Plotting ROC Curves for Edge Detection Performance Evaluation in MATLAB

In the MATLAB environment, we can utilize various plotting functions to generate ROC (Receiver Operating Characteristic) curves for evaluating edge detection performance. ROC curves serve as fundamental performance assessment tools that illustrate the trade-off between detection accuracy and recall rate in edge detection algorithms. The implementation typically involves comparing ground truth edge maps with algorithm-detected edges using metrics like true positive rate and false positive rate across varying threshold values. Through MATLAB's image processing toolbox functions such as edge() for detection and perfcurve() for metric calculation, we can systematically visualize how edge detection algorithms perform under different sensitivity thresholds. This visualization enables researchers to observe performance variations quantitatively and select optimal thresholds for achieving superior edge detection results. Therefore, generating ROC curves constitutes a critical step in comprehensive edge detection performance evaluation workflows.