Adaptive-Scale Filtering and Feature Detection Using Range Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Resolution Selection Using Generalized Entropies of Multiresolution Histograms
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Significantly Different Textures: A Computational Model of Pre-attentive Texture Segmentation
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
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
Significant edges in the case of non-stationary Gaussian noise
Pattern Recognition
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We devise a statistical framework for edge detection by performing a statistical analysis of zero crossings of the second derivative of an image. This analysis enables us to estimate at each pixel of an image the probability that an edge passes through the pixel. We present a statistical analysis of the Lindeberg operators that we use to compute image derivatives. We also introduce a confidence probability that tells us how reliable the edge probability is, given the image's noise level and the operator's scale. Combining the edge and confidence probabilities leads to a probabilistic scale selection algorithm. We present the results of experiments on natural images.