Object recognition and pose estimation using appearance manifolds

Conventionally, image object recognition and pose estimation are two independent components in machine vision. This paper presented a simple but effective method KNN-SNG, which tightly couples these two components within a single algorithm framework. The basic idea of this method came from the bionic pattern recognition and the manifold ways of perception. Firstly, the shortest neighborhood graphs (SNG) are established for each registered object. SNG can be regarded as a covering and triangulation for a hypersurface on which the training data are distributed. Then for recognition task, the determined test image lies on which SNG by employing the parameter “k”, which could be calculated adaptively. Finally, the local linear approximation method was adopted to build a local map between high-dimensional image space and low-dimensional manifold for pose estimation. The projective coordinates on manifold can depict the pose of object. Experiment results manifested the effectiveness of the method.