The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection
Abstract
Contour detection serves as the basis of a variety of computer vision tasks such as image segmentation and object recognition. The mainstream works to address this problem focus on designing engineered gradient features. In this work, we show that contour detection accuracy can be improved by instead making the use of the deep features learned from convolutional neural networks (CNNs). While rather than using the networks as a blackbox feature extractor, we customize the training strategy by partitioning contour (positive) data into subclasses and fitting each subclass by different model parameters. A new loss function, named positive-sharing loss, in which each subclass shares the loss for the whole positive class, is proposed to learn the parameters. Compared to the sofmax loss function, the proposed one, introduces an extra regularizer to emphasizes the losses for the positive and negative classes, which facilitates to explore more discriminative features. Our experimental results demonstrate that learned deep features can achieve top performance on Berkeley Segmentation Dataset and Benchmark (BSDS500) and obtain competitive cross dataset generalization result on the NYUD dataset.
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Larsson, G., Maire, M., & Shakhnarovich, G. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_35 · Cited by 946
Holistically-nested edge detection
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Kokkinos, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.579 · Cited by 657
Portable System for Box Volume Measurement Based on Line-Structured Light Vision and Deep Learning
Peng, T., Zhang, Z., Song, Y., Chen, F., & Zeng, D. (2019) · Sensors · doi:10.3390/s19183921 · Cited by 20
Self-citationWeakly- and Semi-Supervised Fast Region-Based CNN for Object Detection
Wang, X.-G., Wang, J.-S., Tang, P., & Liu, W.-Y. (2019) · Journal of Computer Science and Technology · doi:10.1007/s11390-019-1975-z · Cited by 8
Self-citation
Metadata
- Authors
- Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z.
- Venue
- 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2015-06
- Pages
- 3982-3991
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W1910619957
- Cited by
- 515
- References
- 15
- FWCI
- 24.80
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Shen, W., Wang, X., Wang, Y., Bai, X., & Zhang, Z. (2015). DeepContour: A deep convolutional feature learned by positive-sharing loss for contour detection. 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2015.7299024
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