Enhanced computational integral imaging system for partially occluded 3D objects using occlusion removal technique and recursive PCA reconstruction

Lee, Byung-Gook; Liliana,; Shin, Dong-Hak

doi:10.1016/j.optcom.2010.01.044

Cited by 13 publications

(8 citation statements)

References 16 publications

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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].…”

Section: Introductionmentioning

confidence: 99%

“…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.…”

Section: Introductionmentioning

confidence: 99%

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

Lee¹,

Ko²,

Lee³

et al. 2015

Journal of the Optical Society of Korea

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In this paper we propose an improved version of the computational integral imaging reconstruction (CIIR) for visualizing a partially occluded object by utilizing an image inpainting technique. In the proposed method the elemental images for a partially occluded three-dimensional (3D) object are recorded through the integral imaging pickup process. Next, the depth of occlusion within the elemental images is estimated using two different CIIR methods, and the weight mask pattern for occlusion is generated. After that, we apply our image inpainting technique to the recorded elemental images to fill in the occluding area with reliable data, using information from neighboring pixels. Finally, the inpainted elemental images for the occluded region are reconstructed using the CIIR process. To verify the validity of the proposed system, we carry out preliminary experiments in which faces are the objects. The experimental results reveal that the proposed system can dramatically improve the quality of a reconstructed CIIR image.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lee¹,

Ko²,

Lee³

et al. 2015

Journal of the Optical Society of Korea

Self Cite

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“…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.…”

Section: Introductionmentioning

confidence: 99%

“…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. In these studies, the occlusion region is detected by calculating the depth map.…”

Section: Introductionmentioning

confidence: 99%

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

Ko¹,

Lee²,

Lee³

2015

Journal of the Optical Society of Korea

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We propose a partial occlusion removal method for computational integral imaging reconstruction (CIIR) based on the usage of the exemplar based inpainting technique. The proposed method is an improved version of the original linear inpainting based CIIR (LI-CIIR), which uses the inpainting technique to fill in the data missing region. The LI-CIIR shows good results for images which contain objects with smooth surfaces. However, if the object has a textured surface, the result of the LI-CIIR deteriorates, since the linear inpainting cannot recover the textured data in the data missing region well. In this work, we utilize the exemplar based inpainting to fill in the textured data in the data missing region. We call the proposed method the neighboring elemental image exemplar based inpainting (NEI-exemplar inpainting) method, since it uses sources from neighboring elemental images to fill in the data missing region. Furthermore, we also propose an automatic occluding region extraction method based on the use of the mutual constraint using depth estimation (MC-DE) and the level set based bimodal segmentation. Experimental results show the validity of the proposed system.

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3D object recognition and classification: a systematic literature review

Carvalho

Wangenheim

2019

Pattern Anal Applic

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Enhanced computational integral imaging system for partially occluded 3D objects using occlusion removal technique and recursive PCA reconstruction

Cited by 13 publications

References 16 publications

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

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

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

3D object recognition and classification: a systematic literature review

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