title: Reduced complexity rotation invariant texture classification using a blind deconvolution approach.
abstract: In this paper, we present a texture classification procedure that makes use of a blind deconvolution approach. Specifically, the texture is modeled as the output of a linear system driven by a binary excitation. We show that features computed from one-dimensional slices extracted from the two-dimensional autocorrelation function (ACF) of the binary excitation allows representing the texture for rotation-invariant classification purposes. The two-dimensional classification problem is thus reconduced to a more simple one-dimensional one, which leads to a significant reduction of the classification procedure computational complexity.
authors.affiliation: Dipartimento Elettronica Applicata, Università degli Studi Roma Tre, via Della Vasca Navale 84, 100146 Roma, Italy. campisi@uniroma3.it
authors.forename: Patrizio
authors.initials: P
authors.lastname: Campisi
authors.forename: Stefania
authors.initials: S
authors.lastname: Colonnese
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; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D012399:Rotation; D013223:Statistics as Topic