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检索条件"任意字段=Conference on Image Processing - Machine Vision Applications VI"

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Robot Automatic Wire Welding Based on machine vision

Robot Automatic Wire Welding Based on Machine Vision

引用

International conference on Computer Engineering and applications (ICCEA)

作者： Xinsheng Liu Lidan Li Hongmei Xing Shaohua Wang Chuan Zhang Xiyao Diao Zhang Zhuo China North Vehicle Research Institute Beijing China

ISBN: (数字)9798350386776

ISBN: (纸本)9798350386783

Currently, the welding process between electrical connectors and multi-core wires mainly relies on manual operation. This traditional method not only consumes a lot of time and manpower, but also long-term operation may cause certain physical burden and health hazards to the operator. Therefore, researching and implementing automated welding between electrical connectors and multi-core wires has become an urgent problem to be solved. On the basis of summarizing the current research status at home and abroad, the software and hardware parts of the system were designed to meet the requirements of identifying and positioning welding circular electrical connectors. By introducing image processing and machine vision technology, adopting a dual machine collaboration approach and based on machine vision methods, automatic wire welding of electrical connectors has been achieved, improving welding efficiency and reducing the labor intensity of operators. In addition, it is also conducive to promoting the development of industrial automation.

关键词： Connectors Automation Service robots Welding machine vision Wires Collaboration

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image Color Composition Detection Using Classification Supervised Learning to Support Object Detection in machine Learning 1

Image Color Composition Detection Using Classification Super...

引用

1st International conference on Technology Innovation and Its applications, ICTIIA 2022

作者： Tjahjadi, Noach Nathanael Hardjono, Benny Sutrisno Computer Science Faculty Jakarta Indonesia Informatics' Department

ISBN: (数字)9781665488266

ISBN: (纸本)9781665488266

Object detection is one of the most challenging problems in Computer vision. It is difficult because there are many variations between images which have the same object category. Other factors include changes in perspective, scale, partial occlusion. The human visual system is more sensitive to color information than 'gray levels' hence it is used for feature extraction in this study. The color interval value model has been chosen as a new machine Learning Model. It is hoped that this model will be able to predict the colors in images. The study begins with preparation and preprocessing of the dataset, followed by Classifier Training to get a reference dataset, and finally the interval model. Evaluation has been conducted to determine the predictive performance of the created model. Initial results show that the macro average of precision is 60% and the recall is 23%. These results have shown that the model still cannot achieve color prediction of images with higher accuracy. (Abstract) © 2022 IEEE.

关键词： Object detection

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An Efficient Face Recognition Method Based on CNN

An Efficient Face Recognition Method Based on CNN

引用

Power Electronics, Computer applications (ICPECA), IEEE International conference on

作者： Xiu He Feng Ding Guangzhou Xinhua University Guangzhou China

As a prevailing research in artificial intelligence, the application of computer vision is widely used in many fields which are closely related to people's livelihood, such as industrial automation, new retail industry, smart transportation and security monitoring. And the proposed face recognition method is a branch in the field of computer vision, it integrates neural networks, biology, image signal processing, machine learning and other fields, which promote research and cross-development among different disciplines. Hence, this paper focuses on face recognition method by using convolutional neural network(CNN), and CNN has the property of "weight sharing", which has been widely popularized in image recognition, it can greatly simplify the work of large-scale network training. The experiments demonstrate that the proposed face recognition method is successful, and the accuracy of the proposed method can be as high as 98%.

关键词： Training Computer vision image recognition image color analysis Face recognition Color Signal processing

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Reading Pixels Parallel and processing Pixels Parallel (RPP-PPP): An Approach to Detecting and Tracking Objects in Static and Dynamic camera

Reading Pixels Parallel and Processing Pixels Parallel (RPP-...

引用

2022 International conference on machine Learning and Data Engineering, ICMLDE 2022

作者： Sivakumar, K. Shanmugapriya, P. Dept. of Computer Scienc and Engineering SCSVMV Tamilnadu Kanchipuram India

Object detection and Tracking are important tasks in both moving and static camera applications like unmanned vehicles, human-assisted vehicles, product parts inspection, video surveillance, and many more. These applications are required to detect moving objects and track static and dynamic cameras. It also needed to process these tasks with rate and lesser time. Many computer vision applications are always used to read the image/frame pixels one by one from row by column or column by row pattern. Reading pixels one by one from the image/frame took more time in real-time applications. In this proposed method we used to read the pixels from the entire image/frame in parallel using Parallel. For utilities and process these pixels in parallel. In the proposed work we used the background subtraction model for object detection and tracking and noted the difference in both sequential and parallel processes and compared the output with the existing background subtraction model. In this work analyzed the time taken to read the entire image using various tools and languages. The work was implemented using C#.NET parallel. For other thread-based functions to minimize the pixel reading time and processing time for object detection and tracking. Finally, the proposed work gave a considerable performance and compared with the existing system. © 2023 The Authors. Published by Elsevier B.V.

