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Learning to Detect Features in Texture Images

Author(s): Zhang, Linguang; Rusinkiewicz, Szymon

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Abstract: Local feature detection is a fundamental task in computer vision, and hand-crafted feature detectors such as SIFT have shown success in applications including image-based localization and registration. Recent work has used features detected in texture images for precise global localization, but is limited by the performance of existing feature detectors on textures, as opposed to natural images. We propose an effective and scalable method for learning feature detectors for textures, which combines an existing "ranking" loss with an efficient fully-convolutional architecture as well as a new training-loss term that maximizes the "peakedness" of the response map. We demonstrate that our detector is more repeatable than existing methods, leading to improvements in a real-world texture-based localization application.
Publication Date: 2018
Citation: Zhang, Linguang, and Szymon Rusinkiewicz. "Learning to Detect Features in Texture Images." In IEEE/CVF Conference on Computer Vision and Pattern Recognition (2018): pp. 6325-6333. doi:10.1109/CVPR.2018.00662
DOI: 10.1109/CVPR.2018.00662
ISSN: 1063-6919
EISSN: 2575-7075
Pages: 6325 - 6333
Type of Material: Conference Article
Journal/Proceeding Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Version: Author's manuscript

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