Journal ArticleDOI
A computational strategy exploiting genetic algorithms to recover color surface reflectance functions
Raimondo Schettini,Silvia Zuffi +1 more
Reads0
Chats0
TLDR
This work addresses the recovery of a surface reflectance function, given the tristimulus values under one or more illuminants, using a variety of standard datasets.Abstract:
Information about the spectral reflectance of a color surface is useful in many applications. Assuming that reflectance functions can be adequately approximated by a linear combination of a small number of basis functions, we address here the recovery of a surface reflectance function, given the tristimulus values under one or more illuminants. Basis functions presenting different characteristics and cardinalities are investigated, and genetic algorithms are employed to optimize the estimation. Our analysis of a variety of standard datasets provides information about the ability of each set of basis functions we used to model generic reflectance spectra.read more
Citations
More filters
Journal ArticleDOI
Color in image and video processing: most recent trends and future research directions
TL;DR: The most recent trends as well as the state-of-the-art, with a broad survey of the relevant literature, in the main active research areas in color imaging.
Journal ArticleDOI
Spectral reflectance estimation from camera responses by support vector regression and a composite model.
Weifeng Zhang,Dao-Qing Dai +1 more
TL;DR: The proposed approach utilizes a composite modeling scheme, which formulates the RGB values and the sampled wavelength together as the input term to make the most use of the information from the training samples to improve the spectral estimation accuracy.
Journal ArticleDOI
Reflectance spectra recovery from tristimulus values by extraction of color feature match
TL;DR: In this article, a modified pseudo-inverse method was proposed to recover spectral reflectance from CIE tristimulus values using color feature match. But, the method is not suitable for spectral spectral reconstruction.
Journal ArticleDOI
Improved method for spectral reflectance estimation and application to mobile phone cameras.
TL;DR: In this article , the authors proposed an improved method for estimating surface-spectral reflectance from the image data acquired by an RGB digital camera. But the method is limited to the visible range, where a camera captures multiple images for the scene of an object under multiple light sources.
Journal ArticleDOI
Spectral reflectance reconstruction with the locally weighted linear model
TL;DR: A locally weighted linear model is proposed for spectral reflectance reconstruction that improves the contribution of the local neighbors with higher similarity to the test point, which can reduce the influence of noisy points and redundant points.
References
More filters
Book
Genetic algorithms in search, optimization, and machine learning
TL;DR: In this article, the authors present the computer techniques, mathematical tools, and research results that will enable both students and practitioners to apply genetic algorithms to problems in many fields, including computer programming and mathematics.
Book
An Introduction to Genetic Algorithms
TL;DR: An Introduction to Genetic Algorithms focuses in depth on a small set of important and interesting topics -- particularly in machine learning, scientific modeling, and artificial life -- and reviews a broad span of research, including the work of Mitchell and her colleagues.
Journal ArticleDOI
An Introduction to Genetic Algorithms.
TL;DR: An Introduction to Genetic Algorithms as discussed by the authors is one of the rare examples of a book in which every single page is worth reading, and the author, Melanie Mitchell, manages to describe in depth many fascinating examples as well as important theoretical issues.
Book
Evaluation of linear models of surface spectral reflectance with small numbers of parameters
TL;DR: In this paper, the authors analyze two large sets of empirical surface spectral reflectances and examine three conjectures concerning constraints on surface reflectance: that empirical surface reflectances fall within a linear model with a small number of parameters, that empirical surfaces reflectances are within a matrix-based model composed of band-limited functions with only three or four parameters, and that the shape of the spectral-sensitivity curves of human vision enhances the fit between empirical surface reflectsances and linear models.