A Computer-Assisted Colorization Approach Based on Efficient Belief Propagation and Graph Matching

Abstract

Abstract. Region-based approaches have been proposed to computerassisted colorization problem, typically using shape similarity and topology relations between regions. Given a colored frame, the objective is to automatically colorize consecutive frames, minimizing the user effort to colorize the remaining regions. We propose a new colorization algorithm based on graph matching, using Belief Propagation to explore the spatial relations between sites through Markov Random Fields. Each frame is represented by a graph with each region being associated to a vertex. A colored frame is chosen as a 'model' and the colors are propagated to uncolored frames by computing a correspondence between regions, exploring the spatial relations between vertices, considering three types of information: adjacency, distance and orientation. Experiments are shown in order to demonstrate the importance of the spatial relations when comparing two graphs with strong deformations and with 'topological' differences.

Motion Estimation from RGB-D Images Using Graph Homomorphism

“…This approach has been used to solve many Computer Vision problems such as interactive natural image segmentation [6], computerassisted colorization [7] and point matching for non-rigid registration [8], among others [9].…”