Performance Evaluation of2D Object Recognition Techniques

We propose an empirical performance evaluation of six different 2D object recognition techniques. For this purpose, two novel recognition methods that we have developed with the aim to fulfill increasing industrial demands are compared to the normalized cross correlation, the sum of absolute differences, and the Hausdorff distance as three standard similarity measures in industrial applications, as well as to PatMax R © — an object recognition tool developed by Cognex. Additionally, a new method for refining the object’s pose based on a least-squares adjustment is included in our analysis. After a description of the respective methods, several criteria that allow an objective evaluation of object recognition approaches are introduced . Experiments on real images are used to apply the proposed criteria. The experimental set-up used for the evaluation measurements is explained in detail. The results are illustrated and analyzed extensively.