关键词： Pixels

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Redefining Night vision: The Power of MSR-Driven Neural ISP

Redefining Night Vision: The Power of MSR-Driven Neural ISP

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International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jingchao Hou Guanghui He Shanghai Jiao Tong University

This paper addresses two key limitations in existing image Signal processing (ISP) approaches: the suboptimal performance in low-light conditions and the lack of trainability in traditional ISP methods. To tackle these issues, we propose a novel, trainable ISP framework that incorporates both the strengths of traditional ISP techniques and advanced Multi-Scale Retinex (MSR) algorithms for night-time enhancement. Our method consists of three primary components: an ISP-based Luminance Harmonization layer to initially optimize luminance levels in RAW data, a deep learning-based MSR layer for nuanced decomposition of image components, and a specialized enhancement layer for both precise, region-specific luminance enhancement and color denoising. The proposed approach is validated through rigorous experiments on machine vision benchmarks and objective visual quality indicators. Our results demonstrate not only a significant improvement over existing methods but also robust adaptability under diverse lighting conditions. This work offers a versatile ISP framework with promising applications beyond its immediate scope.

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Performance Improvement in Welding Operations Through image processing

Performance Improvement in Welding Operations Through Image ...

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Artificial Intelligence and machine Learning applications Theme: Healthcare and Internet of Things (AIMLA), International conference on

作者： Mohit Kumar Sharma Soumya V Menon Padmaja Tripathy Department of Electrical Engineering Vivekananda Global University Jaipur India Department of Chemistry and Biochemistry School of Sciences JAIN (Deemed to be University) Bangalore Karnataka India Department of Mechnical Engineering ARKA JAIN University Jamshedpur Jharkhand India

ISBN: (数字)9798350349221

ISBN: (纸本)9798350349238

This examination intends to enhance the overall performance of welding operations through picture processing. It's going to use an aggregate of PC vision and gadgets, getting to know to perceive better and tune welds, improve the accuracy of the system, and reduce the capability for mistakes. Particularly, the study will make use of a deep learning method to classify welds in specific classes, allowing the welding operations to be more effectively monitored and operated. Additionally, a convolutional neural network technique will be utilized to pick out the welds and estimate the vital parameters from the image statistics. Sooner or later, a robot arm geared up with a digital camera and a torch can be used to validate the welding process in an actual-world scenario. The effects of this take a look at will be used to enhance the performance and nice of welding operations through higher visibility into the system.

关键词： Welding image processing Production Medical services Manipulators Real-time systems Digital cameras

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Unifying image processing as visual prompting question answering 24

Unifying image processing as visual prompting question answe...

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Proceedings of the 41st International conference on machine Learning

作者： Yihao Liu Xiangyu Chen Xianzheng Ma Xintao Wang Jiantao Zhou Yu Qiao Chao Dong Shanghai Artificial Intelligence Laboratory and Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shanghai Artificial Intelligence Laboratory and Shenzhen Institute of Advanced Technology Chinese Academy of Sciences and University of Macau Shanghai Artificial Intelligence Laboratory Kuaishou Technology University of Macau Shenzhen Institute of Advanced Technology Chinese Academy of Sciences

image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally, task-specific models are developed for individual tasks and designing such models requires distinct expertise. Building upon the success of large language models (LLMs) in natural language processing (NLP), there is a similar trend in computer vision, which focuses on developing large-scale models through pretraining and in-context learning. This paradigm shift reduces the reliance on task-specific models, yielding a powerful unified model to deal with various tasks. However, these advances have predominantly concentrated on high-level vision tasks, with less attention paid to low-level vision tasks. To address this issue, we propose a universal model for general image processing that covers image restoration, image enhancement, image feature extraction tasks, etc. Our proposed framework, named PromptGIP, unifies these diverse image processing tasks within a universal framework. Inspired by NLP question answering (QA) techniques, we employ a visual prompting question answering paradigm. Specifically, we treat the input-output image pair as a structured question-answer sentence, thereby reprogramming the image processing task as a prompting QA problem. PromptGIP can undertake diverse cross-domain tasks using provided visual prompts, eliminating the need for task-specific finetuning. Capable of handling up to 15 different image processing tasks, PromptGIP represents a versatile and adaptive approach to general image processing. Codes will be available at https://***/lyh-18/PromptGIP.

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Autonomous Object Detection and Counting using Edge Detection and image processing Algorithms

Autonomous Object Detection and Counting using Edge Detectio...

