Paper
5 June 2014 Three-dimensional object recognition via integral imaging and scale invariant feature transform
Faliu Yi, Inkyu Moon
Author Affiliations +
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.
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Faliu Yi and Inkyu Moon "Three-dimensional object recognition via integral imaging and scale invariant feature transform", Proc. SPIE 9117, Three-Dimensional Imaging, Visualization, and Display 2014, 911715 (5 June 2014); https://doi.org/10.1117/12.2048766
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CITATIONS
Cited by 1 scholarly publication and 2 patents.
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KEYWORDS
3D image processing

Object recognition

Integral imaging

Detection and tracking algorithms

3D acquisition

3D image reconstruction

Feature extraction

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