Proceedings Article | 19 September 2013
Proc. SPIE. 8905, International Symposium on Photoelectronic Detection and Imaging 2013: Laser Sensing and Imaging and Applications
KEYWORDS: Principal component analysis, 3D acquisition, 3D imaging standards, Detection and tracking algorithms, Image processing, 3D modeling, Image filtering, Object recognition, Target recognition, 3D image processing
Spin image has been applied to 3D object recognition system successfully because of its advantages of rotation,
translation and view invariant. However, this method is very time consuming, owning to its high-dimensional
characteristics and its complicated matching procedure. To reduce the recognition time, in this paper we propose a
coarse-to-fine matching strategy for spin images. There are two steps to follow. Firstly, a low dimensional feature is
introduced for a given point. The feature contain two components, its first component is the perpendicular distance from
the centroid of the given point’s neighbor region to the tangential plane of the given point, its second component is the
maximum distance between the projection point of the centroid on the tangential plane and projection points of the
neighbor region on the tangential plane. Secondly when comparing a point from a target with a point from a model, their
low features are matched first, only if they satisfy the low feature constrains, can they be selected as a candidate point
pair and their spin images are further matched by similarity measurement. When all the target points and all the model
points finish above matching process, those candidate point pairs with high spin image similarity are selected as
corresponding point pairs, and the target can be recognized as the model with the most amount of corresponding point
pairs. Experiment based on Stanford 3D models is conducted, and the comparison of experiment results of our method
with the standard spin image shows that the propose method is more efficient while still maintain the standard spin
image’s advantages.
