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Conforming identification of the fundamental matrix in image matching problem
Fursov V.A., Gavrilov A.V., Goshin Ye.V., Pugachev K.G.

Samara National Research University, Samara, Russia,
Image Processing Systems Institute of RAS – Branch of the FSRC “Crystallography and Photonics” RAS, Samara, Russia

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DOI: 10.18287/2412-6179-2017-41-559-563

Pages: 559-563.

Abstract:
The article considers the conforming identification of the fundamental matrix in the image matching problem. The method consists in the division of the initial overdetermined system into lesser dimensional subsystems. On these subsystems, a set of solutions is obtained, from which a subset of the most conforming solutions is defined. Then, on this subset the resulting solution is deduced. Since these subsystems are formed by all possible combinations of rows in the initial system, this method demonstrates high accuracy and stability, although it is computationally complex. A comparison with the methods of least squares, least absolute deviations, and the RANSAC method is drawn.

Keywords:
conforming identification; parallel algorithm; least squares method; least absolute deviations; epipolar geometry; projective geometry.

Citation:
Fursov VA, Gavrilov AV, Goshin YeV, Pugachev KG. Conforming identification of the fundamental matrix in the image matching problem. Computer Optics 2017; 41(4). 559-563. DOI: 10.18287/2412-6179-2017-41-4-559-563.

References:
  1. Fursov VA, Gavrilov AV. Conforming identification of the controlled object. Proceeding of the 2nd International Conference on Computing, Communications and Control Technologies 2004; 326-330.
  2. Fursov VA. Conformed identification of the controlled object with a small number of observations [In Russian]. Mechatronics, Automation, Control 2010; 3(108): 2-8.
  3. Goshin EV, Fursov VA. Conformed identification in corresponding points detection problem. Computer Optics 2012; 36(1): 131-135.
  4. Gruzman IS, Kirichuk VS, Kosyh VP, et al. Digital image processing in information systems [In Russian]. Novosibirsk: “NGTU” Publisher; 2002.
  5. Fischler MA, Bolles RC. Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Communications of the ACM 1981; 24(6): 381-392. DOI: 10.1145/358669.358692.
  6. Pugachev KG, Goshin EV, Fursov VA. Cluster implementation of the conforming identification algorithm [In Russian]. 3rd International Conference on Information Technology and Nanotechnology (ITNT-2016) 2016; 994-999.
  7. OpenCV. Source: <http://opencv.org/>.
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