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Proceedings Paper

Three-dimensional object recognition via integral imaging and scale invariant feature transform
Author(s): Faliu Yi; Inkyu Moon
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Paper Abstract

We propose a three-dimensional (3D) object recognition approach via computational integral imaging and scale invariant feature transform (SIFT) that can be invariance to object changes in illumination, scale, rotation and affine. Usually, the matching between features extracted in reference object and that in computationally reconstructed image should be done for 3D object recognition. However, this process needs to alternately illustrate all of the depth images first which will affect the recognition efficiency. Considering that there are a set of elemental images with different viewpoint in integral imaging, we first recognize the object in 2D image by using five elemental images and then choose one elemental image with the most matching points from the five images. This selected image will include more information related to the reference object. Finally, we can use this selected elemental image and its neighboring elemental images which should also contain much reference object information to calculate the disparity with SIFT algorithm. Consequently, the depth of the 3D object can be achieved with stereo camera theory and the recognized 3D object can be reconstructed in computational integral imaging. This method sufficiently utilizes the different information provided by elemental images and the robust feature extraction SIFT algorithm to recognize 3D objects.

Paper Details

Date Published: 5 June 2014
PDF: 5 pages
Proc. SPIE 9117, Three-Dimensional Imaging, Visualization, and Display 2014, 911715 (5 June 2014); doi: 10.1117/12.2048766
Show Author Affiliations
Faliu Yi, Chosun Univ. (Korea, Republic of)
Inkyu Moon, Chosun Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 9117:
Three-Dimensional Imaging, Visualization, and Display 2014
Bahram Javidi; Jung-Young Son; Osamu Matoba; Manuel Martínez-Corral; Adrian Stern, Editor(s)

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