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Relevant bibliographies by topics / Computer vision; Active

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Academic literature on the topic 'Computer vision; Active'

Author: Grafiati

Published: 4 June 2021

Last updated: 15 February 2022

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Journal articles on the topic "Computer vision; Active"

1

Aloimonos, John, Isaac Weiss, and Amit Bandyopadhyay. "Active vision." International Journal of Computer Vision 1, no. 4 (January 1988): 333–56. http://dx.doi.org/10.1007/bf00133571.

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McGarrity, C., and R. L. Dalglish. "An autonomous computer peripheral for active 3D vision." Measurement Science and Technology 7, no. 11 (November 1, 1996): 1591–604. http://dx.doi.org/10.1088/0957-0233/7/11/008.

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Sipe, M. A., and D. Casasent. "Feature space trajectory methods for active computer vision." IEEE Transactions on Pattern Analysis and Machine Intelligence 24, no. 12 (December 2002): 1634–43. http://dx.doi.org/10.1109/tpami.2002.1114854.

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de Croon, G. C. H. E., I. G. Sprinkhuizen-Kuyper, and E. O. Postma. "Comparing active vision models." Image and Vision Computing 27, no. 4 (March 2009): 374–84. http://dx.doi.org/10.1016/j.imavis.2008.06.004.

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PINHANEZ, CLAUDIO S. "BEHAVIOR-BASED ACTIVE VISION." International Journal of Pattern Recognition and Artificial Intelligence 08, no. 06 (December 1994): 1493–526. http://dx.doi.org/10.1142/s0218001494000723.

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A vision system was built using a behavior-based model, the subsumption architecture. The so-called active eye moves the camera’s axis through the environment, detecting areas with high concentration of edges, with the help of a kind of saccadic movement. The design and implementation process is detailed in the article, paying particular attention to the fovea-like sensor structure which enables the active eye to efficiently use local information to control its movements. Numerical measures for the eye’s behavior were developed, and applied to evaluate the incremental building process and the effects of the saccadic movements on the whole system. A higher level behavior was also implemented, with the purpose of detecting long straight edges in the image, producing pictures similar to hand drawings. Robustness and efficiency problems are addressed at the end of the paper. The results seem to prove that interesting behaviors can be achieved using simple vision methods and algorithms, if their results are properly interconnected and timed.

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Pahlavan, Kourosh, and Jan-Olof Eklundh. "Mechatronics of active vision." Mechatronics 4, no. 2 (March 1994): 113–23. http://dx.doi.org/10.1016/0957-4158(94)90038-8.

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Sipe, M. A., and D. Casasent. "Global feature space neural network for active computer vision." Neural Computing & Applications 7, no. 3 (September 1998): 195–215. http://dx.doi.org/10.1007/bf01414882.

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Tistarelli, M., and E. Grosso. "Active vision-based face authentication." Image and Vision Computing 18, no. 4 (March 2000): 299–314. http://dx.doi.org/10.1016/s0262-8856(99)00059-1.

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Allen, M. J., F. J. Marin, F. García-Lagos, N. E. Gough, and Q. Mehdi. "Fuzzy processing for active vision." Integrated Computer-Aided Engineering 10, no. 3 (June 27, 2003): 267–85. http://dx.doi.org/10.3233/ica-2003-10304.

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Serebrennyi, Vladimir, Andrei Boshliakov, and Georgii Ovsiankin. "Active stabilization in robotic vision systems." MATEC Web of Conferences 161 (2018): 03019. http://dx.doi.org/10.1051/matecconf/201816103019.

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In the article considered prospective approaches to the design of active systems for stabilizing systems based on the parallel kinematics mechanism and possible applications of such systems. Attention is drawn to the fact that not only object fluctuations are an important object for stabilization, but it is also important to compensate for the body vibrations, along with its vibrations. Based on the analysis, it was concluded that it is perspective to use mechanisms with parallel kinematics for the design of active stabilization systems. Was obtained a mathematical model of the hexapod, according to which a computer model in the Simulink package was designed. Its analysis confirmed the possibility of using a mechanism with parallel kinematics in designing an active stabilization system and presented requirements to the actuators of the system.

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Dissertations / Theses on the topic "Computer vision; Active"

1

Tordoff, Ben. "Active control of zoom for computer vision." Thesis, University of Oxford, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.270752.

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Luckman, Adrian John. "Active perception in machine vision." Thesis, University of York, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.280521.

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Li, Fuxing. "Active stereo for AGV navigation." Thesis, University of Oxford, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.338984.

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Du, Fenglei. "The fundamentals of an active vision system." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239358.

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Onder, Murat. "Face Detection And Active Robot Vision." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605290/index.pdf.

