12,038 resources related to Machine Vision
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted papers will be peer reviewed. Accepted high quality papers will be presented in oral and postersessions, will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE
The CDC is the premier conference dedicated to the advancement of the theory and practice of systems and control. The CDC annually brings together an international community of researchers and practitioners in the field of automatic control to discuss new research results, perspectives on future developments, and innovative applications relevant to decision making, automatic control, and related areas.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
The International Conference on Robotics and Automation (ICRA) is the IEEE Robotics and Automation Society’s biggest conference and one of the leading international forums for robotics researchers to present their work.
The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Part I will now contain regular papers focusing on all matters related to fundamental theory, applications, analog and digital signal processing. Part II will report on the latest significant results across all of these topic areas.
Computer, the flagship publication of the IEEE Computer Society, publishes peer-reviewed technical content that covers all aspects of computer science, computer engineering, technology, and applications. Computer is a resource that practitioners, researchers, and managers can rely on to provide timely information about current research developments, trends, best practices, and changes in the profession.
The magazine covers theory, analysis, design (computer-aided design), and practical implementation of circuits, and the application of circuit theoretic techniques to systems and to signal processing. Content is written for the spectrum of activities from basic scientific theory to industrial applications.
IEE Colloquium on Application of Machine Vision, 1995
2012 International Conference on Machine Vision and Image Processing (MVIP), 2012
Classification of objects has been a significant area of concern in machine vision applications. In recent years, Support Vector Machines (SVM) is gaining popularity as an efficient data classification algorithm and is being widely used in many machine vision applications due to its good data generalization performance. The present paper describes the development of multi-class SVM classifier employing one-versus-one max-wins ...
2010 International Conference on Machine Vision and Human-machine Interface, 2010
The corner extraction of point cloud is an important part of product quality testing in the field of machine vision, the key problem of corner extraction is curvature estimation, so in this paper, an adaptive corner extraction method of point cloud is given. In this method, firstly make use of the image edge extraction method to obtain the edge points, ...
2009 Second International Conference on Machine Vision, 2009
Today's high speed, complex manufacturing systems require the development of automation technologies that can be efficiently integrated into the systems and used in manufacture floors. This article presents a successful industrial application of machine vision technology for medical syringe assembly. The developed vision inspection station with ten cameras that was integrated into an assembly line has a capability of inspecting ...
2017 24th International Conference on Mechatronics and Machine Vision in Practice (M2VIP), 2017
Automated inspection of apples requires that the entire surface of the fruit passes through the view of at least one camera. This work uses Monte Carlo simulation to investigate the extent of coverage of the apple surface when viewed by three four and five cameras arranged in a plane. Each camera has a circular inspection area that is 45 to ...
Tech Super Stars Panelist - Karen Bartleson: 2016 Technology Time Machine
IROS TV 2019- How to Build a Robot: Vision Based Estimation of Driving Energy for Planetary Rovers
Robotics History: Narratives and Networks Oral Histories: Gary Bradsky
Q&A with Alicia Abella, Assistant VP at AT&T
Q&A with Karen Bartleson: IEEE Technology Time Machine Podcast, Episode 3
Robotics History: Narratives and Networks Oral Histories: Danica Kragic
LPIRC: Developing Mobile Computer Vision Models
Visual Wake Words Challenge - Aakanksha Chowdhery - LPIRC 2018
Achieving Low Latency Mobile Edge Cloud Services - Dipankar Raychaudhuri - IEEE Sarnoff Symposium, 2019
Aerial Experimentation & Research Platform for Advanced Wireless - Rudra Dutta - IEEE Sarnoff Symposium, 2019
IROS TV 2019- Istituto Italiano di Tecnologia (IIT)- Human Centered Science and Technologies
IEEE Entrepreneurship @ #CollisionConf: Mighty AI
ICASSP 2010 - Advances in Neural Engineering
Globecom 2019: Geng Wu Keynote
LPIRC: On Device Vision, Google AI-Style
IROS TV 2019- Rutgers University- Center for Accelerated Real Time Analytics
Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
DOE Vision and Programmatic Activities in Advanced Computing Technologies: IEEE Rebooting Computing 2017
Q&A with Kip Ludwig: IEEE Brain Podcast, Episode 7
Classification of objects has been a significant area of concern in machine vision applications. In recent years, Support Vector Machines (SVM) is gaining popularity as an efficient data classification algorithm and is being widely used in many machine vision applications due to its good data generalization performance. The present paper describes the development of multi-class SVM classifier employing one-versus-one max-wins voting method and using Radial Basis Function (RBF) and Linear kernels. The developed classifiers have been applied for color-based classification of apple fruits into three pre-defined classes and their performance is compared with conventional K-Nearest Neighbor (KNN) and Naïve Bayes classifiers. The multi-class SVM classifier with RBF kernel has shown superior classification performance.
The corner extraction of point cloud is an important part of product quality testing in the field of machine vision, the key problem of corner extraction is curvature estimation, so in this paper, an adaptive corner extraction method of point cloud is given. In this method, firstly make use of the image edge extraction method to obtain the edge points, and sorting the points to obtain the ordered edge points, secondly using the adaptive method to calculate the support region for the straight line fitting, in the end, from the intersection of the straight lines to get the curvature angle. Using this method can obtain the corners and borders of point cloud, and solve the problem of support region size when calculating curvature or fitting straight line. The experimental results show that this method has good anti- interference, and good ability to detect and locate the corner points.
