Feature Extraction of Rich Texture Document

We describe here an efficient algorithm for re-assembling one or more unknown objects that have been broken or torn into a large number N of irregular fragments. The puzzle assembly problem has many application areas such as restoration and reconstruction of archeological findings, repairing of broken objects, solving jigsaw type puzzles, molecular docking problem, etc. The pieces usually include not only geometrical shape information but also visual information such as texture, color, and continuity of lines. This paper presents a new approach to the puzzle assembly problem that is based on using textural features and geometrical constraints. The texture of a band outside the border of pieces is predicted by in-painting and texture synthesis methods. Feature values are derived from these original and predicted images of pieces. An affinity measure of corresponding pieces is defined and alignment of the puzzle pieces is carried out. The optimization of total affinity gives the best assembly of puzzle. Experimental results are presented on real and artificial data sets.