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ULSD: Unified line segment detection across pinhole, fisheye, and spherical cameras
- Source :
- ISPRS Journal of Photogrammetry and Remote Sensing. 178:187-202
- Publication Year :
- 2021
- Publisher :
- Elsevier BV, 2021.
-
Abstract
- Image line segment detection is a fundamental problem in computer vision and remote sensing. Although numerous state-of-the-art methods have shown great performance for straight line segment detection, line segment detection for distorted images without undistortion is still a challenging problem. Besides, there is a lack of a unified line segment detection framework for both distorted and undistorted images. To address these two problems, we propose a novel learning-based Unified Line Segment Detection method (i.e., ULSD) for distorted and undistorted images in this paper. Specifically, we first propose a novel equipartition point-based Bezier curve representation to model arbitrary distorted line segments. Then the line segment detection is tackled by equipartition point regression with an end-to-end trainable neural network. Consequently, the proposed ULSD is independent of camera distortion parameters and does not need any undistortion preprocessing. In the experiments, the proposed method is firstly evaluated on the pinhole, fisheye, and spherical image datasets, respectively, as well as trained and tested on the mixed dataset with differently distorted images. The experimental results on each distortion model show that the proposed ULSD is more competitive than the state-of-the-art methods for both accuracy and efficiency, especially for the results of the unified model trained on the mixed datasets, thus demonstrating the effectiveness and generality of the proposed ULSD to real-world scenarios. The source code and datasets are available at https://github.com/lh9171338/ULSD-ISPRS.
- Subjects :
- Source code
010504 meteorology & atmospheric sciences
Computer science
media_common.quotation_subject
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
0211 other engineering and technologies
02 engineering and technology
01 natural sciences
Spherical image
Line segment
Preprocessor
Point (geometry)
Computer vision
Computers in Earth Sciences
Engineering (miscellaneous)
021101 geological & geomatics engineering
0105 earth and related environmental sciences
media_common
Artificial neural network
business.industry
Distortion (optics)
Atomic and Molecular Physics, and Optics
Computer Science Applications
Pinhole (optics)
Artificial intelligence
business
Subjects
Details
- ISSN :
- 09242716
- Volume :
- 178
- Database :
- OpenAIRE
- Journal :
- ISPRS Journal of Photogrammetry and Remote Sensing
- Accession number :
- edsair.doi...........bdd1558ead578632e7b47dd843d4e0b0