On the performance characterisation of image segmentation algorithms: A case study

Abstract

An experimental vehicle is being developed for the purposes of precise crop treatment, with the aim of reducing chemical use and thereby improving quality and reducing both costs and environmental contamination. For differential treatment of crop and weed, the vehicle must discriminate between crop, weed and soil. We present a two stage algorithm designed for this purpose, and use this algorithm to illustrate how empirical discrepancy methods, notably the analysis of type I and type II statistical errors and receiver operating characteristic curves, may be used to compare algorithm performance over a set of test images which represent typical working conditions for the vehicle. Analysis of performance is presented for the two stages of the algorithm separately, and also for the combined algorithm. This analysis allows us to understand the effects of various types of misclassification error on the overall algorithm performance, and as such is a valuable methodology for computer vision engineers.