A traffic image code method based on machine learning parameter choice

作者：Cao W L^*; Shi Z K; Feng J H

来源：Systems Engineering-Theory & Practice, 2007, 27(1): 157-162.

• Image compression
• Image scanning
• ITS
• Machine learning

Combined image compression with traffic image transmission in ITS, this paper presents a new image compression method of polynomial approach which based on machine learning parameter choice. We defined two species of index to measure the stability of the image scanning ways. We also researched how to get the parameter which is produced by machine learning, and how to use the parameter to monotonize the image scanning data. Moreover, we compressed the scanned data using the polynominal approach method. The most merit of our method is facility and high efficiency. We have gotten good results aimed at the small complex traffic image, especially in middle and low signal-to-noise ratio. Our method also can be popularize to a kind of image which interest area is locating in the center of the image.

• 出版日期2007
• 单位西北工业大学