A Comprehensive Review of Image Line Segment Detection and Description: Taxonomies, Comparisons, and Challenges

TL;DR

This paper provides a comprehensive review of image line segment detection and description methods, including taxonomies, comparisons, challenges, and future research directions, to aid researchers in understanding and advancing this field.

Contribution

It introduces detailed taxonomies for detection and description methods, analyzes existing approaches, evaluates state-of-the-art algorithms, and offers insights and challenges to guide future research.

Findings

Taxonomies categorize detection and description methods.

Evaluation of top algorithms provides performance insights.

Identifies key challenges and potential solutions in the field.

Abstract

An image line segment is a fundamental low-level visual feature that delineates straight, slender, and uninterrupted portions of objects and scenarios within images. Detection and description of line segments lay the basis for numerous vision tasks. Although many studies have aimed to detect and describe line segments, a comprehensive review is lacking, obstructing their progress. This study fills the gap by comprehensively reviewing related studies on detecting and describing two-dimensional image line segments to provide researchers with an overall picture and deep understanding. Based on their mechanisms, two taxonomies for line segment detection and description are presented to introduce, analyze, and summarize these studies, facilitating researchers to learn about them quickly and extensively. The key issues, core ideas, advantages and disadvantages of existing methods, and their potential applications for each category are analyzed and summarized, including previously unknown findings. The challenges in existing methods and corresponding insights for potentially solving them are also provided to inspire researchers. In addition, some state-of-the-art line segment detection and description algorithms are evaluated without bias, and the evaluation code will be publicly available. The theoretical analysis, coupled with the experimental results, can guide researchers in selecting the best method for their intended vision applications. Finally, this study provides insights for potentially interesting future research directions to attract more attention from researchers to this field.