Model-independent estimation of local blur and application to edge detection

Precise and efficient object boundary detection is the key for successful accomplishment of many imaging applications involving object segmentation or recognition. Blur-scale at a given image location represents the transition-width of the local object interface. Hence, the knowledge of blur-scale is crucial for accurate edge detection and object segmentation. In this thesis, we present new theory and algorithms for computing local blur-scales and apply it for scale-based gradient computation and edge detection. The new blur-scale computation method is based on our observation that gradients inside a blur-scale region follow a Gaussian distribution with non-zero mean. New statistical criteria using maximal likelihood functions are established and applied for local blur-scale computation. Gradient vectors over a blur-scale region are summed to enhance gradients at blurred object interfaces while leaving gradients at sharp transitions unaffected. Finally, a blur-scale based non-maxima suppression method is developed for edge detection. The method has been applied to both natural and phantom images. Experimental results show that computed blur-scales capture true blur extents at individual image locations. Also, the new scale-based gradient computation and edge detection algorithms successfully detect gradients and edges, especially at the blurred object interfaces.