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 com-ponents within a single algorithm framework. The basic idea of this method came from the bionic pattern recog-nition 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 deter-mined 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.
Zhong-Hua Hao Shi-Wei Ma
. Object recognition and pose estimation using appearance manifolds[J]. Advances in Manufacturing, 2013
, 1(3)
: 258
-264
.
DOI: DOI10.1007/s40436-013-0022-5
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