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检索条件"任意字段=3rd International Conference on Computer Vision and Image Processing, CVIP 2018"

共 1646 条记录，以下是261-270 订阅

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Dynamic Hand Gesture Recognition Using computer vision and Neural Networks 3

Dynamic Hand Gesture Recognition Using Computer Vision and N...

引用

3rd international conference for Convergence in Technology (I2CT)

作者： Munasinghe, M. I. N. P. Univ Moratuwa Dept Comp Sci & Engn Moratuwa Sri Lanka

ISBN: (纸本)9781538642733

Using hand gestures is one of the most natural ways of interacting with the computer and most importantly correct interpretation of moving hand gestures in real-time has many applications. In this paper, the author has designed and developed a system which can recognize gestures in front of a web camera real time using motion history images (NMI) and feedforward neural networks. Firstly, background from captured frames is removed using Gaussian mixture based background/foreground segmentation algorithm in order to capture moving areas in the frame and thereafter median filtering has applied to remove random noise from the frame. Then binary thresholding with Otsu's binarization has applied and it will identify optimal threshold value and these processed frames are merged and cumulative motion history image is generated using a developed algorithm based on the structural similarity measure. Structural similarity between the cumulated image and the initial frame also calculated and used in this algorithm. Finally feed forward neural network with stochastic gradient-based optimizer has used to classify the gestures.

关键词： computer vision hand gesture recognition neural networks human-computer interaction motion history images

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A Method for Block Compression of images Based on Data-hiding 3

A Method for Block Compression of Images Based on Data-hidin...

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3rd international conference on computer Graphics and Digital image processing (CGDIP)

作者： Cui, T. Zhou, Q. Shen, J. Li, J. Hu, Y. L. Liang, W. CAST Natl Key Lab Sci & Technol Space Microwave Xian 710100 Shaanxi Peoples R China China Acad Space Technol Xian Xian 710100 Peoples R China

The existing image block compression method does not reduce the amount of data after the image block processing. For the image with large amount of raw data, such as high-resolution remote sensing images, the image compression will consume more hardware resources and cause great pressure on the compression performance. Aiming at this situation, a method for block compressed of images based on data-hiding is proposed. The reference image block and similar image blocks are judged by the similarity of image blocks. The number of similar image blocks is hidden in the reference image block by data-hiding, and only the reference image blocks are subjected to JPEG2000 compression. The experimental results show that the method reduces the amount of data before image compression by 1/3 and increase the compression ratio of image compression by 1.5 times.

关键词： image compression

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Research on online detection transmission line technology for automatic classification of business image recognition model of State Grid : Research on transmission line image recognition model detection algorithm 3

Research on online detection transmission line technology fo...

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3rd international Academic Exchange conference on Science and Technology Innovation, IAECST 2021

作者： Chen, Xiang Zhou Liu, Wen Li Liu, Wen Si Chen, Shi Cong Big Data Center of State Grid Corporation of China Bei Jing China

ISBN: (纸本)9781665402675

Transmission line deep learning image recognition is one of the most important breakthroughs in the field of artificial intelligence in recent ten years. It has achieved great success in many fields, such as sample recognition, grid processing, computer vision recognition, video analysis and multimedia. This paper will focus on the latest research progress of transmission line deep learning image recognition in transmission line recognition, line damage detection and video analysis, and discuss its development trend. © 2021 IEEE.

关键词： Electric lines

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A Fast Detection Scheme for Original Fabric Based on Blob, Canny and Rotating Integral Algorithm 3

A Fast Detection Scheme for Original Fabric Based on Blob, C...

