Occlusion removal method of partially occluded 3D object using sub-image block matching in computational integral imaging

Abstract

In this paper, we propose an occlusion removal method using sub-image block matching for improved recognition of partially occluded 3D objects in computational integral imaging (CII). When 3D plane images are reconstructed in CII, occlusion degrades the resolution of reconstructed images. To overcome this problem, we apply the sub-image transform to elemental image array (EIA) and these sub-images are employed using block matching method for depth estimation. Based on the estimated depth information, we remove the unknown occlusion. After completing the occlusion removal for all sub-images, we obtain the modified EIA without occlusion information through the inverse sub-image transform. Finally, the 3D plane images are reconstructed by using a computational integral imaging reconstruction method with the modified EIA. The proposed method can provide a substantial gain in terms of the visual quality of 3D reconstructed images. To show the usefulness of the proposed method we carry out some experiments and the results are presented.

Improved Recognition of Far Objects by using DPM method in Curving-Effective Integral Imaging

“…집적영상 기술은 1908년에 Lipmann [7] 에 의해 제안된 이후, 부분적으로 가려진 3D 물체의 인식을 위해서 많은 연구가 진행되고 있다 [4][5][6] . 일반적으로 집적영상 기반의 인식 시스템은 물체인식을 위한 참조영상 (reference image)의 생성과정과 참조영상을 이용하여 목표 물체를 인식하는 두 가지 과정으로 나누어진다.…”

“…나 레이저와 같은 특별한 광원이 필요하지 않고, 수직 과 수평 시차와 연속적인 시점을 가지는 등의 장점을 가지고 있기 때문에, 현재 집적영상기술 기반의 3D 물체를 검출하고 인식하는 연구들이 활발히 진행되고 있다 [4][5][6] .…”

Computational Integral Imaging Reconstruction of a Partially Occluded Three-Dimensional Object Using an Image Inpainting Technique

“…Computational integral imaging (CII) is able to capture and reconstruct partially occluded 3D objects for 3D visualization and recognition [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. CII consists of the pickup process and the subsequent computational integral imaging reconstruction (CIIR) process.…”

“…In CIIR, the elemental images are digitally processed using a computer, by which 3D images can be easily reconstructed at a desired reconstruction plane. This CII approach is very useful in the visualization and recognition of 3D objects which are partially occluded by interposed objects [13][14][15][16][17][18][19][20].…”

“…Studies to address this problem [14][15][16][17][18] have been based on removing the occlusion from the recorded elemental images to obtain plane images of good visual quality. However, after the occluding object has been removed, corresponding holes remain in the elemental images.…”