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METHODS OF STEREO PAIR IMAGES FORMATION
WITH A GIVEN PARALLAX VALUE
V.G. Chafonovaa, I.V. Gazeevaa a
Saint Petersburg State University of Film and Television, Saint Petersburg, 191119, Russian Federation, [email protected]
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.
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10.Rosten E., Drummond T. Machine learning for high-speed corner detection // Proc. 9th European Conference
on Computer Vision ECCV 2006. Graz, Austria, 2006. V. 3951 LNCS. P. 430–443.
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. V. 32. N 1. P. 105–119.
12.Bay H., Ess A., Tuytelaars T., Van Gool L. Speeded up robust features (SURF) // Computer Vision and Image
Understanding. 2008. V. 110. N 3. P. 346–359.
13.Rosten E., Drummond T. Fusing points and lines for high performance tracking // Proc. 10th IEEE
Interna-tional Conference on Computer Vision. 2005. V. 2. P. 1508–1515.
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Viktoriya G. Chafonova – postgraduate, Saint Petersburg State University of Film and Television, Saint Petersburg, 191119, Russian Federation, [email protected]
Irina V. Gazeeva – PhD, Associate professor, Associate professor, Saint Petersburg State University of Film and Television, Saint Petersburg, 191119, Russian Federation, [email protected]
