Visibility Measurement with Image Understanding
Xu-Cheng Yin · 2013
High hardware cost, complex operation and narrow application are main problems of existing measuring methods of atmospheric visibility. In this paper, machine learning is introduced into the study of visibility measurement and a method of daytime visibility measurement is proposed based on image understanding. Firstly, image features and vectors based on pixel contrast are designed and extracted grounded on the segmentation of the regions of interest in measured scene images. Then, the relational model between image features and visibility is constructed by training support vector regression. Finally, visibility of images to be measured is computed according to the model. Experimental results show that the proposed method has both high visibility measuring precision and good flexibility. Moreover, it reduces the limitations of existing visibility measurement methods.