Estimating Illumination Chromaticity via Support Vector Regression
نویسندگان
چکیده
The technique of support vector regression is applied to the problem of estimating the chromaticity of the light illuminating a scene from a color histogram of an image of the scene. Illumination estimation is fundamental to white balancing digital color images and to understanding human color constancy. Under controlled experimental conditions, the support vector method is shown to perform better than the neural network and color by correlation methods.
منابع مشابه
Recovery of Chromaticity Image Free from Shadows via Illumination Invariance
A recent method for recovering a greyscale image that is free from shadow effects is extended such that the recovered image is a colour image, in the sense that 2-dimensional chromaticity information is recovered. First, the effect of lighting change, and thus to a large degree shadowing, is removed by projecting logarithms of 2D colour band-ratio chromaticities into a direction that is indepen...
متن کاملReducing Worst-Case Illumination Estimates for Better Automatic White Balance
Automatic white balancing works quite well on average, but seriously fails some of the time. These failures lead to completely unacceptable images. Can the number, or severity, of these failures be reduced, perhaps at the expense of slightly poorer white balancing on average, with the overall goal being to increase the overall acceptability of a collection of images? Since the main source of er...
متن کاملEstimating the scene illumination chromaticity by using a neural network.
A neural network can learn color constancy, defined here as the ability to estimate the chromaticity of a scene's overall illumination. We describe a multilayer neural network that is able to recover the illumination chromaticity given only an image of the scene. The network is previously trained by being presented with a set of images of scenes and the chromaticities of the corresponding scene...
متن کاملPrediction of daily evaporation using hybrid support vector regression-firefly optimization algorithm and multilayer perceptron
Prediction of daily evaporation is a valuable and determinant tool in sustainable agriculture and hydrological issues, especially in the design and management of water resources systems. Therefore, in this study, the ability of artificial intelligence models of multi-layer perceptron (MLP), support vector regression (SVR), and the hybrid model of support vector regression-firefly optimization a...
متن کاملApplication of Gene Expression Programming and Support Vector Regression models to Modeling and Prediction Monthly precipitation
Estimating and predicting precipitation and achieving its runoff play an important role to correct management and exploitation of basins, management of dams and reservoirs, minimizing the flood damages and droughts, and water resource management, so they are considered by hydrologists. The appropriate performance of intelligent models leads researchers to use them for predicting hydrological ph...
متن کامل