title: Class separation improvements in pixel classification using colour injection.
abstract: This paper presents an improvement in the colour image segmentation in the Hue Saturation (HS) sub-space. The authors propose to inject (add) a colour vector in the Red Green Blue (RGB) space to increase the class separation in the HS plane. The goal of the work is the development of an algorithm to obtain the optimal colour vector for injection that maximizes the separation between the classes in the HS plane. The chromatic Chrominace-1 Chrominance-2 sub-space (of the Luminance Chrominace-1 Chrominance-2 (YC(1)C(2)) space) is used to obtain the optimal vector to add. The proposal is applied on each frame of a colour image sequence in real-time. It has been tested in applications with reduced contrast between the colours of the background and the object, and particularly when the size of the object is very small in comparison with the size of the captured scene. Numerous tests have confirmed that this proposal improves the segmentation process, considerably reducing the effects of the variation of the light intensity of the scene. Several tests have been made in skin segmentation in applications for sign language recognition via computer vision, where an accurate segmentation of hands and face is required.
authors.affiliation: Department of Electronics and Electromechanics, Pontificia Universidad Católica Madre y Maestra, 822 Santiago, Dominican Republic. eblanco@pucmmsti.edu.do
authors.forename: Edward
authors.initials: E
authors.lastname: Blanco
authors.forename: Manuel
authors.initials: M
authors.lastname: Mazo
authors.forename: Luis
authors.initials: L
authors.lastname: Bergasa
authors.forename: Sira
authors.initials: S
authors.lastname: Palazuelos
authors.forename: Jose
authors.initials: J
authors.lastname: Rodríguez
authors.forename: Cristina
authors.initials: C
authors.lastname: Losada
authors.forename: Jose
authors.initials: J
authors.lastname: Martín
mesh_terms: D000465:Algorithms; D003116:Color; D007089:Image Enhancement; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008027:Light; D008962:Models, Theoretical; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity