Analysis of ROC for Edge Detectors
This work addresses performance evaluation issues for edge detection in computer vision, but it is incremental as it focuses on customizing existing methods for better ROC analysis.
The paper tackled the problem of evaluating edge detectors using ROC analysis on the BIPED dataset, finding challenges for filters like Laplacian and Canny, and introduced customization techniques that improved results for more accurate assessment.
This paper presents an evaluation of edge detectors using receiver operating characteristic (ROC) analysis on the BIPED dataset. Our study examines the benefits and drawbacks of applying this technique in Matlab. We observed that while ROC analysis is suitable for certain edge filters, but for filters such as Laplacian, Laplacian of Gaussian, and Canny, it presents challenges when accurately measuring their performance using ROC metrics. To address this issue, we introduce customization techniques to enhance the performance of these filters, enabling more accurate evaluation. Through our customization efforts, we achieved improved results, ultimately facilitating a comprehensive assessment of the edge detectors.