An overview of edge and object contour detection

Abstract

In computer vision, edge and object contour detection is essential for higher-level vision tasks, such as shape matching, visual salience, image segmentation, and object recognition. It has attracted much attention during the past several decades, and many excellent methods have been proposed. In this paper, we make a comprehensive introduction to representative edge and object contour detection methods in the past two decades. Based on the development of these methods, we mainly classify them into two categories: traditional methods and learning-based methods. We further divide traditional methods into local pattern methods, edge grouping methods, active contour models, and bio-inspired methods. Further, we divide learning-based methods into classical learning-based methods and deep learning-based methods. At the same time, we introduce the most popular benchmarks and evaluation measures and quantitatively compare the performances of these promising methods. Moreover, we discuss current challenges in edge and object contour detection and suggest some future trends to bridge gaps with human vision. We believe that this overview will benefit newcomers and promote the development of edge and object contour detection.