A Robust Roll Angle Estimation Algorithm Based on Gradient Descent. Fan, R., Wang, L., Liu, M., & Pitas, I. In 2019 27th European Signal Processing Conference (EUSIPCO), pages 1-5, Sep., 2019. Paper doi abstract bibtex This paper introduces a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense subpixel disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the presented roll angle estimation method takes fewer iterations to achieve the same precision as the previous method.
@InProceedings{8903049,
author = {R. Fan and L. Wang and M. Liu and I. Pitas},
booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
title = {A Robust Roll Angle Estimation Algorithm Based on Gradient Descent},
year = {2019},
pages = {1-5},
abstract = {This paper introduces a robust roll angle estimation algorithm, which is developed from our previously published work, where the roll angle was estimated from a dense subpixel disparity map by minimizing a global energy using golden section search algorithm. In this paper, to achieve greater computational efficiency, we utilize gradient descent to optimize the aforementioned global energy. The experimental results illustrate that the presented roll angle estimation method takes fewer iterations to achieve the same precision as the previous method.},
keywords = {gradient methods;optimisation;search problems;stereo image processing;robust roll angle estimation algorithm;gradient descent;dense subpixel disparity map;golden section search algorithm;global energy;Signal processing algorithms;Roads;Estimation;Minimization;Europe;Real-time systems;Signal processing},
doi = {10.23919/EUSIPCO.2019.8903049},
issn = {2076-1465},
month = {Sep.},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533276.pdf},
}
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