Efficient Edge Detection Method for Focused Images

In many areas of image processing, we deal with focused images. Indeed, the most important object is focused and the background is smooth. Finding edges in such images is difficult, since state-of-the-art edge detection methods assume that edges should be sharp. In this way, smooth edges are not detected. Therefore, these methods can detect the main object edges that skip the background. However, we are often also interested in detecting the background as well. Therefore, in this paper, we propose an edge detection method that can efficiently detect the edges of both a focused object and a smooth background alike. The proposed method is based on the local use of the k-Means algorithm from Machine Learning (ML). The local use is introduced by the proposed enhanced image filtering. The k-Means algorithm is applied within a sliding window in such a way that, as a result of filtering, we obtain a given square image area instead of just a simple pixel like in classical filtering. The results of the proposed edge detection method were compared with the best represented methods of different approaches of edge detection like pointwise, geometrical, and ML-based ones.