A new method for detecting data matrix under similarity transform for machine vision applications

Data matrices are widely used in the automotive, aerospace and computer manufacturing industries. In industry, they are used to identify objects used in process control. In this paper, we focus on detecting data matrices where a camera is configured to see it in a perpendicular direction that is typical in machine vision applications. In this case, the image projection can be modeled as a similarity transform. Data matrices are attached or marked by laser on the surface of objects, and have L-shaped solid lines which act as references for decoding. Under a similarity transform, distances from the center of a data matrix to each side of the L-shape are equal. This symmetric property is used to detect a data matrix, and experimental results show the feasibility of the proposed algorithm.