A Local Thresholding Algorithm for Image Segmentation by Using Gradient Orientation Histogram

This paper proposes a new local thresholding method to further explore the relationship between gradients and image patterns. In most studies, the image gradient histogram is simply divided into K bins that have the same intervals in angular space. This kind of empirical approaches may not fully capture the correlation information between pixels. In this paper, a variance-based idea is applied to the gradient orientation histogram. It clusters pixels into subsets with different angular intervals. Analyzing these subsets with similar common patterns respectively will help to assist in achieving the optimal thresholds for image segmentation. For the result assessments, the proposed algorithm is compared with other 1-D and 2-D histogram-based thresholding methods, as well as hybrid local–global thresholding methods. It is shown that the proposed algorithm can effectively recognize the common features of the images that belong to the same category, and maintain the stable performances when the number of thresholds increases. Furthermore, the processing time of the present algorithm is competitive with those of other algorithms, which shows the potential application in real-time scenes.