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Multi-Focusing Image Capture System for 3D Stereo Image

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초록

In this paper, we suggest a new camera capturing and synthesizing algorithm with the multi-captured left and right images for the better comfortable feeling of 3D depth and also propose 3D image capturing hardware system based on the this new algorithm. We also suggest the simple control algorithm for the calibration of camera capture system with zooming function based on a performance index measure which is used as feedback information for the stabilization of focusing control problem. We also comment on the theoretical mapping theory concerning projection under the assumption that human is sitting 50cm in front of and watching the 3D LCD screen for the captured image based on the modeling of pinhole Camera. We choose 9 segmentations and propose the method to find optimal alignment and focusing based on the measure of alignment and sharpness and propose the synthesizing fusion with the optimized 9 segmentation images for the best 3D depth feeling.

Abstract

In this paper, we suggest a new camera capturing and synthesizing algorithm with the multi-captured left and right images for the better comfortable feeling of 3D depth and also propose 3D image capturing hardware system based on the this new algorithm. We also suggest the simple control algorithm for the calibration of camera capture system with zooming function based on a performance index measure which is used as feedback information for the stabilization of focusing control problem. We also comment on the theoretical mapping theory concerning projection under the assumption that human is sitting 50cm in front of and watching the 3D LCD screen for the captured image based on the modeling of pinhole Camera. We choose 9 segmentations and propose the method to find optimal alignment and focusing based on the measure of alignment and sharpness and propose the synthesizing fusion with the optimized 9 segmentation images for the best 3D depth feeling.

참고문헌 (10)

  1. C. Zhang, "View‐dependent Nonuniform Sampling for Image‐based Rendering", ICIP 2004 (2004)
  2. D. Scharstein, "A taxonomy and evaluation of dense two‐frame stereo correspondence algorithms", International Journal of Computer Vision, 47(1), pp.7~42 (2002)
  3. I.J.Cox, "Dynamic histogram warping of image pairs for cinstant image brightness", IEEE International Conference on Image Processing, 통권 : 2, pp.366~369 (1995)
  4. Fleet, D.J., "Optical flow estimation, in Mathematical models for Computer Vision: The Handbook" (2005), Springer
  5. J.L. Barron, "Perfomance of optical flow techniques", International Journal of Computer Vision, 12(1), pp.43~77 (1994)
  6. N. Holliman, "Smoothing Region Boundaries in Variable Depth Mapping for Real Time Stereoscopic Images", vol.5664A of Proceedings of SPIE‐IST Symposium on Electronic Imaging, 통권 : 5664 (2005)
  7. N. Holliman, "3D Display Systems, in Handbook of Opto‐electronics" (2005), IOP Press
  8. N. Holliman, "Mapping Perceived Depth to Regions of Interest in Stereoscopic Images, in Stereoscopic Displays and Virtual reality System XI", Proceedings of SPIE 5291 (2004)
  9. S.Kim, "A New Algorithm for Depth Perception in 3D Screen and Its Implementation", 2007 International Sympsium on Information Technology Convergence (2007)
  10. T.Dang, "Selfcalibration for Active Automotive Stereo Vision", IEEE Intelligent Vehicles Symposium, pp.364~369 (2006)

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