The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Daniel Crevier - One of the best experts on this subject based on the ideXlab platform.
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Educational experiments in Machine Vision
IEEE Transactions on Education, 1996Co-Authors: Daniel CrevierAbstract:We present a set of four educational experiments in Machine Vision. These were designed to run on low-cost hardware which is yet powerful enough to serve in genuine industrial applications of Machine Vision. The experiments introduce students to thresholding, connected component analysis, Hough transforms, stereo Vision, and color coordinate systems. The programming involved is close enough to the hardware to expose students to real-time processing techniques and prepares them to tackle the type of problems they will face in field applications of Machine Vision. The experiments are: locate coins in an image, identify their denominations, and count the amount of money present; extract the straight edges of a cube by the Hough transform technique; extract three-dimensional (3-D) information from left and right images of the same cube; and transform color images from RGB to HSI coordinates and visually assess the results.
Kapul Dev Gill - One of the best experts on this subject based on the ideXlab platform.
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A STUDY OF Machine Vision IN THE AUTOMOTIVE INDUSTRY
1991Co-Authors: Kapul Dev GillAbstract:With the growth of industrial automation, it has become increasingly important to validate the quality of every manufactured part during production. Until now, human visual inspection aided with hard tooling or Machines have been the primary means to this end, but the speed of today's production lines, the complexity of production equipment and the highest standards of quality to which parts must adhere frequently, make the traditional methods of industrial inspection and control impractical, if not impossible. Subsequently, new solutions have been developed for the monitoring and control of industrial processes, in realtime. One such technology is the area of Machine Vision. After many years of research and development, computerised Vision systems are now leaving the laboratory and are being used successfully in the factory environment. They are both robust and competitively priced as a sensing technique which has now opened up a whole new sector for automation. Machine Vision systems are becoming an important integral part of the automotive manufacturing process, with applications ranging from inspection, classification, robot guidance, assembly verification through to process monitoring and control. Although the number of systems in current use is still relatively small, there can be no doubt, given the issues at stake, that the automotive industry will once again lead the way with the implementation of Machine Vision just as it has done robotic technology. The thesis considered the issue of Machine Vision and in particular, its deployment within the automotive industry. The thesis has presented work on Machine Vision for the prospective end-user and not the designer of such systems. It will provide sufficient background about the subject, to separate Machine Vision promises from reality and permit intelligent decisions regarding Machine Vision applications to be made. The initial part of the dissertation focussed on the strategic issues affecting the selection of Machine Vision at the planning stage, such as a listing of the factors to justify investment, the capability of the technology and type of problems that are associated with this relatively new but complex science. Though it is widely accepted that no two industrial Machine Vision systems are identical, knowledge of the basic fundamentals which underpin the structure of the technology in its application is presented. This work covered a structured description detailing typical hardware components such as camera technology, lighting systems, etc... which form an integral part of an industrial system and discussions regarding the criteria for selection are presented. To complement this work, a further section is specifically devoted to the bewildering array of Vision software analysis techniques which are currently available today. A detailed description of the various techniques that are applied to images in order to make use of and understand the data contained within them are discussed and explored. Applications for Machine Vision fall into two main categories namely robotic guidance and inspection. Obviously within each category there are many further subgroups. Within this context the latter part of the thesis reviews with a well structured description of several industrial case studies derived from the automotive industry, which illustrate that Machine Vision is capable of providing real time solutions to manufacturing based problems. In conclusion, despite the limited availability of industrially based Machine Vision systems, the success of implementation is not always guaranteed, as the technology imposes both technical limitations and introduce new human engineering considerations. By understanding the application and the implications of the technical requirements on both the "staging" and the "image-processing" power required of the Machine Vision system. The thesis has shown that the most significant elements of a successful application are indeed the lighting, optics, component design, etc... - the "Staging". From the case studies investigated, optimised "staging" has resulted in the need for less computing power in the Machine Vision system. Inevitably, greater computing power not only requires more time but is generally more expensive. The experience gained from the this project, has demonstrated that Machine Vision technology is a realistic alternative means of capturing data in real-time. Since the current limitations of the technology are well suited to the delivery process of the quality function within the manufacturing process.
Robin Bradbeer - One of the best experts on this subject based on the ideXlab platform.
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Mechatronics and Machine Vision in Practice - Mechatronics and Machine Vision in practice
2008Co-Authors: John Billingsley, Robin BradbeerAbstract:Proceedings of 13th Annual Conference on Mechatronics and Machine Vision in Practice, Toowoomba, Australia, 5-7 December 2006.
Paul F. Whelan - One of the best experts on this subject based on the ideXlab platform.
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An Introduction to Machine Vision
Machine Vision Algorithms in Java, 2001Co-Authors: Paul F. Whelan, Derek MolloyAbstract:The purpose of this chapter is to introduce the reader to the basic principles of Machine Vision. In this discussion the differences between computer, Machine and human Vision are highlighted. In doing so, we draw attention to the key elements involved in Machine Vision systems engineering. While this book concentrates on the software aspects of the design cycle, this task cannot be taken in isolation. Successful application of Vision technology to real-world problems requires an appreciation of all the issues involved.
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Visual programming for Machine Vision
Intelligent Machine Vision, 2001Co-Authors: Paul F. WhelanAbstract:This chapter will outline the development of a visual programming environment for Machine Vision applications, namely JVision 2 [WHE97a, WHE97b]. The purpose of JVision is to provide Machine Vision developers with access to a non-platform-specific software development environment. This requirement was realized through the use of Java, a platform-independent programming language. The software development environment provides an intuitive interface which is achieved using a drag-and-drop block-diagram approach, where each image-processing operation is represented by a graphical block with inputs and outputs which can be interconnected, edited, and deleted as required. Java provides accessibility, thereby reducing the workload and increasing the “deliverables” in terms of cross-platform compatibility and increased user base. JVision is just one example of such a visual programming development environment for Machine Vision. Other notable examples include Khoros[KR199] and WiT [W1T99]. See [JAW96, GOS96] for details on the Java programming language.
S. Aishwarya - One of the best experts on this subject based on the ideXlab platform.
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A Review on Applications of Machine Vision Systems in Industries
Indian Journal of Science and Technology, 2016Co-Authors: V. Nandini, R. Deepak Vishal, C. Arun Prakash, S. AishwaryaAbstract:Objectives: This paper presents a review on the applications of Machine Vision technology in various industries such as food industry, textile industry, PCB industry and tile industry, thereby enhancing the conventional system. Methods: This technique used for inspection, sorting, handling and testing various components by Machine Vision is given with ample illustrations. Using this as a guide, generic Machine Vision approaches can be derived and applied to solve many problems. Findings: The Machine Vision techniques applied in the above mentioned fields are studied. It has been proven from research that automating processes in several industries has led to better results than manual processing. As a scientific discipline, Machine Vision is concerned with the concept behind artificial systems that incorporates image processing. Machine Vision systems are based on extracting the images of the objects which are to be inspected/tested and processing them to retrieve the required data. This review paper provides an insight on how to combine two or more Machine Vision techniques to successfully incorporate them in more generic applications that may have the potential to perform with greater agility and accuracy.
