Re-se-arch
Our re-se-arch has been generously supported by ARO, NSF, ARFL, IARPA, BlueHalo and Salesforce.
2020
Xue, Nan; Bai, Song; Wang, Fu-Dong; Xia, Gui-Song; Wu, Tianfu; Zhang, Liangpei; Torr, Philip H. S.
Learning Regional Attraction for Line Segment Detection Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2020, ISSN: 0162-8828.
@article{RegionalAttractionLSD,
title = {Learning Regional Attraction for Line Segment Detection},
author = {Nan Xue and Song Bai and Fu-Dong Wang and Gui-Song Xia and Tianfu Wu and Liangpei Zhang and Philip H.S. Torr},
url = {https://arxiv.org/abs/1912.09344},
doi = {10.1109/TPAMI.2019.2958642},
issn = {0162-8828},
year = {2020},
date = {2020-01-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
abstract = {This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.
2019
Xue, Nan; Bai, Song; Wang, Fudong; Xia, Gui-Song; Wu, Tianfu; Zhang, Liangpei
Learning Attraction Field Representation for Robust Line Segment Detection Proceedings Article
In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
@inproceedings{AFM_LSD,
title = {Learning Attraction Field Representation for Robust Line Segment Detection},
author = {Nan Xue and Song Bai and Fudong Wang and Gui-Song Xia and Tianfu Wu and Liangpei Zhang},
url = {https://arxiv.org/abs/1812.02122
https://github.com/cherubicXN/afm_cvpr2019},
year = {2019},
date = {2019-06-18},
booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
abstract = {This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three components: (i) A region-partition map in which every pixel is assigned to one and only one line segment; (ii) An attraction field map in which every pixel in a partition region is encoded by its 2D projection vector w.r.t. the associated line segment; and (iii) A squeeze module which squashes the attraction field to a line segment map that almost perfectly recovers the input one. By leveraging the duality, we learn ConvNets to compute the attraction field maps for raw in-put images, followed by the squeeze module for LSD, in an end-to-end manner. Our method rigorously addresses several challenges in LSD such as local ambiguity and class imbalance. Our method also harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution. In experiments, our method is tested on the WireFrame dataset and the YorkUrban dataset with state-of-the-art performance obtained. Especially, we advance the performance by 4.5 percents on the WireFrame dataset. Our method is also fast with 6.6~10.4 FPS, outperforming most of existing line segment detectors.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This paper presents a region-partition based attraction field dual representation for line segment maps, and thus poses the problem of line segment detection (LSD) as the region coloring problem. The latter is then addressed by learning deep convolutional neural networks (ConvNets) for accuracy, robustness and efficiency. For a 2D line segment map, our dual representation consists of three components: (i) A region-partition map in which every pixel is assigned to one and only one line segment; (ii) An attraction field map in which every pixel in a partition region is encoded by its 2D projection vector w.r.t. the associated line segment; and (iii) A squeeze module which squashes the attraction field to a line segment map that almost perfectly recovers the input one. By leveraging the duality, we learn ConvNets to compute the attraction field maps for raw in-put images, followed by the squeeze module for LSD, in an end-to-end manner. Our method rigorously addresses several challenges in LSD such as local ambiguity and class imbalance. Our method also harnesses the best practices developed in ConvNets based semantic segmentation methods such as the encoder-decoder architecture and the a-trous convolution. In experiments, our method is tested on the WireFrame dataset and the YorkUrban dataset with state-of-the-art performance obtained. Especially, we advance the performance by 4.5 percents on the WireFrame dataset. Our method is also fast with 6.6~10.4 FPS, outperforming most of existing line segment detectors.