@Article{F.JavierSanchez2017,
author="F. Javier Sanchez
and Jorge Bernal
and Cristina Sanchez Montes
and Cristina Rodriguez de Miguel
and Gloria Fernandez Esparrach",
title="Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos",
journal="Machine Vision and Applications",
year="2017",
pages="1--20",
optkeywords="Specular highlights",
optkeywords="bright spot regions segmentation",
optkeywords="region classification",
optkeywords="colonoscopy",
abstract="A novel specular highlights detection method in colonoscopy videos is presented. The method is based on a model of appearance dening specularhighlights as bright spots which are highly contrasted with respect to adjacent regions. Our approach proposes two stages; segmentation, and then classicationof bright spot regions. The former denes a set of candidate regions obtained through a region growing process with local maxima as initial region seeds. This process creates a tree structure which keeps track, at each growing iteration, of the region frontier contrast; nal regions provided depend on restrictions over contrast value. Non-specular regions are ltered through a classication stage performed by a linear SVM classier using model-based features from each region. We introduce a new validation database with more than 25; 000 regions along with their corresponding pixel-wise annotations. We perform a comparative study against other approaches. Results show that our method is superior to other approaches, with our segmented regions beingcloser to actual specular regions in the image. Finally, we also present how our methodology can also be used to obtain an accurate prediction of polyp histology.",
optnote="MV; 600.096; 600.175",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2975), last updated on Thu, 16 Feb 2023 11:56:09 +0100",
opturl="https://link.springer.com/article/10.1007/s00138-017-0864-0",
file=":http://refbase.cvc.uab.es/files/SBS2017.pdf:PDF"
}