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

共 1642 条记录，以下是111-120 订阅

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vision Mesh: A Novel Video Sensor Networks Platform for Water Conservancy Engineering

Vision Mesh: A Novel Video Sensor Networks Platform for Wate...

引用

3rd IEEE international conference on computer Science and Information Technology (ICCSIT)

作者： Zhang, Meiyan Cai, Wenyu Zhejiang Water Conservancy & Hydropower Coll Dept Elect Engn Hangzhou 310018 Zhejiang Peoples R China Hangzhou Dianzi Univ Sch Elect & Informat Hangzhou 310018 Zhejiang Peoples R China

ISBN: (纸本)9781424455379

Video is an important medium for the observation of a variety of phenomena in the physical world such as for water conservancy engineering. Video sensor networks (VSN) consists of a large number of sensor nodes with cheap CMOS cameras, therefore, it has the ability of large scale visual monitoring. However, a kind of powerful and scalable platform is urgent for simulation and development of VSN. This paper proposed a novel and scalable video sensor networks platform named as vision Mesh for water conservancy engineering. vision Mesh is composed of a mass of image or video sensor nodes - vision Motes, with which multi-view image or video information of FOV (Field of View) can be acquired simply. vision Mote is built with Atmel ARM9 CPU and operated on Linux operating system, with TI CC2430 Zigbee module taken as wireless transceiver. Furthermore, OpenCV machine vision lib is migrated to vision Mesh platform so as to improve video processing ability, therefore, vision Mesh has the ability of image and video processing and is of strong scalability to extend performance. In this paper, we provide an overview of vision Mesh architecture and provide an insightful platform for VSN.

关键词： Video sensor networks CMOS camera vision Mesh vision Mote Water Conservancy Engineering

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Unfeatured Weld Positioning Technology Based on Neural Network and Machine vision 3

Unfeatured Weld Positioning Technology Based on Neural Netwo...

引用

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

作者： Cai, Chengtao Wang, Boyu Liu, Yue Yan, Yongjie Harbin Engn Univ Coll Automat Harbin Heilongjiang Peoples R China State Key Lab Air Traff Management Syst & Technol Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538649916

In machine vision, image processing technology is the basis of target recognition and positioning. When the background of the image is complex, especially when the background feature is similar to the target feature, the accuracy of the target recognition by traditional image processing methods cannot be guaranteed. In this paper, based on the background of automatic welding technology, proposing a new method of combining the neural networks and machine vision. Specifically, the image is preprocessed by using an improved convolutional auto-encoder to enhance the target features and remove the characteristics of the main interferers. Then, use the improved traditional image processing technology to extract the target and complete the processing of the featureless image. Finally, use a binocular camera to achieve accurate positioning of the target. This paper provides a new idea for the identification and positioning of the target.

关键词： neural networks machine vision weld positioning featureless image convolutional auto-encoder

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Tiny Electronic Component Detection Based on Deep Learning 3

Tiny Electronic Component Detection Based on Deep Learning

引用

3rd IEEE international conference on Cloud Computing and Internet of Things, CCIOT 2018

作者： Liu, Chun Liu, Shaoqian HuBei University of Technology School of Computer Wuhan China

ISBN: (纸本)9781538671412

Tiny electronic component detection mostly suffers from compact size and multiple interferences of environment. To solve this problem, this paper presents an object detection method based on convolutional neural networks (CNNs). This method combines residual blocks in residual networks with CNNs. The CNNs can reduce the dimensions of the image, and the residual block in the residual network can train deeper neural networks, and then use the successful application of CNNs in computer vision to improve the recognition rate of components and improve the efficiency of automated processing of tiny electronic components. After the hyper-parameter adjustment, this method achieved 95.63% accuracy on our test set. © 2018 IEEE.

