Atapour-Abarghouei, Amir and de La Garanderie, Gregoire Payen and Breckon, Toby P. (2016) 'Back to Butterworth - a Fourier Basis for 3D Surface Relief Hole Filling within RGB-D Imagery.', 2016 23rd International Conference on Pattern Recognition (ICPR) Cancun, 4-8 Dec 2016.
We address the problem of hole filling in RGB-D (color and depth) images, obtained from either active or stereo based sensing, for the purposes of object removal and missing depth estimation. This is performed independently on the low frequency depth information (surface shape) and the high frequency depth detail (relief) by way of a Fourier space transform and classical Butterworth high/low pass filtering. The high frequency detail is then filled using a texture synthesis method, whilst the low frequency shape information is inpainted using structural inpainting. Here, a classical non-parametric sampling approach is extended, using the concept of query expansion, to perform high frequency depth synthesis with the final output then recombined in Fourier space. In order to improve the overall depth relief (D) and edge detail accuracy, color information (RGB) is also used to constrain the sampling process within high frequency component completion. Experimental results demonstrate the efficacy of the proposed method outperforming prior work for generalized depth filling in the presence of high frequency surface relief detail.
|Item Type:||Conference item (Paper)|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/ICPR.2016.7900062|
|Publisher statement:||© 2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||No date available|
|Date deposited:||29 November 2021|
|Date of first online publication:||24 April 2017|
|Date first made open access:||29 November 2021|
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