Spin Image Revisited: Fast Candidate Selection using Outlier Forest Search

Spin Image Revisited: Fast Candidate Selection using Outlier Forest Search
computer vision; spin image; object recognition
Issue Date
ACCV Workshop on Color Depth Fusion in Computer vision
Spin-images have been widely used for surface registration and object detection from range images in that they are scale, rotation, and pose invariant. The computational complexity, however, is linear to the number of spin images in the model data set because valid candidates are chosen according to the similarity distribution between the input spin image and whole spin images in the data set. In this paper we present a fast method for valid candidate selection as well as approximate esti- mate of the similarity distribution using outlier search in the partitioned vocabulary trees. The sampled spin images in each tree are used for approximate density estimation and best matched candidates are then collected in the trees according to the statistics of the density. In contrast to the previous approaches that attempt to build compact representa- tions of the spin images, the proposed method reduces the search space using the hierarchical clusters of the spin images such that the computa- tional complexity is drastically reduced from O(K N) to O(K logN). K and N are the size of the spin-image features and the model data sets respectively. As demonstrated in the experimental results with a con- sumer depth camera, the proposed method is tens of times faster than the conventional method while the registration accuracy is preserved.
Appears in Collections:
KIST Publication > Conference Paper
Files in This Item:
There are no files associated with this item.
RIS (EndNote)
XLS (Excel)


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.