A Probabilistic Approach for Color Correction in Image Mosaicking Applications
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Image mosaicking, also known as image compositing or stitching, is defined as the problem of combining two or more partially overlapping images of a scene to produce a new picture, usually of higher resolution and with a wider field of view of the scene. Originally, image mosaicking was used for combining aerial photographs. Existing methods deal with deal with color correction, most involve strong assumptions, which are in general, difficult to fulfill in complex environments. In this project the system proposed a probabilistic color correction algorithm for correcting the photometrical disparities. This project proposes a new color correction algorithm that presents several technical novelties. To assess if the parametric nature of the proposed approach contributes to an improvement over a baseline nonparametric method. To provide an estimation for this mapping function, denoted as color palette mapping function. To propose one or several color palette mapping functions. To enforce a smooth color palette mapping function. To model color distribution in images. A probabilistic color correction algorithm for correcting the photometrical disparities. First, the image to be color corrected is segmented into several regions using mean shift. Then, connected regions are extracted using a region fusion algorithm. Local joint image histograms of each region are modeled as collections of truncated Gaussians using a maximum likelihood estimation procedure. Then, local color palette mapping functions are computed using these sets of Gaussians. The color correction is performed by applying those functions to all the regions of the image.
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