词： robotics computer vision pose detection convolutional neural network image classification

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Machine learning content adaptive filters for image de-blurring 13

Machine learning content adaptive filters for image de-blurr...

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13th International conference on Interfaces and Human computer Interaction 2019, IHCI 2019, 12th International conference on Game and Entertainment Technologies 2019, GET 2019 and 13th International conference on computer graphics, Visualization, computer vision and image processing 2019, CGVCVIP 2019

作者： Mainali, Pradip OneSpan Belgium

ISBN: (纸本)9789898533913

this paper proposes a novel algorithm to recover blindly a sharp image from its degraded form by pixel pattern classification and filtering by using the deconvolution filters trained for the corresponding pixel pattern. Noise amplification is a well-known phenomenon that arises due to an ill-posed nature of the deconvolution, which is tackled better with the pattern classification, thereby state-of-the-art image de-blurring quality is demonstrated, yet at the very low complexity. For the computational efficiency, the pixel pattern classes are learnt in a multi-layer structure consisting of the coarser and the finer patterns, reducing the pattern search complexity in the dictionary by a factor of 6 without losing any quality. As compared to deep-learning based methods, the proposed approach provides a smaller memory footprint and lightweight implementation. For lightweight and real-time implementation, the paper also validates a simple class matching metric of l1-norm. the proposed method is suitable for embedded applications such as camera, TV systems, etc. © Copyright 2019 IADIS Press. All rights reserved.

关键词： Pixels

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Performance Comparison of Object Detection algorithms with different Feature Extractors 6

Performance Comparison of Object Detection algorithms with d...

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6th International conference on Signal processing and Integrated Networks (SPIN)

作者： Gupta, Archit Puri, Raghav Verma, Mrinal Gunjyal, Siddharth Kumar, Ashish BVCOE ECE Dept New Delhi India

ISBN: (纸本)9781728113807

In this work, speed vs accuracy of different Neural Network architectures using alternate feature extractors in the field of Object Detection is being computed, thereby finding the fastest and most accurate architecture out of the lot in order to carry out Object Detection. We made use of three architectures and three extractors to build different combinations of models in order to compute mAP, which is the metric used or commenting upon accuracy. COCO data-set has been used to extract sample images and the work is implemented on TensorFlow library.

关键词： computer vision Object Detection image processing TensorFlow Faster R-CNN App Development

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