关键词： computer vision

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3D Reconstructions with KinFu Using Different RGBD Sensors 3

3D Reconstructions with KinFu Using Different RGBD Sensors

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3rd IEEE international conference on image processing, Applications and Systems, IPAS 2018

作者： Patiño Mejia, Isabel C. Zell, Andreas University of Tuebingen Tuebingen Germany

ISBN: (纸本)9781728102474

RGBD sensors are widely used in robotics and computer vision. Since the Microsoft Kinect was launched, various other sensors, such as the Asus Xtion PRO LIVE and the Intel R200, have been developed. Among other applications, these sensors are useful for 3D reconstruction, which can be achieved with KinFu (an open-source implementation of KinectFusion). Reconstructions of objects and people are useful in multiple fields such as computer graphics and ergonomics. In this paper, we analyze if the 3D reconstructions generated by KinFu depend on the sensor used to record its input. The analysis comprehends qualitative and quantitative evaluations of reconstructions created from point clouds recorded with six well-known sensors. The results suggest that the reconstruction is sensor dependent and the selection of a determined sensor is conditioned to the requirements of each application. © 2018 IEEE.

关键词： image reconstruction

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Research on Key Technologies of target location based on Intelligent Robot 3

Research on Key Technologies of target location based on Int...

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3rd international conference on computer vision, image and Deep Learning and international conference on computer Engineering and Applications, CVIDL and ICCEA 2022

作者： Li, Zhong Guo, Caihui Guangzhou City Construction College Guangdong China Citi University Ulaanbaatar Mongolia

ISBN: (纸本)9781665459112

In recent years, the country has proposed the strategic development goal of 'Made in China 2025', and the intelligent manufacturing industry has gradually received national attention. Intelligent robots rely on the advantages of high efficiency and strong adaptability to the working environment to promote the development of intelligent manufacturing industry. Machine vision technology makes robots more intelligent, and the robot system integrating vision has also developed rapidly. Compared with monocular vision, binocular vision can obtain target stereo information, and it has become a key research topic in related fields. Under this background, this paper designs an intelligent robot target recognition and positioning system based on binocular vision according to the actual industrial demand. This paper mainly studies the recognition and localization algorithm of circular object workpiece by intelligent robot based on binocular vision system, and designs and realizes the whole experimental system according to the research content. Based on the system experiment, this paper introduces the calibration of the binocular camera system and the robot vision system, the target image pretreatment algorithm, the recognition and positioning algorithm of the target workpiece and so on. Finally, the visual interface is designed to control the robot to complete the simulated grasping action and realize the whole system. The object image is acquired by binocular camera, the object identification and location are completed according to the image processing algorithm, and the robot is controlled to complete the simulation grasping experiment, which has certain reference significance in practical application. © 2022 IEEE.

关键词： Binocular vision

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3D objects descriptors methods: Overview and trends 3

3D objects descriptors methods: Overview and trends

引用

3rd international conference on Advanced Technologies for Signal and image processing (ATSIP)

作者： H'roura, Jihad Bekkari, Aissam Mammass, Driss Bouzit, Ali Mansouri, Alamin Roy, Michael Le Goic, Gaetan Ibn Zohr Univ IRF SIC Lab Agadir Morocco Univ Bourgogne Franche Comte LE2I FRE CNRS ENSAM UBFC Lab 2005 Auxerre France

ISBN: (纸本)9781538605516

Object recognition or object's category recognition under varying conditions is one of the most astonishing capabilities of human visual system. The scientists in computer vision have been trying for decades to reproduce this ability by implementing algorithms and providing computers with appropriate tools. Hence, several intelligent systems have been proposed To act in this field, numerous approaches have been proposed In this paper we present an overview of the current trend in 3D objects recognition and describe some representative state of the art methods, highlighting their limits and complexity.

