Determining Hysteresis Thresholds for Edge Detection by Combining the Advantages and Disadvantages of Thresholding Methods

Abstract

Hysteresis is an important technique for edge detection, but the unsupervised determination of its parameters is not an easy problem. In this paper, we propose a method for unsupervised determination of hysteresis thresholds using the advantages and disadvantages of two thresholding methods. The basic idea of our method is to look for the best hysteresis thresholds in a set of candidates. First, the method finds a subset and a overset of the unknown edge points set. Then, it determines the best edge map with the measure chi(2). Compared with a general method to determine the parameters of an edge detector, our method performs well and is less computationally complex. The basic idea of our method can be generalized to other pattern recognition problems.

A novel method to look for the hysteresis thresholds for the Canny edge detector

“…Thus, if a candidates set for hysteresis thresholds is known (for example, by using the method proposed in [19]), this method is a fully unsupervised method. More details on this method and its performance are shown in [21,23].…”

A New Method for Edge Extraction in Images using Local Form Factors

“…The BEM proposed in [21] has been considered in several research works for measuring errors in detection and localization. This index is devoid of the shortcomings prevailed in the earlier two [25], [31].…”

“…The metric is expressed as follow - [21], [25][26], [31]. In the present article we choose to use 2 w as the performance index for the proposed edge detector.…”

Generation of fuzzy edge images using trapezoidal membership functions

“…Very often the binarization is usually carried out in an supervised way [3], being an exception some of the methods based on single [24,18] or double thresholds [29,19,30]. By using these unsupervised methods the impact of the adjustments of α and β are hard to predict.…”