title: A nonparametric approach for histogram segmentation.
abstract: In this work, we propose a method to segment a 1-D histogram without a priori assumptions about the underlying density function. Our approach considers a rigorous definition of an admissible segmentation, avoiding over and under segmentation problems. A fast algorithm leading to such a segmentation is proposed. The approach is tested both with synthetic and real data. An application to the segmentation of written documents is also presented. We shall see that this application requires the detection of very small histogram modes, which can be accurately detected with the proposed method.
authors.affiliation: CNRS, Té1écom, Paris 75015, France. julie.delon@enst.fr
authors.forename: Julie
authors.initials: J
authors.lastname: Delon
authors.forename: Agnès
authors.initials: A
authors.lastname: Desolneux
authors.forename: José-Luis
authors.initials: JL
authors.lastname: Lisani
authors.forename: Ana Belén
authors.initials: AB
authors.lastname: Petro
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*