AAM and Non-rigid Registration in Augmented Reality

Abstract

Augmented Reality copes with the problem of dynamically augmenting or enhancing the real world with computer generated virtual objects [Azuma, 1997; Azuma, 2001]. Registration is one of the most pivotal problems in augmented reality applications. Typical augmented reality applications track 2D patterns on rigid planar objects in order to acquire the pose of the camera in the scene. Although the problem of rigid registration has been widely studied [Yuan et al., 2005; Yuan et al., 2006; Guan et al., 2008a; Guan et al., 2008b; Li et al., 2008], non-rigid registration is recently receiving more and more attention. There are many non-rigid objects existing in the real world such as animated faces, deformable cloth, hand and so forth. How to overlaid virtual objects on the non-rigid objects is particular challenging. Recently, many related non-rigid registration approaches have been reported. In many cases (e.g. human faces), only a few feature points can be reliably tracked. In [Bartoli et al., 2004], a non-rigid registration method using point and curve correspondences was proposed to solve this problem. They introduced curves into the non-rigid factorization framework because there are several curves (e.g. the hairline, the eyebows) can be used to determine the mapping. The mapping function is computed from both point and curve correspondences. This method can successfully augment the non-rigid object with a virtual object. In [Pilet et al., 2005; Pilet et al., 2007], they presented a real-time method for detecting deformable surfaces with no need whatsoever for a prior pose knowledge. The deformable 2D meshes are introduced. With the use of fast wide baseline matching algorithm, they can superimpose an appropriately deformed logo on the T-shirt. These methods are robust to large deformations, lighting changes, motion blur and occlusions. To align the virtual objects generated by computers with the real world seamlessly, the accurate registration data should be provided. In general, registration can be achieved by solving a point matching problem. The problem is to find the correspondence between two sets of tracked feature points. Therefore, the detection of feature points and the points tracking are the two main problems. Rigid object detection and tracking have been extensively studied and effective, robust, and real-time solutions proposed [Lowe, 2004; Lepetit & Fua, 2005; Lepetit et al., 2005; Rosten & Drummond, 2005]. Non-rigid object detection and tracking is far more complex because the object is deformable and not only the registration data but also a large number of deformation parameters must be estimated. Source: Augmented Reality, Book edited by: Soha Maad, ISBN 978-953-7619-69-5, pp. 230, January 2010, INTECH, Croatia, downloaded from SCIYO.COM