METHODS OF STEREO PAIR IMAGES FORMATION WITH A GIVEN PARALLAX VALUE

V. G. Chafonova, I. V. Gazeeva


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Abstract
Two new complementary methods of stereo pair images formation are proposed. The first method is based on finding the maximum correlation between the gradient images of the left and right frames. The second one implies the
finding of the shift between two corresponding key points of images for a stereo pair found by a detector of point features. These methods give the possibility to set desired values of vertical and horizontal parallaxes for the selected object in the image. Application of these methods makes it possible to measure the parallax values for the objects on the final stereo pair in pixels and / or the percentage of the total image size. It gives the possibility to predict the possible excesses in parallax values while stereo pair printing or projection. The proposed methods are easily automated after object selection, for which a predetermined value of the horizontal parallax will be exposed. Stereo pair images superposition using the key points takes less than one second. The method with correlation application requires a little bit more computing time, but makes it possible to control and superpose undivided anaglyph image. The proposed methods of stereo pair formation can find their application in programs for editing and processing images of a stereo pair, in the monitoring devices for shooting cameras and in the devices for video sequence quality assessment.

Keywords: vertical and horizontal parallax, stereo image, stereo pair images, correlation, gradient

Acknowledgements. The work has been carried out with the financial support of the Ministry of Education and Science of the Russian Federation.

References
 1.     Melkumov A.S. Instrumentarii dlya malobyudzhetnoi stereos"emki [Tools for low-budget stereo photos]. Mir Tekhniki Kino, 2011, no. 22, pp. 25–32.
2.     Komar V.G., Rozhkov S.N., Chekalin D.A. Neobkhodimost' normirovaniya parametrov stereopary i stereoproektsii s tsel'yu snizheniya zritel'nogo diskomforta v usloviyakh kinozala [Necessity of stereo pai r and srereo projection pa rameters normalizati on for visual discomfort minimizing in theat rical conditions]. Mir Tekhniki Kino, 2012, no. 24, pp. 31–44.
3.     Elkhov V.A., Kondrat'ev N.V., Ovechkis Yu.N., Pautova L.V. Analiz parametrov mnogoob"ektivnoi s"emki v sisteme bezochkovogo kinopokaza mnogorakursnykh stereoizobrazhenii [Analysis of parameters of multiobjective shooting in the system of glassless stereoscopic cinema]. Mir Tekhniki Kino, 2010, no. 17, pp. 2–7.
4.     Rozhkov S.N., Ovsyannikova N.A. Stereoskopiya v Kino-, Foto-, Videotekhnike. Terminologicheskii Slovar' [Stereoscopy in Cinematographic, Photographic, Video Equipment. Glossary]. Moscow, Paradiz Publ., 2003, 138 p.
5.     Melkumov A.S. Osnovy Stereografii [Basics of Stereography]. Mir Tekhniki Kino, 2010, no. 18, pp. 30–38.
6.     Rozhkov S.N. Osobennosti vospriyatiya stereoizobrazheniya v kinozale [Specifics of perception in stereo cinema]. Mir Tekhniki Kino, 2008, no. 10, pp. 10–15.
7.     Gazeeva I.V., Tikhomirova G.V., Chafonova V.G. Algoritmy tsifrovoi konvergentsii izobrazhenii stereopary [Algorithms of the digital stereopair convergence]. Mir Tekhniki Kino, 2014, no. 1 (31), pp. 10–17.
8.     Gonsales R.C., Woods R.E., Eddins S.L. Digital Image Processing Using MATLAB. Prentice Hall, 2004, 344 p.
9.     Gonzales R.C., Woods R.E. Digital Image Processing. 2nd Ed. Prentice Hall, 2002, 190 p.
10.Rosten E., Drummond T. Machine learning for high-speed corner detection. Proc. 9th European Conference on Computer Vision ECCV 2006. Graz, Austria, 2006, vol. 3951 LNCS, pp. 430–443. doi: 10.1007/11744023_34
11.Rosten E., Porter R., Drummond T. Faster and better: a machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, vol. 32, no. 1, pp. 105–119. doi: 10.1109/TPAMI.2008.275
12.Bay H., Ess A., Tuytelaars T., Van Gool L. Speeded up robust features (SURF). Computer Vision and Image Understanding, 2008, vol. 110, no. 3, pp. 346–359. doi: 10.1016/j.cviu.2007.09.014
13.Rosten E., Drummond T. Fusing points and lines for high performance tracking. Proc. 10th IEEE International Conference on Computer Vision, 2005, vol. 2, pp. 1508–1515. doi: 10.1109/ICCV.2005.104
14.Volkovich A.N., Zhuk D.V., Tuzikov A.V. [Three-dimensional model reconstructing by means of stereo images considering its multisequencing]. Scientific and Technical Journal of Information Technologies, Mechanics and Optics, 2008, no. 58, pp. 3–10.
15.Goshin Y.V., Fursov V.A. Metod soglasovannoi identifikatsii v zadache opredeleniya sootvetstvennykh tochek na izobrazheniyakh [Identification in corresponding points detection problem]. Computer Optics, 2012, vol. 36, no. 1, pp. 131–135.


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