[IPOL announce] new article: Line Segment Detection: a Review of the 2022 State of the Art
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Wed Feb 28 12:32:06 CET 2024
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.
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