Relevant bibliographies by topics / Vision model

Contents

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

Academic literature on the topic 'Vision model'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 February 2022

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Journal articles on the topic "Vision model"

1

Pece, Arthur E. C. "Generative model based vision." Image and Vision Computing 21, no. 1 (January 2003): 1–3. http://dx.doi.org/10.1016/s0262-8856(02)00124-5.

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Pece, Arthur E. C., and Rasmus Larsen. "Generative model based vision." Computer Vision and Image Understanding 106, no. 1 (April 2007): 3–4. http://dx.doi.org/10.1016/j.cviu.2006.10.006.

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Naisberg, Y. "Biophysical vision model and learning paradigms about vision: review." Medical Hypotheses 57, no. 4 (October 2001): 409–18. http://dx.doi.org/10.1054/mehy.2001.1290.

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Kantabutra, Sooksan, and Gayle C. Avery. "Proposed Model for Investigating Relationships between Vision Components and Business Unit Performance." Journal of Management & Organization 8, no. 2 (2002): 22–39. http://dx.doi.org/10.1017/s1833367200005009.

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ABSTRACTLeaders are widely exhorted to employ visions, yet surprisingly little research has been conducted on what constitutes an “effective” vision. A research model is proposed for investigating relationships between vision components, and business unit performance as measured by employee and customer satisfaction. The model, expressed both graphically and as three propositions, proposes that vision attributes of brevity, clarity, abstractness, challenge, future orientation, stability, and desirability, plus vision content relating to employee and customer satisfaction, can directly affect p
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Kantabutra, Sooksan, and Gayle C. Avery. "Proposed Model for Investigating Relationships between Vision Components and Business Unit Performance." Journal of the Australian and New Zealand Academy of Management 8, no. 2 (2002): 22–39. http://dx.doi.org/10.5172/jmo.2002.8.2.22.

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ABSTRACTLeaders are widely exhorted to employ visions, yet surprisingly little research has been conducted on what constitutes an “effective” vision. A research model is proposed for investigating relationships between vision components, and business unit performance as measured by employee and customer satisfaction. The model, expressed both graphically and as three propositions, proposes that vision attributes of brevity, clarity, abstractness, challenge, future orientation, stability, and desirability, plus vision content relating to employee and customer satisfaction, can directly affect p
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Higuchi, Kazutoshi, Mitsuru Kaise, Hiroto Noda, Go Ikeda, Teppei Akimoto, Hiroshi Yamawaki, Osamu Goto, Nobue Ueki, Seiji Futagami, and Katsuhiko Iwakiri. "Usefulness of 3-Dimensional Flexible Endoscopy in Esophageal Endoscopic Submucosal Dissection in an Ex Vivo Animal Model." Gastroenterology Research and Practice 2019 (November 3, 2019): 1–5. http://dx.doi.org/10.1155/2019/4051956.

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Background and Aims. Three-dimensional (3D) rigid endoscopy has been clinically introduced in surgical fields to enable safer and more accurate procedures. To explore the feasibility of 3D flexible endoscopy, we conducted a study comparing 2-dimensional (2D) and 3D visions for the performance of esophageal endoscopic submucosal dissection (ESD). Methods. Six endoscopists (3 experts and 3 trainees) performed ESD of target lesions in isolated porcine esophagus using a prototype 3D flexible endoscope under 2D or 3D vision. Study endpoints were procedure time, speed of mucosal incision and submuco
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HASEGAWA, Tsutomu. "Model-based Vision Robotic Manipulation." Journal of the Robotics Society of Japan 10, no. 2 (1992): 153–58. http://dx.doi.org/10.7210/jrsj.10.153.

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Lockwood, P., S. Lu, and C. Martin. "A Model for Binocular Vision." IFAC Proceedings Volumes 28, no. 14 (June 1995): 71–74. http://dx.doi.org/10.1016/s1474-6670(17)46808-x.

