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14 August 2019 An improved self-supervised framework for feature point detection
Yunhui Wu, Jun li
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Proceedings Volume 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019); 1117927 (2019) https://doi.org/10.1117/12.2540147
Event: Eleventh International Conference on Digital Image Processing (ICDIP 2019), 2019, Guangzhou, China
Abstract
The robust image feature point is a critical component of image matching. In order to detect feature points that are robust to illumination changes and viewpoint changes, an improved self-supervised learning framework for feature point detector is proposed. Firstly, feature point detector is trained in simple synthetic datasets. Then, the labeled dataset is generated by applying the Homographic Adaptation to automatically label the unlabeled area image. Finally, the full convolution network is trained with the labeled dataset. In this paper, the convolutional neural network in the selfsupervised learning framework is improved, mainly by increasing the number of layers of the neural network, from the original 8 layers to 11 layers. Experiments on the HPatches dataset show that the improved self-supervised feature point detector has achieved good results
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Yunhui Wu and Jun li "An improved self-supervised framework for feature point detection", Proc. SPIE 11179, Eleventh International Conference on Digital Image Processing (ICDIP 2019), 1117927 (14 August 2019); https://doi.org/10.1117/12.2540147
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KEYWORDS
Detection and tracking algorithms
Convolution
Convolutional neural networks
Image retrieval
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