A comprehensive review of contour extraction models: classical and AI-based approaches

Abstract

Contour extraction is a core task in computer vision, serving as the foundation for object detection, segmentation, and scene understanding across various applications, including autonomous vehicles, medical imaging, and industrial automation. This paper provides an in-depth review of both classical and modern artificial intelligence (AI)-based contour extraction models. The classical methods, such as edge detectors and gradient-based operators, alongside advanced AI models, including convolutional neural networks and semantic segmentation architectures are reviewed in it. By examining each model’s strengths, limitations, and applicability to diverse real-world tasks, we aim to provide a comprehensive guide to contour extraction, highlight key challenges, and propose potential research directions in this evolving field.

Keywords

• contour extraction
• Canny
• U-Net
• Holistically-Nested Edge Detection (HED)
• ContourNet
• Mask R-CNN