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Shi, Jin Fang, Zhen Wei Su, and Guo Hui Li. "Computational Model for Machine Vision Inspection Based on Vision Attention." Advanced Materials Research 383-390 (November 2011): 2398–403. http://dx.doi.org/10.4028/www.scientific.net/amr.383-390.2398.

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Human vision system exploits this fact by visual selective attention mechanisms towards important and informative regions. A computational model of combination both bottom-up and top-down simulating human vision system for machine vision inspection is proposed. In this model, top-down knowledge-based information is highlighted to integrate into bottom-up stimulus-based process of vision attention. The model is tested on inspecting contaminants in cotton images. Experiment result shows that the proposed model is feasible and effective in visual inspection. And it is available and quasi-equivale
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Oh, Il Kweon, Seong Won Yeom, and Dong Weon Lee. "Modal Reduced Order Model for Vision Sensing of IPMC Actuator." Key Engineering Materials 326-328 (December 2006): 1523–26. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.1523.

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In order to control the IPMC (Ionic Polymer Metal Composite) actuators, it is necessary to use a vision sensing system and a reduced order model from the vision sensing data. In this study, the MROVS (Modal Reduced Order Vision Sensing) model using the least square method has been developed for implementation of the biomimetic motion generation. The simulated transverse displacement is approximated with a sum of the lower mode shapes of the cantilever beam. The NIPXI 1409 image acquisition board and CCD camera (XC-HR50) are used in the experimental setup. Present results show that the MROVS mo
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Dissertations / Theses on the topic "Vision model"

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Gomes, Herman M. "Model learning in iconic vision." Thesis, University of Edinburgh, 2002. http://hdl.handle.net/1842/323.

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Generally, object recognition research falls into three main categories: (a) geometric, symbolic or structure based recognition, which is usually associated with CAD-based vision and 3-D object recognition; (b) property, vector or feature based recognition, involving techniques that vary from specific feature vectors, multiple filtering to global descriptors for shape, texture and colour; and (c) iconic or image based recognition, which either complies with the traditional sensor architecture of an uniform array of sampling units, or uses alternative representations. An example is the log-pola
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Dickinson, John William. "Image structure and model-based vision." Thesis, University of Oxford, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.292532.

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Brown, Gary. "An object oriented model of machine vision." Thesis, Kingston University, 1997. http://eprints.kingston.ac.uk/20614/.

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In this thesis an object oriented model is proposed that satisfies the requirements for a generic, customisable, reusable and flexible machine vision framework. These requirements are identified as being: ease of customisation for a particular application domain; independence from image definition; independence from shape representation scheme; ability to add new domain specific shape descriptors; independence from implemented machine vision algorithms; and the ability to maximise reuse of the generic framework. The thesis begins with a review of key machine vision functions and traditional ar
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Vasilaki, Eleni. "A biologically inspired dynamic model for vision." Thesis, University of Sussex, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.288864.

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Broun, A. "Autonomous model building using vision and manipulation." Thesis, University of the West of England, Bristol, 2016. http://eprints.uwe.ac.uk/25851/.

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It is often the case that robotic systems require models, in order to successfully control themselves, and to interact with the world. Models take many forms and include kinematic models to plan motions, dynamics models to understand the interaction of forces, and models of 3D geometry to check for collisions, to name but a few. Traditionally, models are provided to the robotic system by the designers that build the system. However, for long-term autonomy it becomes important for the robot to be able to build and maintain models of itself, and of objects it might encounter. In this thesis, the
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Hnatow, Justin Michael. "A theoretical eye model for uncalibrated real-time eye gaze estimation /." Online version of thesis, 2006. http://hdl.handle.net/1850/2606.

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Shao, Yuan. "A Bayesian reasoning framework for model-driven vision." Thesis, University of Sheffield, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.284789.

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Du, Li. "The viewpoint consistency constraint in model-based vision." Thesis, University of Reading, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317162.

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Becker, Shawn C. (Shawn Carter). "Vision-assisted modeling for model-based video representations." Thesis, Massachusetts Institute of Technology, 1997. http://hdl.handle.net/1721.1/29123.

