Due to the electronic rolling shutter, high-speed Complementary Metal-Oxide Semiconductor( CMOS) aerial cameras are generally subject to geometric distortions,which cannot be perfectly corrected by conventional vision...Due to the electronic rolling shutter, high-speed Complementary Metal-Oxide Semiconductor( CMOS) aerial cameras are generally subject to geometric distortions,which cannot be perfectly corrected by conventional vision-based algorithms. In this paper we propose a novel approach to address the problem of rolling shutter distortion in aerial imaging. A mathematical model is established by the coordinate transformation method. It can directly calculate the pixel distortion when an aerial camera is imaging at arbitrary gesture angles.Then all pixel distortions form a distortion map over the whole CMOS array and the map is exploited in the image rectification process incorporating reverse projection. The error analysis indicates that within the margin of measuring errors,the final calculation error of our model is less than 1/2 pixel. The experimental results show that our approach yields good rectification performance in a series of images with different distortions. We demonstrate that our method outperforms other vision-based algorithms in terms of the computational complexity,which makes it more suitable for aerial real-time imaging.展开更多
基金Sponsored by the National Natural Science Foundation of China(Grant No.60902067)the Foundation for Science & Technology Research Project of Jilin Province(Grant No.11ZDGG001)
文摘Due to the electronic rolling shutter, high-speed Complementary Metal-Oxide Semiconductor( CMOS) aerial cameras are generally subject to geometric distortions,which cannot be perfectly corrected by conventional vision-based algorithms. In this paper we propose a novel approach to address the problem of rolling shutter distortion in aerial imaging. A mathematical model is established by the coordinate transformation method. It can directly calculate the pixel distortion when an aerial camera is imaging at arbitrary gesture angles.Then all pixel distortions form a distortion map over the whole CMOS array and the map is exploited in the image rectification process incorporating reverse projection. The error analysis indicates that within the margin of measuring errors,the final calculation error of our model is less than 1/2 pixel. The experimental results show that our approach yields good rectification performance in a series of images with different distortions. We demonstrate that our method outperforms other vision-based algorithms in terms of the computational complexity,which makes it more suitable for aerial real-time imaging.
