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Volume: 31 | Article ID: art00023
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An Examination of the Effects of Noise Level on Methods to Determine Curvature in Range Images
Jacob D Hauenstein
Timothy S Newman
  DOI :  10.2352/ISSN.2470-1173.2019.10.IQSP-325  Published Online :  January 2019
Abstract

A study of the impact of image noise on well-known range image curvature determination methods is presented here. The study considers 12 methods, and each is analyzed based on its performance at varying levels of input noise. The performance analyses consider quality factors of (1) absolute error, (2) correlation with correct, expected curvature values, and (3) signal-tonoise ratio (SNR). Curvature-based renderings are also presented for some data to provide basic visualizations of the impact of noise on one curvature-based task. The work can benefit tasks using range data (e.g., from Kinect or commercial-grade sensors).

Journal Title : Electronic Imaging
Publisher Name : Society for Imaging Science and Technology
Publisher Location : 7003 Kilworth Lane, Springfield, VA 22151 USA
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 DOI 10.2352/ISSN.2470-1173.2019.10.IQSP-325
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Jacob D Hauenstein, Timothy S Newman, "An Examination of the Effects of Noise Level on Methods to Determine Curvature in Range Images"  in Proc. IS&T Int’l. Symp. on Electronic Imaging: Image Quality and System Performance XVI,  2019,  pp 325-1 - 325-7,  https://doi.org/10.2352/ISSN.2470-1173.2019.10.IQSP-325

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Copyright © Society for Imaging Science and Technology 2019
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
7003 Kilworth Lane, Springfield, VA 22151 USA
10.2352/ISSN.2470-1173.2019.10.IQSP-325
2470-1173(20190113)2019:10L.3251;1-
ei_24701173_v2019n10_r1/s23.xml
/ist/ei/2019/00002019/00000010/art00023
Articles
An Examination of the Effects of Noise Level on Methods to Determine Curvature in Range Images
HauensteinJacob D
NewmanTimothy S
13012019
2019
10
325-1
325-7
2019
A study of the impact of image noise on well-known range image curvature determination methods is presented here. The study considers 12 methods, and each is analyzed based on its performance at varying levels of input noise. The performance analyses consider quality factors of (1) absolute error, (2) correlation with correct, expected curvature values, and (3) signal-tonoise ratio (SNR). Curvature-based renderings are also presented for some data to provide basic visualizations of the impact of noise on one curvature-based task. The work can benefit tasks using range data (e.g., from Kinect or commercial-grade sensors).
Range ImagesCurvatureFeature Extraction3D Vision