title: Automatic color based reassembly of fragmented images and paintings.
abstract: The problem of reassembling image fragments arises in many scientific fields, such as forensics and archaeology. In the field of archaeology, the pictorial excavation findings are almost always in the form of painting fragments. The manual execution of this task is very difficult, as it requires great amount of time, skill and effort. Thus, the automation of such a work is very important and can lead to faster, more efficient, painting reassembly and to a significant reduction in the human effort involved. In this paper, an integrated method for automatic color based 2-D image fragment reassembly is presented. The proposed 2-D reassembly technique is divided into four steps. Initially, the image fragments which are probably spatially adjacent, are identified utilizing techniques employed in content based image retrieval systems. The second operation is to identify the matching contour segments for every retained couple of image fragments, via a dynamic programming technique. The next step is to identify the optimal transformation in order to align the matching contour segments. Many registration techniques have been evaluated to this end. Finally, the overall image is reassembled from its properly aligned fragments. This is achieved via a novel algorithm, which exploits the alignment angles found during the previous step. In each stage, the most robust algorithms having the best performance are investigated and their results are fed to the next step. We have experimented with the proposed method using digitally scanned images of actual torn pieces of paper image prints and we produced very satisfactory reassembly results.
authors.affiliation: Aristotle University of Thessaloniki, Thessaloniki, 54124, Greece. etsamour@csd.auth.gr
authors.forename: Efthymia
authors.initials: E
authors.lastname: Tsamoura
authors.forename: Ioannis
authors.initials: I
authors.lastname: Pitas
mesh_terms: D000465:Algorithms; D001106:Archaeology; D003116:Color; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016011:Normal Distribution; D010151:Paintings