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The main task in this thesis is to design a robot vision system with face detection and tracking capability. Hence there are two main works in the thesis: Firstly, the detection of the face on an image that is taken from the camera on the robot must be achieved. Hence this is a serious real time image processing task and time constraints are very important because of this reason. A processing rate of 1 frame/second is tried to be achieved and hence a fast face detection algorithm had to be used. The Eigenface method and the Subspace LDA (Linear Discriminant Analysis) method are implemented, tested and compared for face detection and Eigenface method proposed by Turk and Pentland is decided to be used. The images are first passed through a number of preprocessing algorithms to obtain better performance, like skin detection, histogram equalization etc. After this filtering process the face candidate regions are put through the face detection algorithm to understand whether there is a face or not in the image. Some modifications are applied to the eigenface algorithm to detect the faces better and faster. Secondly, the robot must move towards the face in the image. This task includes robot motion. The robot to be used for this purpose is a Pioneer 2-DX8 Plus, which is a product of ActivMedia Robotics Inc. and only the interfaces to move the robot have been implemented in the thesis software. The robot is to detect the faces at different distances and arrange its position according to the distance of the human to the robot. Hence a scaling mechanism must be used either in the training images, or in the input image taken from the camera. Because of timing constraint and low camera resolution, a limited number of scaling is applied in the face detection process. With this reason faces of people who are very far or very close to the robot will not be detected. A background independent face detection system is tried to be designed. However the resultant algorithm is slightly dependent to the background. There is no any other constraints in the system.

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Benameur, Kaouthar. "Control strategies for an active vision system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0003/NQ44363.pdf.

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Bradshaw, Kevin J. "Surveillance of dynamic scenes with an active vision system." Thesis, University of Oxford, 1994. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.260139.

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Hoad, Paul. "Active robot vision and its use in object recognition." Thesis, University of Surrey, 1994. http://epubs.surrey.ac.uk/844223/.

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Object recognition has been one of the main areas of research into computer vision in the last 20-30 years. Until recently most of this research has been performed on scenes taken using static monocular, binocular or even trinocular cameras. It is believed, however, that by adding the ability to move the look point and concentrate on a region of interest a more robust and efficient method of vision can be achieved. Recent studies into the ability to provide human-like vision systems for a more active approach to vision have lead to the development of a number of robot controlled vision systems. In this thesis the development of one such system at the University of Surrey, the stereo robot head "Getafix" is described. The design, construction and development of the head and its control system have been undertaken as part of this project with the aim of improving current vision tasks, in particular, that of object recognition. In this thesis the design of the control systems, kinematics and control software of the stereo robot head will be discussed. A number of simple commissioning experiments are also shown, using the concepts of the robot control developed herein. Camera lens control and calibration is also described. A review of classical primitive based object recognition systems is given and the development of a novel generic cylindrical object recognition strategy is shown. The use of this knowledge source is demonstrated with other vision processes of colour and stereo. The work on the cylinder recognition strategy and the stereo robot head are finally combined within an active vision framework. A purposive active vision strategy is used to detect cylindrical structures, that would otherwise be undetectable by the cylindrical object detection algorithm alone.

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Alvino, Christopher Vincent. "Multiscale Active Contour Methods in Computer Vision with Applications in Tomography." Diss., Georgia Institute of Technology, 2005. http://hdl.handle.net/1853/6896.

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Most applications in computer vision suffer from two major difficulties. The first is they are notoriously ridden with sub-optimal local minima. The second is that they typically require high computational cost to be solved robustly. The reason for these two drawbacks is that most problems in computer vision, even when well-defined, typically require finding a solution in a very large high-dimensional space. It is for these two reasons that multiscale methods are particularly well-suited to problems in computer vision. Multiscale methods, by way of looking at the coarse scale nature of a problem before considering the fine scale nature, often have the ability to avoid sub-optimal local minima and obtain a more globally optimal solution. In addition, multiscale methods typically enjoy reduced computational cost. This thesis applies novel multiscale active contour methods to several problems in computer vision, especially in simultaneous segmentation and reconstruction of tomography images. In addition, novel multiscale methods are applied to contour registration using minimal surfaces and to the computation of non-linear rotationally invariant optical flow. Finally, a methodology for fast robust image segmentation is presented that relies on a lower dimensional image basis derived from an image scale space. The specific advantages of using multiscale methods in each of these problems is highlighted in the various simulations throughout the thesis, particularly their ability to avoid sub-optimal local minima and their ability to solve the problems at a lower overall computational cost.

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Antonis, Jan. "Development of an active computer vision system for 3 dimensional modelling." Thesis, Queen's University Belfast, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.301753.

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Books on the topic "Computer vision; Active"

1

Sood, A. K. Active Perception and Robot Vision. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992.

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Active computer vision by cooperative focus and stereo. New York: Springer-Verlag, 1989.

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Krotkov, Eric Paul. Active Computer Vision by Cooperative Focus and Stereo. New York, NY: Springer New York, 1989.

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Krotkov, Eric Paul. Active Computer Vision by Cooperative Focus and Stereo. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9663-5.

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Ikeuchi, Katsushi, Yasuyuki Matsushita, Ryusuke Sagawa, Hiroshi Kawasaki, Yasuhiro Mukaigawa, Ryo Furukawa, and Daisuke Miyazaki. Active Lighting and Its Application for Computer Vision. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-56577-0.

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Tsotsos, John Konstantine. Active vs. passive visual search: Which is more efficient? Toronto: University of Toronto Dept. of Computer Science, 1990.

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A few steps towards 3D active vision. Berlin: Springer, 1997.

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8

Zhang, Beiwei. Automatic Calibration and Reconstruction for Active Vision Systems. Dordrecht: Springer Netherlands, 2012.

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9

Borotschnig, Hermann. Uncertain information fusion in active object recognition. Edited by Pinz Axel. [Wien]: Österreichische Computer Gesellschaft, 1999.

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10

Wilkes, David. Active object recognition. Toronto: University of Toronto, 1994.

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Book chapters on the topic "Computer vision; Active"

1

Tsotsos, John K. "Active Recognition." In Computer Vision, 1–9. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_866-1.

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Shen, Rui, Gaopeng Gou, Irene Cheng, and Anup Basu. "Active Calibration." In Computer Vision, 5. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_283.

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Hogue, Andrew, and Michael Jenkin. "Active Stereo Vision." In Computer Vision, 1–6. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_282-1.

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Hogue, Andrew, and Michael R. M. Jenkin. "Active Stereo Vision." In Computer Vision, 8–12. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_282.

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Faraji, Mehdi, and Anup Basu. "Simplified Active Calibration." In Computer Vision, 1–4. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_881-1.

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Kawashima, Hiroaki. "Active Appearance Models." In Computer Vision, 1–5. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-03243-2_800-1.

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Clark, James J. "Active Sensor (Eye) Movement Control." In Computer Vision, 1–5. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-03243-2_278-1.

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Clark, James J. "Active Sensor (Eye) Movement Control." In Computer Vision, 5–8. Boston, MA: Springer US, 2014. http://dx.doi.org/10.1007/978-0-387-31439-6_278.

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Wang, Ce, Zhanyi Hu, and Song De Ma. "Active vision based stereo vision." In Recent Developments in Computer Vision, 229–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-60793-5_78.

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Eklundh, Jan-Olof. "Trends in active vision." In Computer Science Today, 505–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/bfb0015263.

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Conference papers on the topic "Computer vision; Active"

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Ammirato, Phil, Alexander C. Berg, and Jana Kosecka. "Active Vision Dataset Benchmark." In 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2018. http://dx.doi.org/10.1109/cvprw.2018.00277.

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Jenkin and Tsotsos. "Active stereo vision and cyclotorsion." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE Comput. Soc. Press, 1994. http://dx.doi.org/10.1109/cvpr.1994.323903.

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Institute, Jozef Stefan, and Ales Ude. "Active humanoid vision and object classification." In 2009 24th International Symposium on Computer and Information Sciences (ISCIS). IEEE, 2009. http://dx.doi.org/10.1109/iscis.2009.5291811.

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Rotstein, H. P., and E. Rivlin. "Optimal servoing for active foveated vision." In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 1996. http://dx.doi.org/10.1109/cvpr.1996.517071.

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Aydin, Ilhan, Ebru Karakose, Mehmet Karakose, M. Tunay Gencoglu, and Erhan Akin. "A new computer vision approach for active pantograph control." In 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA). IEEE, 2013. http://dx.doi.org/10.1109/inista.2013.6577665.

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Davison, A. J. "Active search for real-time vision." In Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1. IEEE, 2005. http://dx.doi.org/10.1109/iccv.2005.29.

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Bandla, Sunil, and Kristen Grauman. "Active Learning of an Action Detector from Untrimmed Videos." In 2013 IEEE International Conference on Computer Vision (ICCV). IEEE, 2013. http://dx.doi.org/10.1109/iccv.2013.230.

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Mishra, Akshaya, Paul W. Fieguth, and David A. Clausi. "From active contours to active surfaces." In 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2011. http://dx.doi.org/10.1109/cvpr.2011.5995612.

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"ACTIVE OBJECT DETECTION." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0002044600970103.

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Xiaodong Yu, Cornelia Fermuller, Ching Lik Teo, Yezhou Yang, and Yiannis Aloimonos. "Active scene recognition with vision and language." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126320.

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