In this paper, we propose a three-dimensional (3D) image correlator by use of computational integral
imaging reconstruction based on the modified convolution property of periodic functions (CPPF) for
recognition of partially occluded objects. In the proposed correlator, elemental images of the reference
and target objects are picked up by a lenslet array, and subsequently are transformed to a sub-image array
which contains different perspectives according to the viewing direction. The modified version of the CPPF
is applied to the sub-images. This enables us to produce the plane sub-image arrays without the magnification
and superimposition processes used in the conventional methods. With the modified CPPF and the sub-image
arrays, we reconstruct the reference and target plane sub-image arrays according to the reconstruction plane.
3D object recognition is performed through cross-correlations between the reference and the target plane
sub-image arrays. To show the feasibility of the proposed method, some preliminary experiments on the
target objects are carried out and the results are presented. Experimental results reveal that the use of
plane sub-image arrays enables us to improve the correlation performance, compared to the conventional
method using the computational integral imaging reconstruction algorithm.
© 2014 Optical Society of Korea
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