Robust Vision Technology of Intelligent Systems for Real-world Applications

Lessons learned from failures are that'robustness'of computer vision is important.Firstly,robust against'illumination changes'.Camera parameters,'ISO gain,aperture(=F#),exposure'determine the image quality.It is designed mainly for Photography(not for Robot),correlated non-linearly and sensitive to illumination changes.So it needs a very simple,but effective way to control the camera parameters

Lessons learned from failures are that＂ robustness＂ of computer vision is important. Firstly, robust against ＂illumination changes＂ quality. It is designed illumination changes. So ＂Robots＂. Secondly, weather. According t Camera parameters, ＂ISO gain, aperture （ = F#）, exposure＂ determine the image mainly for Photography （not for Robot ）, correlated non-linearly and sensitive to it needs a very simple, but effective way to control the camera parameters for robust against ＂outliers＂. A novel robust PCA model for outliers is necessary due to bad o the real-time ＂see-through＂ car system,