Edge Detection Using Dynamic Optimal Partitioning

In this paper, a new edge detector (boundary extractor) is proposed based on finding major change points in a local one-dimensional window of the image intensity values of the rows or columns. The approach amounts to separating the pixels in the window into sets or regions of constant intensities with the edge pixels providing transition points. The edge points are found based on partitioning the interval in an optimal way using dynamic programming with an appropriate cost function. Different cost functions are introduced for the algorithm with simulation results that show the detector´s effectiveness even in the presence of noise