3D Image Correlator using Computational Integral Imaging Reconstruction Based on Modified Convolution Property of Periodic Functions

Abstract

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.

Resolution improvements in integral microscopy with Fourier plane recording

“…The integral image captured by the system contains the spatial and the angular information [7], so it is possible to reconstruct the scene at various depths [8][9][10]. For performing this task, different reconstruction algorithms have been proposed [11][12][13][14][15][16]. In [17,18], several works have been developed adapting InI technology to microscopy.…”

Free segmentation in rendered 3D images through synthetic impulse response in integral imaging

“…The recover of the depth information of a 3D scene can be achieved from a single capture [13,14] by computationally projecting every EI through a virtual pinhole array or, equivalently overlapping and summing the intensities of the different elemental images [14]. Based on this principle, many digital reconstruction algorithms have been proposed [15][16][17][18][19][20]. Here, we have developed a new free depth-reconstruction method based on 2D deconvolution between the integral image capture in an InI setup and the corresponding IRF.…”

Neighboring Elemental Image Exemplar Based Inpainting for Computational Integral Imaging Reconstruction with Partial Occlusion

“…This property enables the CII to reconstruct partially occluded 3-D objects for 3-D visualization and recognition [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Several occlusion removal methods have been studied in [15][16][17][18][19] to solve the problem of the degraded resolution of the computationally reconstructed 3-D image, which occurs due to the partial occlusion.…”