Relevant bibliographies by topics / Industrial machine vision

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Industrial machine vision'

Author: Grafiati
Published: 26 July 2024

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Journal articles on the topic "Industrial machine vision"

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., S. Sathiyamoorthy. "INDUSTRIAL APPLICATION OF MACHINE VISION." International Journal of Research in Engineering and Technology 03, no. 19 (May 25, 2014): 678–82. http://dx.doi.org/10.15623/ijret.2014.0319120.

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Holland, Steven W., and Robert B. Tilove. "Industrial machine vision: lessons and challenges." Optics News 13, no. 2 (February 1, 1987): 12. http://dx.doi.org/10.1364/on.13.2.000012.

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Bai, Ruishuang, Nan Jiang, Le Yu, and Jinyang Zhao. "Research on Industrial Online Detection Based on Machine Vision Measurement System." Journal of Physics: Conference Series 2023, no. 1 (September 1, 2021): 012052. http://dx.doi.org/10.1088/1742-6596/2023/1/012052.

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Abstract Machine vision based on image processing has played a huge function in promoting the level of online monitoring of industrial products. It has a broad utilization prospect in the field of industrial online detection, so it has important research value. Based on this, this paper first analyses the principle of machine vision measurement system, then studies the industrial on-line detection utilization of machine vision measurement system, and finally gives the utilization and development prospect of machine vision measurement system in the field of industrial online detection.
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Lu, K. "Perfect vision [machine vision system]." Manufacturing Engineer 85, no. 5 (October 1, 2006): 42–45. http://dx.doi.org/10.1049/me:20060509.

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Connolly, Christine. "Machine vision developments." Sensor Review 26, no. 4 (October 2006): 277–82. http://dx.doi.org/10.1108/02602280610691980.

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Jia, Limin, and Yang Wang. "Research on Industrial Production Defect Detection Method Based on Machine Vision Technology in Industrial Internet of Things." Traitement du Signal 39, no. 6 (December 31, 2022): 2061–68. http://dx.doi.org/10.18280/ts.390618.

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The realization of automatic operation of production by the industrial Internet of Things needs the functional assistance of machine vision technology. Different from the recognition and detection of some known features, it is difficult to realize defect detection in machine vision applications. Therefore, this article studies the industrial production defect detection method based on machine vision technology in industrial Internet of Things. Firstly, in the second chapter, the images of industrial products collected by machine vision system are preprocessed and thinned to obtain more ideal detection accuracy and measurement accuracy. The methods of image binarization, morphological processing, thinning and burr elimination are given in detail. In the third chapter, product defect detection model is constructed based on U-Net network, and residual structure, hole convolution module, strip pooling module and attention mechanism module are introduced to optimize the network model. Experimental results verify the effectiveness of the model for product defect detection.
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Xie, Xiang. "Industrial Robot Assembly Line Design Using Machine Vision." Journal of Robotics 2023 (March 30, 2023): 1–13. http://dx.doi.org/10.1155/2023/4409033.

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In order to further improve the functional requirements and performance indicators of the industrial robot assembly system and more accurately realize the measurement and recognition of the target position of the assembly line by the vision system, this article constructs a robot assembly line system based on obstacle detection and robot arm obstacle path planning based on machine vision technology and further improves the intelligence and accuracy of the assembly line system through the design and optimization of the system software module. Through the experimental verification of the positioning error based on the eye-to-hand binocular vision system and eye-in-hand monocular vision system, the system proposed in this article meets the design accuracy requirements of less than 0.1 mm in x/y direction and less than 1 mm in depth direction and verifies the feasibility and high accuracy of the system.
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Miller, John W. V. "Special Section on Machine Vision for Industrial Inspection." Journal of Electronic Imaging 10, no. 1 (January 1, 2001): 194. http://dx.doi.org/10.1117/1.1337356.

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Golnabi, H., and A. Asadpour. "Design and application of industrial machine vision systems." Robotics and Computer-Integrated Manufacturing 23, no. 6 (December 2007): 630–37. http://dx.doi.org/10.1016/j.rcim.2007.02.005.