关键词： 3D objects Recognition computer vision skeleton Keypoints

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computer vision System for Cashew Kernel Area Estimation

Computer Vision System for Cashew Kernel Area Estimation

引用

3rd international conference on Computing Communication and Networking Technologies (ICCCNT)

作者： Narendra, V. G. Shetty, Dasharathraj K. Hareesh, K. S. Manipal Inst Technol Dept Comp Sci & Engn Manipal 576104 Karnataka India

ISBN: (纸本)9781467356930

Carrying out Food and Agricultural product Quality Inspection manually is a laborious task. Maintaining consistent product quality is not possible due to human errors. Customer expectations are increasing day by day and seek products of high quality. However, in India the Inspection of Food and Agricultural produce is done manually in most of the cases. Work in the computer vision area is being extensively carried out these days to provide automated, non-destructive and cost-effective techniques to achieve this. computer vision uses image processing as one of the intermediary tools to achieve the ultimate objective. Cashew is one of the important commercial crops of India. The objective of this research is to provide an image processing method for measuring cashew kernel Size (i.e. length and width/height) with an accurate and smallest relative error.

关键词： computer vision image processing image Analysis Quality Inspection Food and Agricultural Products

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A Novel Design of an Intangible Hand Gesture Controlled computer Mouse using vision Based image processing 3

A Novel Design of an Intangible Hand Gesture Controlled Comp...

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3rd international conference on Electrical Information and Communication Technology (EICT)

作者： Titlee, Rokhsana Rahman, Ashfaq Ur Zaman, Hasan U. Rahman, Hafiz Abdur North South Univ Dept Elect & Comp Engn Dhaka Bangladesh

ISBN: (纸本)9781538623077

In this paper we presented an intangible hand gesture based computer mouse control system. Our approach uses a novel skin color segmentation technique to control mouse movement. The system uses morphological operations like structuring elements and blob counting. Our system can remove other skin like objects from the background. One of the key features of the system is its simplicity. We have successfully tested our system for an intangible interface between human hand and computer mouse without any complex processing. Various mouse operations like cursor movements, right click and left click have been performed. The system has been implemented using webcam and MATLAB.

关键词： computer vision Hand gesture YCbCr Skin color detection blob structuring element

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AN APPROACH TO COLOR CONSTANCY USING MULTIPLE imageS

引用

3rd international CONF ON computer vision

作者： TSUKADA, M OHTA, Y Inst of Inf Sci & Electron Univ of Tsukuba Ibaraki Japan

ISBN: (纸本)0818620579

A novel computational algorithm is proposed for color constancy suitable to robot vision. A robot, or a computer, can exactly memorize image information observed in the past. Then it is natural to use more than one image to achieve color constancy. In the algorithm, it is possible to recover the illumination color and the reflectance color only based on the RGB values of two objects identified on two images. It requires no specific assumption on the scene. Experiments show the validity of the proposed algorithm.

关键词： image processing

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Specular reflection Surface Defects Detection by using Deep Learning 2019

Specular reflection Surface Defects Detection by using Deep ...

引用

3rd international conference on Information System and Data Mining (ICISDM)

作者： Zhang, Zhong Zhang, Borui Akiduki, Takuma Toyohashi Univ Technol Dept Mech Engn 1-1 Hibarigaoka Tenpaku Cho Toyohashi Aichi 4418580 Japan Northeastern Univ Dept Mech Engn 3-11 Wenhua Rd Heping District Shenya Peoples R China

ISBN: (纸本)9781450366359

As you know that defects inspection of specular surface is very difficult because its specular reflection is very strong and defects' reflection is weaker. And the existing computer vision-based industrial parts surface defect detection methods are limited by environmental factors, and the image preprocessing process is complex. On the other hand, with the rapid development of Convolutional Neural Networks (CNN) that is one type of deep learning and has excellent performance for image processing, has led to the rapid development of computer vision research based on deep learning. In this paper, we proposed an ensemble CNN in which integrated two convolutional neural network models for surface defect detection, and obtained better results.

关键词： Specular surface Defect detection Convolutional neural network Machine vision Networks

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