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Open Access An Examination of the Effects of Noise Level on Methods to Determine Curvature in Range Images

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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).
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Keywords: 3D Vision; Curvature; Feature Extraction; Range Images

Document Type: Research Article

Publication date: January 13, 2019

This article was made available online on January 13, 2019 as a Fast Track article with title: "An examination of the effects of noise level on methods to determine curvature in range images".

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  • For more than 30 years, the Electronic Imaging Symposium has been serving those in the broad community - from academia and industry - who work on imaging science and digital technologies. The breadth of the Symposium covers the entire imaging science ecosystem, from capture (sensors, camera) through image processing (image quality, color and appearance) to how we and our surrogate machines see and interpret images. Applications covered include augmented reality, autonomous vehicles, machine vision, data analysis, digital and mobile photography, security, virtual reality, and human vision. IS&T began sole sponsorship of the meeting in 2016. All papers presented at EIs 20+ conferences are open access.

    Please note: For purposes of its Digital Library content, IS&T defines Open Access as papers that will be downloadable in their entirety for free in perpetuity. Copyright restrictions on papers vary; see individual paper for details.

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