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3rd IEEE international conference on image, vision and Computing (ICIVC)

作者： Peng, Daizhong Zhong, Guoyun Rao, Zhihua Shen, Tao Chang, YanRong Wang, Mengmeng East China Univ Technol Jiangxi Engn Lab Radiol Geosci Big Date Technol Nanchang Jiangxi Peoples R China Jiangxi Engn Lab Radiol Geosci Big Date Technol Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538649916

Fabric inspection is an important part of testing the quality of textiles. Influenced by labor and cloth inspection machines, traditional fabric inspection results in low detection efficiency and high error inspection. In this paper, we propose a fast and efficient defect detection scheme by installing a camera to grab a cloth image in a weaving circle machine. In the progress of fabric weaving, there are three types of flaws: spots, holes and lines on the fabric. In our scheme, The Blob feature point detection algorithm is used to detect the spots, the Canny operator contour detection method is used to detect the holes, and the proposed rotation screenshot gray integral projection method is used to detect the lines. The experiment results show that our fabric inspection scheme presents higher detection efficiency than existing ones.

关键词： weaving circle machine fabric inspection defect detection image processing

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Simulation of self-driving car using deep learning 3

Simulation of self-driving car using deep learning

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3rd international conference on Intelligent Sustainable Systems, ICISS 2020

作者： Bhalla, Aman Nikhila, Munipalle Sai Singh, Pradeep National Institute of Technology Computer Science and Engineering Raipur India

ISBN: (纸本)9781728170893

The rapid development of Artificial Intelligence has revolutionized the area of autonomous vehicles by incorporating complex models and algorithms. Self-driving cars are always one of the biggest inventions in computer science and robotic intelligence. Highly robust algorithms that facilitate the functioning of these vehicles will reduce many problems associated with driving such as the drunken driver problem. In this paper our aim is to build a Deep Learning model that can drive the car autonomously which can adapt well to the real-time tracks and does not require any manual feature extraction. This research work proposes a computer vision model that learns from video data. It involves image processing, image augmentation, behavioural cloning and convolutional neural network model. The neural network architecture is used to detect path in a video segment, linings of roads, locations of obstacles, and behavioural cloning is used for the model to learn from human actions in the video. © 2020 IEEE.

关键词： computer vision

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Integrating procedural textures with replicated image editing 05

Integrating procedural textures with replicated image editin...

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GRAPHITE 2005 - 3rd international conference on computer Graphics and Interactive Techniques in Australasia and Southeast Asia

作者： Brooks, Stephen Dodgson, Neu A. Faculty of Computer Science Dalhousie University Halifax Canada Computer Laboratory University of Cambridge Cambridge United Kingdom

ISBN: (纸本)1595932267

image editing software is often characterized by a seemingly endless array of toolbars, filters, transformations and layers. But recently, a counter trend has emerged in the field of image editing which aims to reduce the user's workload through semiautomation. This alternate style of interaction has been made possible through advances in directed texture synthesis and computer vision. And it is in this context that we have developed our texture editing system that allows complex operations to be performed on images with minimal user interaction. This is achieved by utilizing the inherent self-similarity of image textures to replicate intended manipulations globally. In this paper, we expand the capabilities of replicated image editing by integrating procedural texture generation. Copyright © 2005 by the Association for Computing Machinery, Inc.

关键词： image processing

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Methods for Long-Distance Crack Location and Detection of Concrete Bridge Structures 3

Methods for Long-Distance Crack Location and Detection of Co...

引用

3rd IEEE international conference on image, vision and Computing (ICIVC)

作者： Cai, Youfa Fu, Xing Shang, Yanna Shi, Jingxin Tianjin Univ State Key Lab Precis Measurement Technol & Instru Tianjin 300072 Peoples R China Beijing Instrument Ind Res Inst Beijing 100176 Peoples R China

ISBN: (纸本)9781538649916

In order to improve the efficiency of crack detection of concrete bridge structures, a new method based on computer vision technology and coordinate mapping is proposed. In this research, this crack measurement system is integrated mainly with a high magnification image acquisition system, a two-dimensional electric cradle head device and a laser ranging system. It has a set of observing coordinate system. Firstly, the marking points' image coordinates are mapped to the observation coordinates. Secondly, according to the marking points' observation coordinates, the measured crack's coordinates are mapped to a same world coordinates so as to realize the spatial location of the measured cracks regardless of different test cycles or instrument's setup positions, which is a great convenience for the review detection of surface cracks of concrete bridge structures. The experiments show that this method is efficient and convenient. It can automatically locate the measured cracks within 16 s, and the deviation is not more than +/- 0.07 degrees. At a distance of 100 m, the measurement accuracy of crack width is better than +/- 0.12 mm.

