A Computational Approach to Edge Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Logical/Linear Operators for Image Curves
IEEE Transactions on Pattern Analysis and Machine Intelligence
ISCV '95 Proceedings of the International Symposium on Computer Vision
On the Quantitative Evaluation of Edge Detection Schemes and their Comparison with Human Performance
IEEE Transactions on Computers
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This paper aims at describing a new framework which allows for the quantitative combination of different edge detectors based on the correspondence between the outcomes of a preselected set of operators. This is inspired from the problem that despite the enormous amount of literature on edge detection techniques, there is no single one that performs well in every possible image context. The so called Kappa Statistics are employed in a novel fashion to enable a sound performance evaluation of the edge maps emerged from different parameter specifications. The proposed method is unique in the sense that the balance between the false detections (False Positives and False Negatives) is explicitly assessed in advanced and incorporated in the estimation of the optimum threshold. Results of this technique are demonstrated and compared to individual edge detection methods.