Analysis of Intensity and Range Image Sequences Using Adaptive-Size Meshes

Abstract

This paper presents a new technique for reconstructing and analyzing sequential images using adaptive-size physically based models. In this method, the mesh size increases or decreases dynamically during reconstruction to locate nodes near surface areas of interest (such as high curvature points) and to optimize the fitting error. Image sequences of range and intensity data are used to demonstrate the power of the technique for shape estimation. Since the consecutive frames are often similar, the sequential image can be efficiently reconstructed by the adaptive-size meshes. In addition, a priori information about nonrigidity can be included so that surface model deforms to fit moving data points while preserving some basic nonrigid constraints (e.g., isometry or conformality). Several intensity and range image sequences are reconstructed efficiently using the adaptive-size meshes. The accuracy of the model is estimated in order to demonstrate the performance of this algorithm. Implementation of the proposed algorithm with and without isometric/conformal constraints is presented. The tracking of corresponding nodes using adaptive-size meshes on the face image sequences is also presented. Performance and accuracy of derived algorithms are demonstrated on simulated data of deforming ellipsoidal and bending planar shapes. Then the algorithm is applied to real range data for bending paper and to volumetric temporal left ventricular data.