Today's high speed, complex manufacturing systems require the development of automation technologies that can be efficiently integrated into the systems and used in manufacture floors. This article presents a successful industrial application of machine vision technology for medical syringe assembly. The developed vision inspection station with ten cameras that was integrated into an assembly line has a capability of inspecting 5 syringes per 250 million seconds for a total production rate of ranging from 300 to 600 parts per minute.
Automated inspection of apples requires that the entire surface of the fruit passes through the view of at least one camera. This work uses Monte Carlo simulation to investigate the extent of coverage of the apple surface when viewed by three four and five cameras arranged in a plane. Each camera has a circular inspection area that is 45 to 50% of the apple size. The number of images per apple rotation and the level of asymmetry (or drunkenness) of the apple's roll are varied to determine their effect on the extent of surface coverage. Results for a single rotation of the apple show that complete surface coverage is not possible with three cameras and up to 30 images per rotation. Four cameras can provide complete surface coverage at 30 images per rotation and five cameras can provide complete surface coverage at 15 images per rotation. The greater the extent of surface coverage the greater the redundancy in inspection and the greater the possibility of false positives. However it is likely that several inspections of each defect will provide more secure recognition than that provided by just one sight of the defect. Increase in the drunkenness of the apple roll has a large adverse effect on the extent of surface coverage and redundancy.
Indexing mechanisms find several applications in machineries, equipments and instruments such as, watches, projectors, machine tools, printing and pressing machinery, packaging and automatic machinery, etc., Currently, Geneva mechanism is extensively used for this purpose due to its reduction in shock loading. Therefore, Geneva mechanism should be carefully designed and those attained dimensions inclusive of tolerances must be measured closely to the true value. These measurements need more than one measuring instrument, which cumulatively costs much. Machine Vision is the suitable remedial solution, which acts fast and reduces the cost for large quantity measurement. In this work, an updated version of analyzer is developed by collaborating Machine vision software with Visual Basic 6.0 to make easy and comfortable, even the user is not fully aware of machine vision operations. The Geneva wheel is designed and modeled using CAD software, the dimensional features of the component is compared with the original modeled CAD image, taken as template. For this, a machine Vision software named Sherlock™ is interfaced with Visual Basic 6.0 designed window. This window is customized for user friendly environment to facilitate even an unskilled operator to operate the inspection system without having a deep knowledge about Machine Vision and its software's.
Since the 1970's, we have been developing technologies in industrial machine vision including intelligent character recognition to produce automated machines for factories, banks, and post offices. In my talk, I will first introduce a brief history of the industrial machine vision and the intelligent character recognition technologies in applied fields. Then I will discuss the strategies and developments in the fields. I will end my talk by touching on my personal experience as a researcher. As time has passed, our major research objective has changed from factory automation to office automation and from office automation to social/security automation. Consequently, the demand for machines that are capable of dealing with more complex and difficult automation tasks has grown. To meet these demands, a machine often requires multiple recognition procedures. This normally leads to the final recognition rate worsening as the number of procedures increases. Therefore, we propose a multiple-hypothesis strategy and an information-integration strategy to improve the final recognition rate so that it can meet the machine's specification. Then, it will be shown that the rejection ability of the recognition procedures has an important role in using these strategies effectively. The usefulness of these strategies has been proved through the successful development of mail sorting machines, document readers, and intelligent automated teller machines. Those developments are also described in detail in my talk. Finally, I would like to touch on my experiences as an industrial researcher, which can be summed up by the phrases "practicality first, novelty second," "development first, research second," and "non-vision first, vision second."
Many materials are manufactured industrially in the form of a continuously moving strip or web; examples include metal strip (cold rolled, plated and stainless steel; aluminium), paper, glass, textile materials (woven, knitted and printed fabric; lace; carpet), etc etc. To ensure high product quality it is necessary to inspect visually,; this inspection can be automated using machine vision. The motivations include increased cost effectiveness, much higher operating speeds, reliability, consistency and objectivity, and improved record keeping. For some applications, automated inspection using machine vision is already operational; other cases remain to be conquered. This paper discusses progress in the application of machine vision to automate web material inspection, concentrating on the development of a generalised theory for signal processing for defect detection. Limitations to the present state-of-the-art in machine vision inspection are considered. It is suggested that the dominant factor limiting the spread of machine vision inspection in industry is cost effectiveness.<<ETX>>
This study aims at developing a non-contact method for measuring the grinding wheel loading and wheel wear and thereby determining the optimum dressing intervals. With the aid of a machine vision system, this paper presents a systematic process for measuring the wheel loading. The images of the grinding wheel have been taken using a digital camera. These images have been transferred to the computer and are processed for determining the percentage of loading. The image toolbox of MATLAB has been used for image processing. Global thresholding technique has been used to differentiate the loaded region of the wheel from rest of the background. Images of the grinding wheel in static as well as dynamic condition are taken and are analyzed. Experimental results are presented which show the ability of using machine vision system in the online monitoring of the grinding wheel loading.
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