KPCC: hard example mining of dense analogs in fisheye lens

Jia Xiao; Min Zeng

doi:10.1117/12.3029015

22 May 2024 KPCC: hard example mining of dense analogs in fisheye lens

Jia Xiao, Min Zeng

Proceedings Volume 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023); 131762A (2024) https://doi.org/10.1117/12.3029015
Event: Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 2023, Hangzhou, China

Abstract

In recent years, there has been a substantial rise in the utilization of fisheye lenses, which offer a wide field-of-view. However, the distortion inherent in these lenses presents a major challenge for intelligent recognition of dense analogs (IRDA) in applications based on convolutional neural network (CNN). To enhance the accuracy of IRDA, we introduce a novel algorithm called Key Point Calibrating and Clustering (KPCC), which is based on an equidistant projection model. Our method can fully mine hard examples and effectively correct their misclassifications predicted by the CNN, thereby significantly improving the accuracy of IRDA.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jia Xiao and Min Zeng "KPCC: hard example mining of dense analogs in fisheye lens", Proc. SPIE 13176, Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023), 131762A (22 May 2024); https://doi.org/10.1117/12.3029015

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