By Andreas Koschan, Marc Pollefeys, Mongi Abidi
This publication offers the completely revised types of lectures given through prime researchers throughout the Workshop on complicated 3D Imaging for security and safety together with the foreign convention on laptop imaginative and prescient and development attractiveness CVPR 2005, held in San Diego, CA, united states in June 2005. It covers the present state-of-the-art in 3D imaging for defense and security.
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In Proc. Mona Lisa European Workshop, Combined Real and Synthetic Image Processing for Broadcast and Video Production, Hamburg, Germany, 1994. 38. H. T. Tanaka, M. Ikeda, and H. Chiaki. Curvature-based face surface recognition using spherical correlation. Principal directions for curved object recognition. In Proceedings of The Third IEEE International Conference on Automatic Face and Gesture Recognition, pp. 372-377, 1998. 39. Y. Wang, X. Huang, C. Lee, S. Zhang, Z. Li, D. Samaras, D. N. Metaxas, A.
The 3D point P (X, Y, Z), whose projection on the image plane is at (x, y), is given by ⎛X ⎞ ⎜ ⎟ ⎜Y ⎟= ⎜ ⎟ ⎜Z ⎟ ⎝ ⎠ ⎛ x⎞ ⎜ ⎟ b ⎜ ⎟ ( f cot θ − x ) ⎜⎜ y ⎟⎟ ⎝f⎠ (1) where f is the camera’s focal length. In the calibration process, the unknown parameters b, θ, and f are computed from the system of nonlinear equations described in (1) with known (x, y) points and their corresponding (X, Y, Z) coordinates. Depth recovered applying triangulation technique has high accuracy. 39. Two cameras (instead of one as shown in Figure 2-3) were engaged to avoid occlusions.
Vertical continuity constraint was enforced in the stripe detection phase. A median filter was used to smooth out local bumps. Horizontal positions of the stripes were obtained at subpixel locations by applying interpolation to the horizontal profiles. 5 seconds. The depth resolution of the reconstruction was not discussed but the 3D face database built was successfully used in 3D face recognition6. Garcia and Dugelay7 used homographies to avoid constraints on orientation of the camera and projector.