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Kai Guan

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Proceedings Article | 23 May 2023 Paper

Study on sample noise immunity of dense matching deep learning supervised methods

Hao Zhang, Kai Guan, Fei Jin

Proceedings Volume 12645, 126452J (2023) https://doi.org/10.1117/12.2680850

KEYWORDS: Error analysis, Deep learning, Feature extraction, Data modeling, Image processing

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In recent years, deep learning dense matching techniques have developed rapidly. Among the various training methods such as supervised, unsupervised and semi-supervised, the matching accuracy of supervised training methods is still much higher than other training methods. However, this training method requires the use of manually labelled parallax maps as samples. The accuracy of the labelling will directly affect the matching accuracy of the trained network model. Therefore, it needs to be analyzed and studied. In this paper, the noise immunity of the deep learning supervised method is studied by simulating systematic error, random error and gross error, and the experimental results show that: (i) The deep learning method has anti-noise ability within a certain range and has a better anti-noise effect on random noise, but the matching accuracy decreases at an accelerated rate as the noise increases. (ii) The pre-training method by transfer learning can effectively improve the matching accuracy, increase the noise immunity, and make the original non-converging network converge.

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