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Erschienen in: Machine Vision and Applications 3-4/2017

18.02.2017 | Original Paper

Removal of specular reflections from image sequences using feature correspondences

verfasst von: Syed. M. Z. Abbas Shah, Stephen Marshall, Paul Murray

Erschienen in: Machine Vision and Applications | Ausgabe 3-4/2017

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Abstract

The presence of specular highlights can hide underlying features of a scene within an image and can be problematic in many application scenarios. In particular, this poses a significant challenge for applications where image stitching is used to create a single static image of a scene from inspection footage of pipes, gas tubes, train tracks and concrete structures. Furthermore, they can hide small defects in the images causing them to be missed during inspection. We present a method which exploits additional information in neighbouring frames from video footage to reduce specularity from each frame. The technique first automatically determines frames which contain overlapping regions before the relationship that exists between them is exploited in order to suppress the effects of specular reflections. This results in an image that is free from specular highlights provided there is at least one frame present in the sequence where a given pixel is present in a diffuse form. The method is shown to work well on greyscale as well as colour images and effectively reduces specularity and significantly improves the quality of the stitched image, even in the presence of noise. While applied to the challenge of reducing specularity in inspection videos, the method improves upon the state-of-the-art in specularity removal, and its applications are wide-ranging as a general purpose pre-processing tool.

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Metadaten
Titel
Removal of specular reflections from image sequences using feature correspondences
verfasst von
Syed. M. Z. Abbas Shah
Stephen Marshall
Paul Murray
Publikationsdatum
18.02.2017
Verlag
Springer Berlin Heidelberg
Erschienen in
Machine Vision and Applications / Ausgabe 3-4/2017
Print ISSN: 0932-8092
Elektronische ISSN: 1432-1769
DOI
https://doi.org/10.1007/s00138-017-0826-6

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