Abstract
Computer vision is a complex field which can be challenging for those outside the research community to apply in the real world. To address this we present a novel formulation for the abstraction of computer vision problems above algorithms, as part of our OpenVL framework. We have created a set of fundamental operations which form a basis from which we can build up descriptions of computer vision methods. We use these operations to conceptually define the problem, which we can then map into algorithm space to choose an appropriate method to solve the problem. We provide details on three of our operations, Match, Detect and Solve, and subsequently demonstrate the flexibility of description these three offer us. We describe various vision problems such as image registration and tracking through the sequencing of our operations and discuss how these may be extended to cover a larger range of tasks, which in turn may be used analogously to a graphics shader language.
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Afrah, A., Miller, G., Fels, S.: Vision system development through separation of management and processing. In: Workshop on Multimedia Information Processing and Retrieval. IEEE, Los Alamitos (2009)
Afrah, A., Miller, G., Parks, D., Finke, M., Fels, S.: Hive a distributed system for vision processing. In: Proc. 2nd International Conference on Distributed Smart Cameras (September 2008)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer Vision with the OpenCV Library, 1st edn. O’Reilly Media, Inc., Sebastopol (2008)
Brown, M., Lowe, D.G.: Recognising panoramas. In: Proceedings of the Ninth IEEE International Conference on Computer Vision, October 16, vol. 2, pp. 1218–1225 (2003)
Camellia, http://camellia.sourceforge.net/
Clouard, R., Elmoataz, A., Porquet, C., Revenu, M.: Borg: A knowledge-based system for automatic generation of image processing programs. IEEE Trans. Pattern Anal. Mach. Intell. 21, 128–144 (1999)
Day, J.D., Zimmermann, H.: The OSI reference model. Proceedings of the IEEE 71, 1334–1340 (1983)
Firschein, O., Strat, T.M.: Radius: Image Understanding For Imagery Intelligence. Morgan Kaufmann, San Francisco (1997)
Fitzgibbon, A.W.: Stochastic rigidity: Image registration for nowhere-static scenes. In: IEEE International Conference on Computer Vision, vol. 1, p. 662 (2001)
Gupta, S., Gupta, E.N., Prince, J.L.: Stochastic formulations of optical flow algorithms under variable brightness conditions. In: Proceedings of IEEE International Conference on Image Processing, vol. III, pp. 484–487 (1995)
Horn, B.K., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)
Kohl, C., Mundy, J.: The development of the image understanding environment. In: in Proceedings 1994 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 443–447. IEEE Computer Society Press, Los Alamitos (1994)
Konstantinides, K., Rasure, J.R.: The khoros software development environment for image and signal processing. IEEE Transactions on Image Processing 3, 243–252 (1994)
Matsuyama, T., Hwang, V.: Sigma: a framework for image understanding integration of bottom-up and top-down analyses. In: Proceedings of the 9th International Joint Conference on Artificial Intelligence, vol. 2, pp. 908–915. Morgan Kaufmann, San Francisco (1985)
Miller, G., Fels, S.: Uniform access to the cameraverse. In: International Conference on Distributed Smart Cameras. IEEE, Los Alamitos (2010)
Miller, G., Fels, S., Oldridge, S.: A conceptual structure for computer vision. In: Conference on Computer and Robot Vision (May 2011)
Miller, G., Oldridge, S., Fels, S.: Towards a computer vision shader language. In: Proceedings of International Conference on Computer Graphics and Interactive Techniques, Poster Session, SIGGRAPH 2011. ACM, New York (2011)
Mundy, J.: The image understanding environment program. IEEE Expert: Intelligent Systems and Their Applications 10(6), 64–73 (1995)
Neider, J., Davis, T.: OpenGL Programming Guide: The Official Guide to Learning OpenGL, Release 1, 1st edn. Addison-Wesley Longman Publishing Co., Inc., Boston (1993)
Oldridge, S., Miller, G., Fels, S.: Mapping the problem space of image registration. In: Conference on Computer and Robot Vision (May 2011)
Panin, G.: Model-based Visual Tracking: the OpenTL Framework, 1st edn. John Wiley and Sons, Chichester (2011)
Peterson, J., Hudak, P., Reid, A., Hager, G.: Fvision: A declarative language for visual tracking (2001)
Pope, A.R., Lowe, D.G.: Vista: A software environment for computer vision research (1994)
Quartz Composer by Apple, http://developer.apple.com/graphicsimaging/quartz/quartzcomposer.html
ShapeLogic, http://www.shapelogic.org
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, p. 511 (2001)
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Miller, G., Oldridge, S., Fels, S. (2011). Towards a General Abstraction through Sequences of Conceptual Operations. In: Crowley, J.L., Draper, B.A., Thonnat, M. (eds) Computer Vision Systems. ICVS 2011. Lecture Notes in Computer Science, vol 6962. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23968-7_19
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DOI: https://doi.org/10.1007/978-3-642-23968-7_19
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