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Redarce, Tanneguy, Yves Lucas, Maurice Betemps, and Alain Jutard. "CAD off-line programming for industrial machine vision." Journal of Intelligent and Robotic Systems 4, no. 2 (June 1991): 129–43. http://dx.doi.org/10.1007/bf00440416.

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Dissertations / Theses on the topic "Industrial machine vision"

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Field, Matthew. "Machine vision system developments for industrial inspection applications." Thesis, University of Central Lancashire, 1997. http://clok.uclan.ac.uk/20334/.

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This thesis describes research in the area of automated industrial inspection using machine vision systems. It is anticipated that the algorithms described will contribute to the design of a machine vision system for the automatic surface inspection of cylindrical pellets. Firstly, the acquisition and segmentation of pellet tray images using area capture is described. Individual pellets are segmented from a pellet tray image by a novel system using the Radon transform coupled with data clustering. Subsequent to the segmentation, the linking of four pellet views depicting the entire circumferential area of the pellet is described along with a simple technique to compensate for intensity variations brought about by imaging the three-dimensional cylindrical surface of the pellet. The image processing techniques of filtering, edge detection, thresholding and morphology are used in the segmentation of grey level pellet defect images. The grey level pellet images are low-pass filtered and binary images formed using edge detection with thresholding. Binary morphology operators are then used in conjunction with a termination condition based on the number of objects in the image to ensure homogenous defect representations. The problem of overlapping defects is addressed, resulting in a second algorithm using the Radon transform coupled with data clustering. Prior to classification salient features are extracted from a set of synthetic binary defect images to form feature vectors. The novel idea of image object classification using 100% fuzzy inference is described, and results are shown to be superior to results obtained by feature space classifiers. The sub-classification of crack defects is carried out using a heuristic classifier, and the parameterisation of pellet defects is described.
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Cho, Tai-Hoon. "A knowledge-based machine vision system for automated industrial web inspection." Diss., This resource online, 1991. http://scholar.lib.vt.edu/theses/available/etd-07282008-134615/.

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Leung, Tin Wah William. "High precision camera-based colour inspection of industrial products." Thesis, University of Huddersfield, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338605.

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King, William E. "Using an FPGA-Based Processing Platform in an Industrial Machine Vision System." Thesis, Virginia Tech, 1998. http://hdl.handle.net/10919/31799.

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This thesis describes the development of a commercial machine vision system as a case study for utilizing the Modular Reprogrammable Real-time Processing Hardware (MORRPH) board. The commercial system described in this thesis is based on a prototype system that was developed as a test-bed for developing the necessary concepts and algorithms. The prototype system utilized color linescan cameras, custom framegrabbers, and standard PCs to color-sort red oak parts (staves). When a furniture manufacturer is building a panel, very often they come from edge-glued paneled parts. These are panels formed by gluing several smaller staves together along their edges to form a larger panel. The value of the panel is very much dependent upon the â matchâ of the individual stavesâ i.e. how well they create the illusion that the panel came from a single board as opposed to several staves. The prototype system was able to accurately classify staves based on color into classes defined through a training process. Based on Trichromatic Color Theory, the system developed a probability density function in 3-D color space for each class based on the parts assigned to that class during training. While sorting, the probability density function was generated for each scanned piece, and compared with each of the class probability density functions. The piece was labeled the name of the class whose probability density function it most closely matched. A â best-faceâ algorithm was also developed to arbitrate between pieces whose top and bottom faces did not fall into the same classes. [1] describes the prototype system in much greater detail. In developing a commercial-quality machine vision system based on the prototype, the primary goal was to improve throughput. A Field Programmable Gate Array (FPGA)-based Custom Computing Machine (FCCM) called the MORRPH was selected to assume most of the computational burden, and increase throughput in the commercial system. The MORRPH was implemented as an ISA-bus interface card, with a 3 x 2 array of Processing Elements (PE). Each PE consists of an open socket which can be populated with a Xilinx 4000 series FPGA, and an open support socket which can be populated with support chips such as external RAM, math processors, etc. In implementing the prototype algorithms for the commercial system, a partition was created between those algorithms that would be implemented on the MORRPH board, and those that would be left as implemented on the host PC. It was decided to implement such algorithms as Field-Of-View operators, Shade Correction, Background Extraction, Gray-Scale Channel Generation, and Histogram Generation on the MORRPH board, and to leave the remainder of the classification algorithms on the host. By utilizing the MORRPH board, an industrial machine vision system was developed that has exceeded customer expectations for both accuracy and throughput. Additionally, the color-sorter received the International Woodworking Fairâ s Challengers Award for outstanding innovation.
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Mosberger, Rafael. "Vision-based Human Detection from Mobile Machinery in Industrial Environments." Doctoral thesis, Örebro universitet, Institutionen för naturvetenskap och teknik, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:oru:diva-48324.

