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2016 | OriginalPaper | Buchkapitel

Spectral Reflectance Images and Applications

verfasst von : Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji Tominaga, Aboul Ella Hassanien

Erschienen in: Image Feature Detectors and Descriptors

Verlag: Springer International Publishing

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Abstract

Spectral imaging has received a great deal of attention recently. Spectral reflectance observed from object surfaces provides crucial information in computer vision and image analysis which include the essential problems of feature detection, image segmentation, and material classification. The estimation of spectral reflectance is affected by several illumination factors such as shading, gloss, and specular highlight. The spectral invariant representations for dielectric materials only, for these factors, are inadequate for other characteristic materials like metal. In this chapter, a spectral invariant representation is introduced for obtaining reliable spectral reflectance images. The invariant formulas for spectral images of natural objects preserve spectral information and are invariant to highlights, shading, surface geometry, and illumination intensity. As an application, a material classification method is presented based on the invariant representation, which results in reliable segmentations for natural scenes and raw circuit boards spectral images.

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Vorheriges Kapitel A Real Time Dactylology Based Feature Extractrion for Selective Image Encryption and Artificial Neural Network

Nächstes Kapitel Image Segmentation Using an Evolutionary Method Based on Allostatic Mechanisms

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Titel: Spectral Reflectance Images and Applications
verfasst von: Abdelhameed Ibrahim
Takahiko Horiuchi
Shoji Tominaga
Aboul Ella Hassanien
Verlag: Springer International Publishing
Buch: Image Feature Detectors and Descriptors
Print ISBN: 978-3-319-28852-9

Electronic ISBN: 978-3-319-28854-3

Copyright-Jahr: 2016
DOI: https://doi.org/10.1007/978-3-319-28854-3_9

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