A fast edge detection algorithm using binary labels
Date of Issue2015
School of Physical and Mathematical Sciences
Edge detection (for both open and closed edges) from real images is a challenging problem. Developing fast algorithms with good accuracy and stability for noisy images is difficult yet and in demand. In this work, we present a variational model which is related to the well-known Mumford-Shah functional and design fast numerical methods to solve this new model through a binary labeling processing. A pre-smoothing step is implemented for the model, which enhances the accuracy of detection. Ample numerical experiments on grey-scale as well as color images are provided. The efficiency and accuracy of the model and the proposed minimization algorithms are demonstrated through comparing it with some existing methodologies.
Inverse problems and imaging
© 2015 American Institute of Mathematical Sciences. This paper was published in Inverse Problems and Imaging and is made available as an electronic reprint (preprint) with permission of American Institute of Mathematical Sciences. The paper can be found at the following official DOI: [http://dx.doi.org/10.3934/ipi.2015.9.551]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.