Object mark segmentation algorithm using Dynamic Programming for poor quality images in automated inspection process

Abstract

This paper presents a method to segment object ID (identification) marks on poor quality images under uncontrolled lighting conditions of automated inspection process. The method is based on multiple templates and normalized gray-level correlation (NGC) method. We propose a multiple template method, called as ATM (Active Template Model) which uses a search technique of multiple templates from model templates to match and segment character regions of the inspection images. Conventional Snakes algorithm provides a good methodology to model the functional of ATM. To increase the computation speed to segment the ID mark regions, we introduce the Dynamic Programming based algorithm. Experimental results using real images from automated factory are presented. © Springer-Verlag Berlin Heidelberg 2004.