We present a novel approach for automatically building image-specific extrapolative spatial models of non-boundary local energy that can be used to perform local statistical tests to detect perceptual boundaries. The non-boundary model consists of statistics of local energy that are spatially extrapolated by a non-boundary confidence map and a scale-adaptive normalised filtering algorithm. We exploit the flexibility of steerable filters to both extract oriented local energy and to provide local statistics of the energy distribution in the orientation-domain to compute the non-boundary confidence map. Finally, we apply our local thresholding technique separately to the three channels of colour images and adopt a max operator to combine the results. We provide a qualitative and quantitative comparison on real images from a hand-segmented natural image database against the best combination of the most widely cited colour edge detectors and automatic global thresholding methods.
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Ng, J., Bharath, A.A. & Kin, P.C.P. Extrapolative Spatial Models for Detecting Perceptual Boundaries in Colour Images. Int J Comput Vision 73, 179–194 (2007). https://doi.org/10.1007/s11263-006-9782-8
- edge detection
- boundary detection
- extrapolative spatial model
- steerable filter