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Color Invariant Representation and Applications

Color Invariant Representation and Applications

Abdelhameed Ibrahim, Takahiko Horiuchi, Shoji Tominaga, Aboul Ella Hassanien
Copyright: © 2017 |Pages: 21
ISBN13: 9781522522294|ISBN10: 1522522298|EISBN13: 9781522522300
DOI: 10.4018/978-1-5225-2229-4.ch046
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MLA

Ibrahim, Abdelhameed, et al. "Color Invariant Representation and Applications." Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, IGI Global, 2017, pp. 1041-1061. https://doi.org/10.4018/978-1-5225-2229-4.ch046

APA

Ibrahim, A., Horiuchi, T., Tominaga, S., & Hassanien, A. E. (2017). Color Invariant Representation and Applications. In A. Hassanien & T. Gaber (Eds.), Handbook of Research on Machine Learning Innovations and Trends (pp. 1041-1061). IGI Global. https://doi.org/10.4018/978-1-5225-2229-4.ch046

Chicago

Ibrahim, Abdelhameed, et al. "Color Invariant Representation and Applications." In Handbook of Research on Machine Learning Innovations and Trends, edited by Aboul Ella Hassanien and Tarek Gaber, 1041-1061. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-2229-4.ch046

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Abstract

Illumination factors such as shading, shadow, and highlight observed from object surfaces affect the appearance and analysis of natural color images. Invariant representations to these factors were presented in several ways. Most of these methods used the standard dichromatic reflection model that assumed inhomogeneous dielectric material. The standard model cannot describe metallic objects. This chapter introduces an illumination-invariant representation that is derived from the standard dichromatic reflection model for inhomogeneous dielectric and the extended dichromatic reflection model for homogeneous metal. The illumination color is estimated from two inhomogeneous surfaces to recover the surface reflectance of object without using a reference white standard. The overall performance of the invariant representation is examined in experiments using real-world objects including metals and dielectrics in detail. The feasibility of the representation for effective edge detection is introduced and compared with the state-of-the-art illumination-invariant methods.

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