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The problem addressed in this thesis is the detection, localisation and tracking of human workers from mobile industrial machinery using a customised vision system developed at Örebro University. Coined the RefleX Vision System, its hardware configuration and computer vision algorithms were specifically designed for real-world industrial scenarios where workers are required to wear protective high-visibility garments with retro-reflective markers. The demand for robust industry-purpose human sensing methods originates from the fact that many industrial environments represent work spaces that are shared between humans and mobile machinery. Typical examples of such environments include construction sites, surface and underground mines, storage yards and warehouses. Here, accidents involving mobile equipment and human workers frequently result in serious injuries and fatalities. Robust sensor-based detection of humans in the surrounding of mobile equipment is therefore an active research topic and represents a crucial requirement for safe vehicle operation and accident prevention in increasingly automated production sites. Addressing the described safety issue, this thesis presents a collection of papers which introduce, analyse and evaluate a novel vision-based method for detecting humans equipped with protective high-visibility garments in the neighbourhood of manned or unmanned industrial vehicles. The thesis provides a comprehensive discussion of the numerous aspects regarding the design of the hardware and the computer vision algorithms that constitute the vision system. An active nearinfrared camera setup that is customised for the robust perception of retroreflective markers builds the basis for the sensing method. Using its specific input, a set of computer vision and machine learning algorithms then perform extraction, analysis, classification and localisation of the observed reflective patterns, and eventually detection and tracking of workers with protective garments. Multiple real-world challenges, which existing methods frequently struggle to cope with, are discussed throughout the thesis, including varying ambient lighting conditions and human body pose variation. The presented work has been carried out with a strong focus on industrial applicability, and therefore includes an extensive experimental evaluation in a number of different real-world indoor and outdoor work environments.
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Koslav, Maria B. "Development of a machine vision based oyster meat sorter." Thesis, Virginia Polytechnic Institute and State University, 1989. http://hdl.handle.net/10919/53225.

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Oyster meats are currently sorted by hand using volume as the sorting parameter. Hand grading is inaccurate, time consuming and costly. Previous research on physical properties of oyster meats showed a high correlation between projected area of oyster meats and their volume thus allowing the use of projected area measurements as a sorting criterion. A machine vision based oyster meat sorting machine was developed to mechanize the sorting process. The machine consists of a dark conveyor belt transporting singulated oysters through a grading station and then along a row of fast acting water jet valves which separates the stream of oysters into 3 classes. The vision system consists of a monochrome television camera, flash light illumination to "freeze" the images, a digitizer/transmitter and a Personal Computer as an image processing unit. Software synchronizes the flash light and digitization of images and calculates projected area of each meat using the planimeter method. The grading results are sent to a valve control board which actuates the spray valves. The sorting rate is 37 oyster meats/min with a sorting accuracy of 87.5%. A description of the design work, adjustment and l calibration procedures and a final sorting test is included.
Master of Science
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Zhang, Zhengwen. "Self-learning systems and neural networks for image texture analysis." Thesis, Brunel University, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.296217.

