[IPOL announce] new article: Line Segment Detection: a Review of the 2022 State of the Art

[announcements about the IPOL journal]

A new article is available in IPOL: https://www.ipol.im/pub/art/2024/481/

Thibaud Ehret, and Jean-Michel Morel,
Line Segment Detection: a Review of the 2022 State of the Art,
Image Processing On Line, 14 (2024), pp. 41–63.
https://doi.org/10.5201/ipol.2024.481

Abstract
We compare nine line segment detectors. The two more ancient ones are 
based on classical edge growing followed by a statistically founded 
validation. The next six are very recent and based on supervised deep 
learning. These six deep learning methods train and validate their 
neural network on two datasets ('YorkUrban', 'Wireframe'); most of them 
compared their results with the now classic LSD (Line Segment Detector) 
and EDlines, and get a better performance than them on these datasets. 
The ninth paper combines deep learning and classical edge growing to 
achieve a purely non-supervised method. The seven machine learning based 
detectors and EDlines are described here. LSD and EDlines are 
parameter-free, fixed to allow for one false alarm on average. Our 
experiments show that the six purely ML based line segment detectors 
show a significant variability to their end-parameters, leading to 
apparent missed or irrelevant detection. We also compared all nine 
detectors on two images: one clearly "in domain" for the 'Wireframe' 
dataset, and the other one slightly out of domain. A quantitative 
comparison would be fallacious. Indeed, while differing in their search 
strategy, the statistical detectors share a very similar definition and 
decision threshold for line segments. The purely ML-based detectors have 
learned from human annotators that were directed at reconstructing 
architectures as wireframes. Hence, these algorithms aim at a different 
goal, the architectural interpretation of the scene. Yet, several of 
them have more complete goals than just line segment detection. Indeed, 
several of them also associate to each segment a descriptor, and aim at 
making the pair segment+descriptor fit for image matching. The readers 
are invited to judge by themselves about the advantages and drawbacks of 
all methods by submitting their own images to the online demos 
associated with the present paper.