DocumentCode :
248361
Title :
3D keypoint detection by light field scale-depth space analysis
Author :
Tosic, I. ; Berkner, K.
Author_Institution :
Ricoh Innovations Corp., Menlo Park, CA, USA
fYear :
2014
fDate :
27-30 Oct. 2014
Firstpage :
1927
Lastpage :
1931
Abstract :
We present a method for 3D keypoint detection from light field data, typically obtained by planar camera arrays or plenoptic cameras. The proposed approach is based on construction of novel light field scale-depth spaces that are designed to leverage the specific properties of light fields. The constructed scale-depth spaces are based on a modified Gaussian kernel that is parametrized both in terms of scale of objects recorded by the light field and in terms of objects´ depth. We prove theoretically that the new scale-depth space formulation and its spatial derivative satisfy the scale invariance property for all depths. By finding local extrema in such scale-depth spaces we locate 3D keypoints (such as 3D edges) and show that they outperform SURF keypoints on a 3D structure estimation task, both in accuracy and computational efficiency.
Keywords :
cameras; image processing; optical information processing; 3D keypoint detection; 3D structure estimation task; light field scale-depth space analysis; modified Gaussian kernel; planar camera arrays; plenoptic cameras; scale invariance property; scale-depth space formulation; Cameras; Computer vision; Estimation; Feature extraction; Image edge detection; Kernel; Three-dimensional displays; 3D keypoint detection; Scale-space analysis; light fields; plenoptic cameras;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Image Processing (ICIP), 2014 IEEE International Conference on
Conference_Location :
Paris
Type :
conf
DOI :
10.1109/ICIP.2014.7025386
Filename :
7025386
Link To Document :
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