关键词： crack location crack detection long-distance computer vision spatial coordinate mapping concrete bridge structures

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Self-supervised image Classification based on the Distances of Deep Feature Space 3

Self-supervised Image Classification based on the Distances ...

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3rd international conference on Video and image processing (Icvip)

作者： He, Zhuoxun Zhang, Ya Wang, Yanfeng Shanghai Jiao Tong Univ 800 Dongchuan Rd Shanghai 200240 Peoples R China

ISBN: (纸本)9781450376822

Deep neural networks have demonstrated their effectiveness in computer vision, especially for image classification and detection. Mixup is recently proposed as a data augmentation scheme, which applies linear combination of two random training examples and corresponding targets. However, the linear assumption is inappropriate for training a non-linear model. In this paper, we propose a self-supervised method which requires the consistency of original and mixed images on feature space. Our work is motived by the semantic information is related to the relative position of features. To implement this idea effectively and efficiently, we perform two-stage of training procedure, i.e., running estimation of class centers of original data in feature space, and training deep neural networks with modified loss term of Mixup. Besides, the proposed approach is also compatible with other variants of Mixup. We validate our approach on two popular image classification datasets, CIFAR10 and CIFAR100 by a variety of advanced deep neural networks, and demonstrate consistent generalization improvements, sometimes significantly. We also conduct analytical experiments to evaluate the robustness of our method to hyperparameters.

关键词： Self-supervision image classification Mixup Class center

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SHAPE AND MOTION WITHOUT DEPTH

引用

3rd international CONF ON computer vision

作者： TOMASI, C KANADE, T Sch of Comput Sci Carnegie Mellon Univ Pittsburgh PA USA

ISBN: (纸本)0818620579

Inferring the depth and shape of remote objects and the camera motion from a sequence of images is possible in principle, but is an ill-conditioned problem when the objects are distant with respect to their size. This problem is overcome by inferring shape and motion without computing depth as an intermediate step. On a single epipolar plane, an image sequence can be represented by the F × P matrix of the image coordinates of P points tracked through F frames. It is shown that under orthographic projection this matrix is of rank three. Using this result, the authors develop a shape-and-motion algorithm based on singular value decomposition. The algorithm gives accurate results, without relying on any smoothness assumption for either shape or motion.

关键词： image processing

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Dermatobot: An image processing Enabled Chatbot for Diagnosis and Tele-remedy of Skin Diseases 3

Dermatobot: An Image Processing Enabled Chatbot for Diagnosi...

引用

3rd international conference for Emerging Technology, INCET 2022

作者： Kohli, Shivansh Verma, Utkarsh Kirpalani, Vinay V Srinath, Ramamoorthy Pes University Computer Science and Engineering Karnataka Bangalore India

ISBN: (纸本)9781665494991

Diseases of dermatological nature have become quite common given the deteriorating environmental conditions, making visits to a dermatologist more frequent. However, a visit to a dermatologist may not always be very feasible as a result of which skin diseases may go untreated and this might become a significant problem in time. Dermatobot is a portable dermatologist for common skin diseases which aims at reducing skin ailments going untreated by providing a combined computer vision and NLP enabled diagnosis to skin diseases and suggesting out remedies for the diagnosed disease. Universal Sentence Encoders were used for similarity matching and an image classification model trained upon EfficientNetB4. The image classification model obtained a top-5 validation accuracy of 92%.Disclaimer: Dermatobot is not a replacement for dermatologists and only recommends mild remedies that the user can adhere to at his/her own avail. © 2022 IEEE.

关键词： Telemedicine

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