NDLI: Adaptive segmentation for multi-view stereo

Content Provider	IET Digital Library
Author	Khuboni, Ray Naidoo, Bashan
Abstract	This study presents an adaptive segmentation method for pre-processing input data to the patch-based multi-view stereo algorithm. A specially developed greyscale transformation is applied to the input image data, thus redefining the intensity histogram. The Nelder–Mead simplex method is used to adaptively locate an optimised segmentation threshold point in the modified histogram. The transformed input image is then segmented using the acquired threshold value, into foreground and background data. The segmentation information acquired is applied to the initial feature extraction and the cyclic patch-expansion procedure to constrain the reconstruction to a three-dimensional visibility space that excludes background artefacts. The method is targeted at segmenting out potentially disruptive data and is able to realise a reduction in cumulative error of the reconstruction process and thus improve the final reconstruction. With this method, the authors obtain results that are relatively similar to the original patch-based method, but with reduced time and space complexity.
Starting Page	10
Ending Page	21
Page Count	12
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 1, Feb (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2015.0446
Journal	IET Computer Vision
Publisher Date	2016-07-14
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Adaptive Segmentation Method Computer Vision And Image Processing Technique Cyclic Patch-expansion Procedure Feature Extraction Greyscale Transformation Image Recognition Image Segmentation Modified Histogram Nelder-Mead Simplex Method Optimised Segmentation Threshold Point Patch-based Multiview Stereo Algorithm Reconstruction Process Stereo Image Processing Three-dimensional Visibility Space
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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