引用

International conference on Trends in Electronics and Informatics (ICEI)

作者： Swati B. Patil Jay Chandrakant Shimpi Archana Girish Tanawade Pranali Gajanan Chavan Vrushali Shrimant Tandulkar Information Technology Vishawakarma Institute of Information Technology Pune India T and T infra Ltd Pune India

machine vision applications are commonly utilised in manufacturing lines as low cost, high precision measuring devices. Output facilities can accomplish high production numbers without mistakes thanks to these solutions that offer contactless control and measurement. A camera may be used to carry out machine vision tasks including product counting., error checking., and dimension measuring. This study makes a recommendation for a vision system application that can do inanimate object item enumeration. The recommended solution uses Otsu thresholding., Hough transformations., edge detection methods., and other image processing algorithms to accomplish automatic counting without taking into account the kind or colour of the product. The system primarily uses one camera. The general idea is to get image with balanced contrast., brightness and appropriate HSV values in it. A picture of the items being captured using camera using android device., and different image processing techniques are then applied to the picture. Further., a real-time machine vision programme was deployed and took photos taken from an actual experimental setup. The practical experiments conducted have shown that the suggested technique yields quick., precise., and trustworthy results based on the comparative study of various detection techniques.

关键词： machine learning algorithms Thresholding (Imaging) image edge detection machine vision Measurement uncertainty Production Object detection

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International conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

International Conference on Robotics, Control, Automation an...

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International conference on Robotics, Control, Automation and Artificial Intelligence, RCAAI 2023

ISBN: (纸本)9789819746491

The proceedings contain 46 papers. The special focus in this conference is on Robotics, Control, Automation and Artificial Intelligence . The topics include: Wildlife Intrusion Detection and Prevention in Farm Fields Using IoT Technology;predictive Maintenance of Gearbox: A Cost-Effective IoT Approach for Remaining Useful Life Estimation;Ergonomic Knee Exoskeleton System with Real-World Perception and Actuation Using Computer vision, image processing and LiDAR Data Fusion;dynamic Voltage and Frequency Scaling Implementation for System-Level Power Reduction;Development of CNC Gantry Drilling machine;a Comprehensive Survey on Robo-Ethics;semi-automated Inkjet Marking System with Lead Screw Actuation for Aerospace Components;effect of Nano-coolant Blends on the Performance of Compact Heat Exchanger—A Review;Identification of Deity images Using CNN and Transfer Learning Models;reinventing Supermarkets: Enhanced Customer Convenience Through Deep Learning Technology;model Predictive Controller Based virtual Inertia Controller for Enhancing the Frequency Stability of Microgrid;GUI-Enabled Wearable Solution for Tremor Detection and Fall Prevention;design and Development of Electrical System for Retrofitting a Turning Centre;an Elucidative Analysis of Quadruped Robot’s Locomotion and Design;comparative Assessment of Pathfinding Algorithms for Efficient Maze Navigation in Industrial applications;automated Bundy Tube Metrology with Deep Learning;an Advanced Surveillance Motorized Car for Real-Time Inspection with Sensing Capabilities;cybersecurity Threats, Forensics, and Challenges;Tea Leaf Disease Detection and Classification Using CNN, SVM and Dense Net;Enhancing Beach Cleaning Efficiency: ML-Based Litter Detection by an Autonomous Robot;ALIVE: A Low-Cost Interactive Vaccine Storage Environment Module Ensuring Easy Portability and Remote Tracking of Operational Logistics to the Last-Mile.

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Efficient Approximate Vedic Multiplier: Design, Analysis, and Application in image Blending

Efficient Approximate Vedic Multiplier: Design, Analysis, an...

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Computer vision and machine Intelligence (CVMI), International conference on

作者： Sanjiv Kumar Gupta Nilesh Kumar Yadav Amit Dhawan Manish Tiwari Sumit Kumar Jha Department of Electronics and Communication Engineering Motilal Nehru National Institute of Technology Allahabad Prayagraj Uttar Pradesh India

Approximate computing has become a widely recognized method for designing energy-efficient arithmetic architectures in the context of error-tolerant applications. This paper presents the design and analysis of a 4-bit approximate Vedic multiplier (AVMT) using the Urdhva Tiryagbhyam method. This Vedic approach, involving vertical and crosswise steps, outperforms traditional multiplication in terms of efficiency. An approximate 2-bit multiplier (AVM2) is designed, and an AVMT is proposed using AVM2. The proposed architecture has better propagation delay and less area utilization compared to other conventional multipliers. AVMT has an 11% reduction in area consumption and a 12% increase in processing speed compared to the exact Vedic multiplier. To assess its practicality in real-world scenarios, the proposed multiplier is integrated into an image-blending application. The results indicate that the system achieves a Structural Similarity Index (SSIM) average value of 0.91, which proves to be suitable for error-resilient image processing applications.

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