Color-based perceptual organization for rotation and scale-invariant pattern matching

摘要

We describe an application of color-based pattern matching, where a real-time vision system needs to detect and exactly localize textile patterns woven into carpet flooring material. These patterns are distributed on a large web in a periodic fashion. The task to be solved is recognition of these patterns by matching them with stored prototypes, computing the exact location and use this information to guide a cutting machine to produce perfect replica of desired tiles. The pattern matching part is challenging because of the presence of distortion, scaling, and rotation of the 2D patterns, and rather high demands on the localization accuracy. Also, the task needs to be solved under real-time constraints. We describe the building blocks used in our system. These are color-based segmentation of the patterns to achieve 2D representation in a graph-like manner, followed by graph-based matching. This block solved the graph-isomorphism problem in real-time tolerating distortions, additions, deletions, rotation, translation, and scale variations between the trained and tested versions of the patterns. We demonstrate the concept showing example images and matching results.