?
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 for “Robots”. Secondly, robust against “outliers”. A novel robust PCA model for outliers is necessary due to bad weather. According to the real-time“see-through” car system, cars are equipped with many cameras and sensors, vehicle to vehicle (V2V) communication are built for autonomous and assisted driving, making other cars transparent using cameras and sensors via wireless network. Thirdly, robust against “complex environments”. By deep learning model, the system has the object recognition function. It can detect object(DET), and make classification and localization (CLS-LOC). Lastly, robust against “difficult conditions”. Sensor fusion approach is important for high-quality 3D modeling. In a word, robustness of computer vision solutions is a very important key for the real-world applications of intelligent systems (automobile, robots), and it involves camera input enhancement ,real-time outlier handling, deep-learning, sensor fusion and many other issues.
Kweon In-So(權(quán)仁昭),卡內(nèi)基梅隆大學(xué)機(jī)器人研究所博士, 韓國(guó)科學(xué)技術(shù)院電氣工程教授(EE),韓國(guó)國(guó)家關(guān)鍵技術(shù)研究中心-KAIST P3數(shù)字車(chē)中心主任。1995—1998年擔(dān)任韓國(guó)科學(xué)技術(shù)院自動(dòng)化工程及設(shè)計(jì)部門(mén)主管(ADE)。Kweon教授主要從事計(jì)算機(jī)視覺(jué)和機(jī)器人研究,為KROS、ICROS和IEEE會(huì)員,自2005年為《國(guó)際計(jì)算機(jī)視覺(jué)雜志》的編輯委員會(huì)成員。Kweon教授在國(guó)際會(huì)議上曾獲多個(gè)獎(jiǎng)項(xiàng),包括“IEEE-CVPR2009最佳學(xué)生論文獎(jiǎng)的亞軍”以及“ICCAS2008學(xué)生論文獎(jiǎng)”。2001年,Kweon教授獲得了KAIST研究獎(jiǎng)。
Kweon In-So
(School of Electrical Engineering, KAIST, Korea)