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Parker, Johne' Michelle. "An analytical and experimental investigation of physically-accurate synthetic images for machine vision design." Diss., Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/19038.

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Parvez, Bilal. "Embedded Vision Machine Learning on Embedded Devices for Image classification in Industrial Internet of things." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-219622.

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Because of Machine Learning, machines have become extremely good at image classification in near real time. With using significant training data, powerful machines can be trained to recognize images as good as any human would. Till now the norm has been to have pictures sent to a server and have the server recognize them. With increasing number of sensors the trend is moving towards edge computing to curb the increasing rate of data transfer and communication bottlenecks. The idea is to do the processing locally or as close to the sensor as possible and then only transmit actionable data to the server. While, this does solve plethora of communication problems, specially in industrial settings, it creates a new problem. The sensors need to do this computationally intensive image classification which is a challenge for embedded/wearable devices, due to their resource constrained nature. This thesis analyzes Machine Learning algorithms and libraries from the motivation of porting image classifiers to embedded devices. This includes, comparing different supervised Machine Learning approaches to image classification and figuring out which are most suited for being ported to embedded devices. Taking a step forward in making the process of testing and implementing Machine Learning algorithms as easy as their desktop counterparts. The goal is to ease the process of porting new image recognition and classification algorithms on a host of different embedded devices and to provide motivations behind design decisions. The final proposal goes through all design considerations and implements a prototype that is hardware independent. Which can be used as a reference for designing and then later porting of Machine Learning classifiers to embedded devices.
Maskiner har blivit extremt bra på bildklassificering i nära realtid. På grund av maskininlärning med kraftig träningsdata, kan kraftfulla maskiner utbildas för att känna igen bilder så bra som alla människor skulle. Hittills har trenden varit att få bilderna skickade till en server och sedan få servern att känna igen bilderna. Men eftersom sensorerna ökar i antal, går trenden mot så kallad "edge computing" för att stryka den ökande graden av dataöverföring och kommunikationsflaskhalsar. Tanken är att göra bearbetningen lokalt eller så nära sensorn som möjligt och sedan bara överföra aktiv data till servern. Samtidigt som detta löser överflöd av kommunikationsproblem, speciellt i industriella inställningar, skapar det ett nytt problem. Sensorerna måste kunna göra denna beräkningsintensiva bildklassificering ombord vilket speciellt är en utmaning för inbyggda system och bärbara enheter, på grund av sin resursbegränsade natur. Denna avhandling analyserar maskininlärningsalgoritmer och biblioteken från motivationen att portera generiska bildklassificatorer till inbyggda system. Att jämföra olika övervakade maskininlärningsmetoder för bildklassificering, utreda vilka som är mest lämpade för att bli porterade till inbyggda system, för att göra processen att testa och implementera maskininlärningsalgoritmer lika enkelt som sina skrivbordsmodeller. Målet är att underlätta processen för att portera nya bildigenkännings och klassificeringsalgoritmer på en mängd olika inbyggda system och att ge motivation bakom designbeslut som tagits och för att beskriva det snabbaste sättet att skapa en prototyp med "embedded vision design". Det slutliga förslaget går igenom all hänsyn till konstruktion och implementerar en prototyp som är maskinvaruoberoende och kan användas för snabb framtagning av prototyper och sedan senare överföring av maskininlärningsklassificatorer till inbyggda system.
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English, Jonathan. "Machine vision for the determination of identity, orientation and position of two dimensional industrial components." Thesis, De Montfort University, 1996. http://hdl.handle.net/2086/4811.

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Books on the topic "Industrial machine vision"

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Alexander, Hornberg, ed. Handbook of machine vision. Weinheim: Wiley-VCH, 2006.

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G, Batchelor Bruce, and Whelan Paul F. 1963-, eds. Selected papers on industrial machine vision systems. Bellingham, Wash: SPIE Optical Engineering Press, 1994.

