An edge detection algorithm using a local distribution

The recently effective edge detection methods have computational complexity. In this paper, a new algorithm is proposed which is simple and fast. First, a set of points in an image is chosen which forms small squares. The value of each point is calculated using a low pass filter. The values of other points can be calculated by using the local linear distribution. It is possible that there is a curve corresponding to each level in any square. Therefore, for each level, one or more closed curves are formed in the full image. These curves are the edges of the image. Now, a set of levels must be chosen so that their corresponding curves produce the desired edges. If a larger number of levels are used, more details will be detected, otherwise only the objects will be recognized. This algorithm is easy to implement and very fast in execution, and can be used in a variety of applications such as normal edge detection, object detection, boundary detection and detecting objects as polygons, this assists in recognizing the shape and calculating the volume, gravity center and etc.