Deep Learning for Illuminant Estimation: Difference between revisions
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== Introduction == | == Introduction == | ||
Our project aims to estimate the illuminant of a scene using various methods of machine learning with a focus on the deep learning method, convolutional neural networks (CNN). Illumination estimation is an area of interest, because it has applications in topics including image reproduction and image retrieval. For image reproduction, an image may be captured under a certain lighting but rendered under a different lighting. In image retrieval and computer vision, the illumination of the scene needs to be estimated so that it can be accounted for when images of objects are obtained under different lighting. | Our project aims to estimate the illuminant of a scene using various methods of machine learning with a focus on the deep learning method, convolutional neural networks (CNN). Illumination estimation is an area of interest, because it has applications in topics including image reproduction and image retrieval. For image reproduction, an image may be captured under a certain lighting but rendered under a different lighting. In image retrieval and computer vision, the illumination of the scene needs to be estimated so that it can be accounted for when images of objects are obtained under different lighting. | ||
In past research on illuminant estimation, various methods have been used to recover the illuminant. These methods include gamut mapping and random forest, an ensemble machine learning methods and are described in more detail in the Background section. | |||
== Background == | == Background == | ||
Revision as of 04:30, 11 December 2015
Authors: Xuerong Xiao, Jennifer Li
Introduction
Our project aims to estimate the illuminant of a scene using various methods of machine learning with a focus on the deep learning method, convolutional neural networks (CNN). Illumination estimation is an area of interest, because it has applications in topics including image reproduction and image retrieval. For image reproduction, an image may be captured under a certain lighting but rendered under a different lighting. In image retrieval and computer vision, the illumination of the scene needs to be estimated so that it can be accounted for when images of objects are obtained under different lighting.
In past research on illuminant estimation, various methods have been used to recover the illuminant. These methods include gamut mapping and random forest, an ensemble machine learning methods and are described in more detail in the Background section.