Adaptive edge detection in a global optimal observation scale

We propose an adaptive edge detection algorithm for LOG operator based on a biological perspective solving the problem of parameter setting. The algorithm can survive in different kinds of images with different imaging qualities. We introduce the concept of Global Optimal Observation Scale that the best scale parameter for LOG lie at the global observation location in scale space. Experimental results demonstrate strong capacity of the algorithm.