Multiscale orientation estimation of perceptual boundaries

The dominant orientation at any point, P, in an image is the direction from P in which there is the least gray-level variance. It is often defined by using gradient estimates, but may be extended to employ neighbourhood operators that provide some degree of phase invariance. However, the approach to estimating the dominant orientation at P depends on the scale or size of the (usually) non-trivial neighbourhood being considered. Multiscale PCA-based orientation estimation techniques involve computationally-heavy solving of eigensystems at each scale and location. In contrast, we propose two methods which use a measure of anisotropy to select or weight orientations respectively at different scales in order to provide a single estimate of orientation at any given point. This is believed to be closer to human perception of contour direction. Results are presented for two simple orientation estimation techniques against comparisons with multiscale PCA estimation for human perceptual boundaries.