title: Local image registration by adaptive filtering.
abstract: We propose a new adaptive filtering framework for local image registration, which compensates for the effect of local distortions/displacements without explicitly estimating a distortion/displacement field. To this effect, we formulate local image registration as a two-dimensional (2-D) system identification problem with spatially varying system parameters. We utilize a 2-D adaptive filtering framework to identify the locally varying system parameters, where a new block adaptive filtering scheme is introduced. We discuss the conditions under which the adaptive filter coefficients conform to a local displacement vector at each pixel. Experimental results demonstrate that the proposed 2-D adaptive filtering framework is very successful in modeling and compensation of both local distortions, such as Stirmark attacks, and local motion, such as in the presence of a parallax field. In particular, we show that the proposed method can provide image registration to: a) enable reliable detection of watermarks following a Stirmark attack in nonblind detection scenarios, b) compensate for lens distortions, and c) align multiview images with nonparametric local motion.
authors.affiliation: Electrical and Computer Engineering Department, University of Rochester, Rochester, NY 14627-0126, USA. caner@ece.rochester.edu
authors.forename: Gulcin
authors.initials: G
authors.lastname: Caner
authors.forename: A Murat
authors.initials: AM
authors.lastname: Tekalp
authors.forename: Gaurav
authors.initials: G
authors.lastname: Sharma
authors.forename: Wendi
authors.initials: W
authors.lastname: Heinzelman
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods*; D013382:Subtraction Technique