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International Exhibition and Conference on Automatic Identification and Sensor Technologies (6th 1992 Stuttgart, Germany). IDENT VISION: Identification technologies, machine vision technologies. Stuttgart: Stuttgarter Messe- und Kongressgesellschaft, 1992.

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Ken, Stonecipher, ed. Industrial Robotics, Machine Vision, and Artificial Intelligence. Indianapolis, IN: Howard W. Sams & Co., 1989.

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Khang, Alex, Vugar Abdullayev Hajimahmud, Anuradha Misra, and Eugenia Litvinova. Machine Vision and Industrial Robotics in Manufacturing. Boca Raton: CRC Press, 2024. http://dx.doi.org/10.1201/9781003438137.

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Matti, Pietikäinen, and Pau L. F. 1948-, eds. Machine vision for advanced production. Singapore: World Scientific, 1996.

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service), SpringerLink (Online, ed. Machine Vision Handbook. London: Springer London, 2012.

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International Conference on Industrial Electronics, Control, and Instrumentation. (13th 1987 Cambridge, Mass.). IECON '87 : industrial applications of robotics and machine vision. Edited by Abramovich Abe, IEEE Industrial Electronics Society, and Keisoku Jidō Seigyo Gakkai (Japan). New York: Institute of Electrical and Electronics Engineers, 1987.

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International, Workshop on Industrial Applications of Machine Vision and Machine Intelligence-Seiken Symposium (1987 Tokyo Japan). Proceedings International Workshop on Industrial Applications of Machine Vision and Machine Intelligence: Seiken Symposium, Tokyo, Japan February 2-5, 1987. New York, N.Y: Publishing Service, Institute of Electrical and Electronics Engineers, 1987.

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G, Batchelor Bruce, Solomon Susan Snell, Waltz Frederick M, Society of Photo-optical Instrumentation Engineers., and Automated Imaging Association, eds. Machine vision applications, architectures, and systems integration II: 7-9 September 1993, Boston, Massachusetts. Bellingham, Wash., USA: SPIE, 1993.

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Book chapters on the topic "Industrial machine vision"

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Frigeni, Fabrizio. "Machine Vision." In Industrial Robotics Control, 389–419. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8989-1_13.

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Werth, Larry. "Machine Vision." In Standard Handbook of Industrial Automation, 86–109. Boston, MA: Springer US, 1986. http://dx.doi.org/10.1007/978-1-4613-1963-4_6.

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Miller, Richard K. "Fundamentals of Machine Vision." In Industrial Robot Handbook, 47–60. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4684-6608-9_4.

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Batchelor, Bruce, and Frederick Waltz. "Machine vision for industrial applications." In Intelligent Machine Vision, 1–29. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0239-7_1.

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Batchelor, Bruce G. "Machine Vision for Industrial Applications." In Machine Vision Handbook, 1–59. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-84996-169-1_1.

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Loughlin, C. "Application of Machine Vision." In Sensors for Industrial Inspection, 379–404. Dordrecht: Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2730-1_18.

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Wilder, J. "Industrial Applications of Machine Vision." In Issues on Machine Vision, 311–39. Vienna: Springer Vienna, 1989. http://dx.doi.org/10.1007/978-3-7091-2830-5_21.

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Awcock, G. J., and R. Thomas. "Design of Industrial Machine Vision Systems." In Applied Image Processing, 1–24. London: Macmillan Education UK, 1995. http://dx.doi.org/10.1007/978-1-349-13049-8_1.

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Baronti, S., A. Casini, F. Lotti, V. Roberto, and C. Vanello. "Morphological Approach to Industrial Image Inspection of Honeycomb Composite Materials." In Issues on Machine Vision, 25–39. Vienna: Springer Vienna, 1989. http://dx.doi.org/10.1007/978-3-7091-2830-5_3.

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Dhaya, R., R. Kanthavel, and Harun Bangali. "Perspectives on Deep Learning Techniques for Industrial IoT." In Machine Vision for Industry 4.0, 79–100. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003122401-4.

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Conference papers on the topic "Industrial machine vision"

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Holland, S. W., and R. B. Tilove. "Current Industrial Applications of Machine Vision and Future Challenges." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.wa2.

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Although research in machine vision began in the late 1960’s, industrial applications have only recently emerged. Among these, however, are some extremely impressive technical achievements. Most forecasts regarding future growth of commercial machine vision are highly optimistic. In this paper, we discuss some of the critical technologies that have contributed to successful machine vision implementation.
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Bolles, Robert C. "Range Sensors and Their Use in Industrial Automation." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.wb1.

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Wieland, Alexis P. "Design of a Knowledge Based Computer Vision System for Industrial Applications." In Machine Vision. Washington, D.C.: Optica Publishing Group, 1985. http://dx.doi.org/10.1364/mv.1985.fd1.

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Wakizako, Hitoshi. "Industrial Robots and Machine Vision." In International Conference on Industrial Application Engineering 2015. The Institute of Industrial Applications Engineers, 2015. http://dx.doi.org/10.12792/iciae2015.003.

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Fryer, R. J., and J. Miller. "Imaging Polarimetry for Industrial Inspection." In British Machine Vision Conference 1991. Springer-Verlag London Limited, 1991. http://dx.doi.org/10.5244/c.5.47.

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Wittels, Norman, and Stanley H. Zisk. "Lighting Design for Industrial Machine Vision." In Cambridge Symposium_Intelligent Robotics Systems, edited by Donald J. Svetkoff. SPIE, 1987. http://dx.doi.org/10.1117/12.937823.

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Kuts, Vladimir, Tauno Otto, Toivo Tähemaa, Khuldoon Bukhari, and Tengiz Pataraia. "Adaptive Industrial Robots Using Machine Vision." In ASME 2018 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/imece2018-86720.

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The use of industrial robots in modern manufacturing scenarios is a rising trend in the engineering industry. Currently, industrial robots are able to perform pre-programmed tasks very efficiently irrespective of time and complexity. However, often robots encounter unknown scenarios and to solve those, they need to cooperate with humans, leading to unnecessary downtime of the machine and the need for human intervention. The main aim of this study is to propose a method to develop adaptive industrial robots using Machine Learning (ML)/Machine Vision (MV) tools. The proposed method aims to reduce the effort of re-programming and enable self-learning in industrial robots. The elaborated online programming method can lead to fully automated industrial robotic cells in accordance with the human-robot collaboration standard and provide multiple usage options of this approach in the manufacturing industry. Machine Vision (MV) tools used for online programming allow industrial robots to make autonomous decisions during sorting or assembling operations based on the color and/or shape of the test object. The test setup consisted of an industrial robot cell, cameras and LIDAR connected to MATLAB through a Robot Operation System (ROS). The online programming tests and simulations were performed using Virtual/Augmented Reality (VR/AR) toolkits together with a Digital Twin (DT) concept, to test the industrial robot program on a digital object before executing it on the real object, thus creating a safe and secure test environment.
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Lacey, A. J., N. A. Thacker, and R. B. Yates. "Surface Approximation from Industrial SEM Images." In British Machine Vision Conference 1996. British Machine Vision Association, 1996. http://dx.doi.org/10.5244/c.10.28.

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Khanipov, Timur, Ivan Koptelov, Anton Grigoryev, Elena Kuznetsova, and Dmitry Nikolaev. "Vision-based industrial automatic vehicle classifier." In Seventh International Conference on Machine Vision (ICMV 2014), edited by Antanas Verikas, Branislav Vuksanovic, Petia Radeva, and Jianhong Zhou. SPIE, 2015. http://dx.doi.org/10.1117/12.2181557.

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Jeyamkondan, S., N. Ray, Glenn A. Kranzler, and Nisha Biju. "Beef quality grading using machine vision." In Environmental and Industrial Sensing, edited by James A. DeShazer and George E. Meyer. SPIE, 2000. http://dx.doi.org/10.1117/12.411743.

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