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Li, Min. "Novel frameworks for deformable model and nonrigid motion analysis." Access to citation, abstract and download form provided by ProQuest Information and Learning Company; downloadable PDF file 2.87Mb, 145 p, 2005. http://wwwlib.umi.com/dissertations/fullcit/3181869.

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Books on the topic "Vision model"

1

The etiology of vision disorders: A neuroscience model. Santa Ana, CA: Optometric Extension Program, 1990.

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Bab-Hadiashar, Alireza, and David Suter, eds. Data Segmentation and Model Selection for Computer Vision. New York, NY: Springer New York, 2000. http://dx.doi.org/10.1007/978-0-387-21528-0.

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Lepetit, Vincent. Monocular model-based 3D tracking of rigid objects. Boston, MA: NOW Publishers, 2005.

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Policing Sri Lanka: A vision beyond the colonial model. Ratmalana: Sarvodaya Vishva Lekha Publishers, 2003.

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CIE Technical Committee TC-8-01. A colour appearance model for colour management systems: CIEAMO2. Vienna, Austria: CIE Central Bureau, 2004.

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Sturm, Jürgen. Approaches to Probabilistic Model Learning for Mobile Manipulation Robots. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013.

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Panin, Giorgio. Model-based visual tracking: The OpenTL framework. Hoboken, N.J: Wiley, 2011.

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Cavadini, Marco. Concept and model of a multiprocessor system for high resolution image correlation. Konstanz: Hartung-Gorre, 1999.

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Yu, Faxin. Three-dimensional model analysis and processing. Hangzhuou: Zhejiang University Press, 2010.

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Verghese, Gilbert. Perspective alignment back-projection for real-time monocular three-dimensional model-based computer vision. Toronto: Dept. of Computer Science, University of Toronto, 1995.

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Book chapters on the topic "Vision model"

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Zhang, Zhengyou. "Camera Model." In Computer Vision, 77–80. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_165.

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Tominaga, Shoji. "Color Model." In Computer Vision, 1–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_449-1.

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Tominaga, Shoji. "Color Model." In Computer Vision, 116–20. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_449.

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Sturm, Peter. "Pinhole Camera Model." In Computer Vision, 610–13. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_472.

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Tan, Ping. "Phong Reflectance Model." In Computer Vision, 1–3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_536-1.

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Ghosh, Abhijeet. "Cook-Torrance Model." In Computer Vision, 146–52. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_531.

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Tominaga, Shoji. "Dichromatic Reflection Model." In Computer Vision, 191–93. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_532.

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Tan, Ping. "Phong Reflectance Model." In Computer Vision, 592–94. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_536.

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Tominaga, Shoji. "Dichromatic Reflection Model." In Computer Vision, 1–3. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03243-2_532-1.

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Tan, Ping. "Oren-Nayar Reflectance Model." In Computer Vision, 1–3. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_535-1.

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Conference papers on the topic "Vision model"

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Benelallam, Amine, Thomas Hartmann, Ludovic Mouline, Francois Fouquet, Johann Bourcier, Olivier Barais, and Yves Le Traon. "Raising Time Awareness in Model-Driven Engineering: Vision Paper." In 2017 ACM/IEEE 20th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2017. http://dx.doi.org/10.1109/models.2017.11.

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Osorio, D., and Peter J. Sobey. "Insect vision as model for machine vision." In Applications in Optical Science and Engineering, edited by David P. Casasent. SPIE, 1992. http://dx.doi.org/10.1117/12.131606.

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Moorhead, Ian R. "Computational color vision model." In Photonics West '98 Electronic Imaging, edited by Bernice E. Rogowitz and Thrasyvoulos N. Pappas. SPIE, 1998. http://dx.doi.org/10.1117/12.320155.

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Moorhead, Ian R. "Spatiochromatic model of vision." In Electronic Imaging: Science & Technology, edited by Bernice E. Rogowitz and Jan P. Allebach. SPIE, 1996. http://dx.doi.org/10.1117/12.238721.

