Edge Detection for Pattern Recognition: A Survey

TL;DR

This survey reviews edge detection methods inspired by human vision, highlighting their importance in pattern recognition and recent research trends across neuroscience and computer vision.

Contribution

It categorizes edge detectors into features, gradients, and sketch models, providing a comprehensive overview of their applications in pattern recognition.

Findings

Increase in research utilizing edge features in computer vision

Edge detection is fundamental for high-level visual intelligence

Crossdisciplinary approaches enhance edge detection methods

Abstract

This review provides an overview of the literature on the edge detection methods for pattern recognition that inspire from the understanding of human vision. We note that edge detection is one of the most fundamental process within the low level vision and provides the basis for the higher level visual intelligence in primates. The recognition of the patterns within the images relate closely to the spatiotemporal processes of edge formations, and its implementation needs a crossdisciplanry approach in neuroscience, computing and pattern recognition. In this review, the edge detectors are grouped in as edge features, gradients and sketch models, and some example applications are provided for reference. We note a significant increase in the amount of published research in the last decade that utilizes edge features in a wide range of problems in computer vision and image understanding having a direct implication to pattern recognition with images.