3d Imaging for Safety and Security by Andreas Koschan, Marc Pollefeys, Mongi Abidi

By Andreas Koschan, Marc Pollefeys, Mongi Abidi

This e-book offers the completely revised models of lectures given via major researchers through the Workshop on complex 3D Imaging for security and safety along side the overseas convention on desktop imaginative and prescient and development acceptance CVPR 2005, held in San Diego, CA, united states in June 2005. It covers the present state-of-the-art in 3D imaging for safeguard and security.

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69. A set of control points, V, was defined on the facial landmarks of a generic 3D model. A cost function was developed based on four weighted estimates: 1) distance between intensities of the given 2D image and the projected image, 2) symmetry, 3) distance between the estimated 3D points and 3D points of the generic model, and 4) model ratios. The estimates were obtained from V and the cost function was optimized for a set of control points, V', featuring the 3D facial model of an individual.

Figure 2-6. Illustration of facial reconstruction from morphing: (a) A 3D template face, (b) 3D template superimposed on a 2D facial image, (c) an intermediate stage of morphing 3D template, (d) reconstructed 3D face. image of the person. The algorithm extracted the shape of the face using a single 2D input image which could be anywhere in the range of frontal to profile views. The output was a 3D textured model of the new face. The technique cited above has two major registration tasks to perform the registration among the 3D scanned head models and the registeration of the 3D generic facial model with 2D intensity images of the face that needs to be modeled.

67 who used a single 3D model, a frontal view, and a profile view. In both works, the deformation was performed by minimizing 44 S. Huq et al. a distance based cost function. Zhang and Cohen used a cubic explicit polynomial for the deformation10. They used a generic 3D model and multiple 2D images of unknown facial poses. 69. A set of control points, V, was defined on the facial landmarks of a generic 3D model. A cost function was developed based on four weighted estimates: 1) distance between intensities of the given 2D image and the projected image, 2) symmetry, 3) distance between the estimated 3D points and 3D points of the generic model, and 4) model ratios.

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