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Podladchikova, Lubov N., Valentina I. Gusakova, Dmitry G. Shaposhnikov, Alain Faure, Alexander V. Golovan, and Natalia A. Shevtsova. "MARR: active vision model." In Intelligent Systems & Advanced Manufacturing, edited by David P. Casasent. SPIE, 1997. http://dx.doi.org/10.1117/12.290313.

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Nazaruks, Vladislavs, and Jānis Osis. "Vision of the TFM-driven Code Acquisition." In Special Session on Model-Driven Innovations for Software Engineering. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007843306170624.

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Teng, Chungte, and Panos A. Ligomenides. "ANN-implemented robust vision model." In Boston - DL tentative, edited by David P. Casasent. SPIE, 1991. http://dx.doi.org/10.1117/12.25200.

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Rogers, M., and J. Graham. "Structured Point Distribution Models: Model Intermittently Present Features." In British Machine Vision Conference 2001. British Machine Vision Association, 2001. http://dx.doi.org/10.5244/c.15.5.

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Abouelaziz, Ilyass, and Mohammed El Hassouni. "New models of visual saliency: Contourlet transform based model and hybrid model." In 2015 Intelligent Systems and Computer Vision (ISCV). IEEE, 2015. http://dx.doi.org/10.1109/isacv.2015.7105547.

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Huang, Haoshuo, Vihan Jain, Harsh Mehta, Jason Baldridge, and Eugene Ie. "Multi-modal Discriminative Model for Vision-and-Language Navigation." In Proceedings of the Combined Workshop on Spatial Language Understanding (. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-1605.

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Reports on the topic "Vision model"

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Jacob J. Jacobson, Robert F. Jeffers, Gretchen E. Matthern, Steven J. Piet, Benjamin A. Baker, and Joseph Grimm. VISION User Guide - VISION (Verifiable Fuel Cycle Simulation) Model. Office of Scientific and Technical Information (OSTI), August 2009. http://dx.doi.org/10.2172/968564.

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Lindquist, Goerge H., J. Richard Freeling, and Allyn W. Dunstan. Computational Vision Model (CVM) Research and Development. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada361237.

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Chang, Huey, Katsushi Ikeuchi, and Takeo Kanade. Model-Based Vision System by Object-Oriented Programming. Fort Belvoir, VA: Defense Technical Information Center, February 1988. http://dx.doi.org/10.21236/ada195819.

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D. E. Shropshire and W. H. West. Software Requirements Specification Verifiable Fuel Cycle Simulation (VISION) Model. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/910990.

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J. J. Jacobson, D. E. Shropshire, and W. B. West. Software Platform Evaluation - Verifiable Fuel Cycle Simulation (VISION) Model. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/911264.

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D. E. Shropshire and W. H. West. Software Requirements Specification Verifiable Fuel Cycle Simulation (VISION) Model. Office of Scientific and Technical Information (OSTI), November 2005. http://dx.doi.org/10.2172/911281.

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Antonio, Joseph C., and William E. Berkley. Night Vision Goggle Model F4949 Preflight Adjustment/Assessment Procedures. Fort Belvoir, VA: Defense Technical Information Center, August 1993. http://dx.doi.org/10.21236/ada271079.

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Kaplan, Robert, and Phillip B. Hanselman. Suitability of Ada for Real-Time Model Based Vision Applications. Fort Belvoir, VA: Defense Technical Information Center, November 1991. http://dx.doi.org/10.21236/ada245710.

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Jacob J. Jacobson, Robert F. Jeffers, Gretchen E. Matthern, Steven J. Piet, and Wendell D. Hintze. User Guide for VISION 3.4.7 (Verifiable Fuel Cycle Simulation) Model. Office of Scientific and Technical Information (OSTI), July 2011. http://dx.doi.org/10.2172/1027943.

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Rhodes, Anthony. Leveraging Model Flexibility and Deep Structure: Non-Parametric and Deep Models for Computer Vision Processes with Applications to Deep Model Compression. Portland State University Library, May 2020. http://dx.doi.org/10.15760/etd.7320.

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