Extrapolative spatial models for detecting perceptual boundaries in colour images
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.
extrapolative spatial model steerable filter edge detection boundary detection phase-invariance