An Isomap-Eigenanalysis-Regression Pose Estimation Algorithm of Three-Dimentional Object

Diverse pose estimation of three-dimensional (3D) object in the whole view-space remains a challenge in the field of pattern recognition. In this paper, a pose estimation algorithm of 3D object named isomap-eigenanalysis-regression (Isomap-E-R), which estimates arbitrary pose of 3D object in the whole view space, is proposed. For the training set, the low-dimensional embedding of input pattern set is computed by isomap, and the eigen-images of the embedding are deduced on the basis of an eigenspace. A different projection direction in low dimensional embedding is utilized to improve the accuracy of pose estimation. The metrics on each direction, derived by linear regression, is then used to further deduce the projection of the training set. For a given new sample, its projection onto the eigen-images is first computed, and the training images nearest to those deduced for the new sample by the algorithm give the estimation poses. The performance analysis on the obtained experimental results demonstrated that the proposed method could estimate the diverse pose of 3D object with significant efficiency and precision. Finally, the algorithm can be also extended to real-time pose estimate of 3D object and other potential applications.