title: Robust rotation-invariant texture classification using a model based approach.
abstract: In this paper, a model based texture classification procedure is presented. The texture is modeled as the output of a linear system driven by a binary image. This latter retains the morphological characteristics of the texture and it is specified by its spatial autocorrelation function (ACF). We show that features extracted from the ACF of the binary excitation suffice to represent the texture for classification purposes. Specifically, we employ a moment invariants based technique to classify the ACF. The resulting proposed classification procedure is thus inherently rotation invariant. Moreover, it is robust with respect to additive noise. Experimental results show that this approach allows obtaining high correct rotation-invariant classification rates while containing the size of the feature space.
authors.affiliation: Dip. Elettronica Applicata, Universitá degli Studi di Roma Tre, 1-00146 Roma, Italy. campisi@uniroma3.it
authors.forename: Patrizio
authors.initials: P
authors.lastname: Campisi
authors.forename: Alessandro
authors.initials: A
authors.lastname: Neri
authors.forename: Gianpiero
authors.initials: G
authors.lastname: Panci
authors.forename: Gaetano
authors.initials: G
authors.lastname: Scarano
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D003196:Computer Graphics; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012399:Rotation; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted