By Katsushi Ikeuchi
This accomplished reference offers quick access to correct info on all facets of machine imaginative and prescient. An A-Z layout of over 240 entries deals a various variety of themes for these looking access into any element in the large box of laptop imaginative and prescient. Over 2 hundred Authors from either and academia contributed to this volume.
Each access comprises synonyms, a definition and dialogue of the subject, and a strong bibliography. wide cross-references to different entries help effective, uncomplicated searches for fast entry to proper details. Entries have been peer-reviewed via a uncommon overseas advisory board, either scientifically and geographically diversified, making sure balanced assurance. Over 3700 bibliographic references for additional analyzing let deeper exploration into any of the themes covered.
The content material of Computer imaginative and prescient: A Reference Guide is expository and instructional, making the publication a pragmatic source for college students who're contemplating coming into the sphere, in addition to execs in different fields who have to entry this important details yet won't have the time to paintings their means via a whole textual content on their subject of interest.
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Extra info for Computer Vision: A Reference Guide
Collins RT, Tsin Y (1999) Calibration of an outdoor active camera system. In: CVPR’99: proceedings of the 1999 IEEE computer society conference on computer vision and pattern recognition (CVPR), Ft. Collins, pp 528–534 9. Davis J, Chen X (2003) Calibrating pan-tilt cameras in widearea surveillance networks. In: ICCV’03: proceedings of the 9th IEEE international conference on computer vision, Nice, pp 144–149 10. Horaud R, Mohr R, Lorecki B (1992) Linear camera calibration. In: ICRA’92: proceedings of the IEEE international conference on robotics and automation, Nice, vol 2, pp 1539–1544 11.
Strategies C and D were also tested on a real camera in an indoor environment. The estimates for ıx and ıy obtained by strategy C (90ı roll) were 3 and 29 pixels, while the values obtained by strategy D were 2 and 30 pixels, which demonstrates the stability of the active calibration algorithms in real situations. The estimated values of fx and fy were 908 and 1,126, respectively. References Active Calibration, Table 2 Results of focal length estimation Strategy Strategy C Strategy D Ground truth fx fy 400 600 400 600 D0 fx fy 403 602 401 601 D5 fx fy 396 603 403 599 extrinsic parameters.
Snoek University of Amsterdam, Amsterdam, The Netherlands Intelligent Systems Lab Amsterdam, Informatics Institute University of Amsterdam, Amsterdam, The Netherlands Gunnar Sparr Centre for Mathematical Sciences, Lund University, Lund, Sweden Anuj Srivastava Florida State University, Tallahassee, FL, USA Contributors Contributors xxxiii Gaurav Srivastava School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN, USA Peer Stelldinger Computer Science Department, University of Hamburg, Hamburg, Germany Peter Sturm INRIA Grenoble Rhône-Alpes, St Ismier Cedex, France Kokichi Sugihara Graduate School of Advanced Mathematical Sciences, Meiji University, Kawasaki, Kanagawa, Japan Min Sun Department of Electrical and Computer Engineering, University of Michigan, Ann Arbor, MI, USA Richard Szeliski Microsoft Research, One Microsoft Way, Redmond, WA, USA Yu-Wing Tai Department of Computer Science, Korean Advanced Institute of Science and Technology (KAIST), Yuseong-gu, Daejeon, South Korea Tomokazu Takahashi Faculty of Economics and Information, Gifu Shotoku Gakuen University, Gifu, Japan Birgi Tamersoy Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX, USA Ping Tan Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore Robby T.
Computer Vision: A Reference Guide by Katsushi Ikeuchi