원문정보
초록
영어
The main problem with existing edge detection techniques is that they have many limitations in detecting edges for complex and diverse images that exist in the real world. This is because only edges of a defined shape are discovered based on an accurate definition of the edge. One of the methods to solve this problem is the cost minimization method. In the cost minimization method, cost elements and cost functions are defined and used. The cost function calculates the cost for the candidate edge model generated according to the candidate edge generation strategy, and if the cost is found to be satisfactory, the candidate edge model becomes the edge for the image. In this study, we proposed an enhanced candidate edge generation strategy to discover edges for more diverse types of images in order to improve the shortcoming of the cost minimization method, which is that it only discovers edges of a defined type. As a result, improved edge detection results were confirmed.
목차
1. Introduction
2. Edge Detection Method
3. Cost Minimization Method
3.1 Improving Dissimilarity
3.2 Cost Function
3.3 Candidate Edge Model Generation Strategy
4. Performance Evaluation
5. Conclusions
Acknowledgement
References