Edges as Outliers: Anisotropic Smoothing Using Local Image Statistics
SCALE-SPACE '99 Proceedings of the Second International Conference on Scale-Space Theories in Computer Vision
Active contour on the basis of inertia
Proceedings of the 2004 ACM symposium on Applied computing
Evaluating edge detection through boundary detection
EURASIP Journal on Applied Signal Processing
A New Contour Detection Approach in Mammogram Using Rational Wavelet Filtering and MRF Smoothing
DICTA '07 Proceedings of the 9th Biennial Conference of the Australian Pattern Recognition Society on Digital Image Computing Techniques and Applications
Comparative study of contour detection evaluation criteria based on dissimilarity measures
Journal on Image and Video Processing - Regular
A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Enhancement and Noise Filtering by Use of Local Statistics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour detection based on nonclassical receptive field inhibition
IEEE Transactions on Image Processing
Oriented Speckle Reducing Anisotropic Diffusion
IEEE Transactions on Image Processing
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The constant appearance of new contour detection methods makes it necessary to have accurate ways of assessing the performance of these methods. This paper proposes an evaluation method of contour detectors for noisy images. The method considers the computation of the optimal threshold that produces a greater approximation to the ground truth and the effect produced by the noise. Both analyzed dimensions allow objective comparisons of the performance of contour detectors.