This article presents an implementation of the FTC (Fine-to-Coarse) algorithm for histogram segmentation, presented by Delon et al. in 2007. This algorithm uses a non-parametric a contrario approach to segment a 1D histogram into its meaningful modes. We describe also how the method may be applied to the hue, saturation and intensity histograms of color images in order to automatically extract their more representative colors, the so-called color palette. The algorithm for color palette extraction described in this paper is based on the one first published in 2007 by Delon et al., with an improvement that affects low-saturated colors. Several results illustrate the effectiveness of the algorithm.
This article describes an experimental procedure to analyze (and verify) the self-similarity concept in natural images and to explore the Gaussianity of groups of similar patches extracted from a single image. The self-similarity assumption means that most image patches of a sufficient size are repeated, of course not identically, but with small variations. The procedure proposed in this paper, and implemented in the accompanying online demo, permits to explore and visualize these clusters of similar patches in a given image. Thanks to it, a user can select a patch in an image, group all patches similar to it up to a translation, or to an isometry, apply PCA to the group, make visual tests about the Gaussianity of the set of patches, and finally apply EM to the set to see if it is a mixture of Gaussians. Source CodeThe reviewed and documented source code and an online demo are available at the web page of this article 1 . Compilation and usage instructions are included in the README.txt file of the archive.
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