Redundant Data Removal from Images 2015-01-0215

This paper presents a simple yet novel approach to remove redundant data from outdoor scenes, thus finding significant application in Advanced Driver Assistance Systems (ADAS). A captured outdoor scene has two main parts, the ground region consisting of the road area along with other lane markings and the background region consisting of various structures, trees, sky etc. To extract the ground region, first the yellow and white road markings are segmented based on the HSI (Hue Saturation Intensity) color model and these regions are filled with the surrounding road color. Further the background region is segmented based on the Lab (Color-opponent) color model, which shows significant improvement as compared to other color spaces.

To extract the background region such as the sky or ground region, it is assumed that the top and bottom most portions of the image does not consist of useful information. Considering these portions as the seed points, the data propagating from these seed points are removed. Lab color space is used for this purpose. Tests on various outdoor scenes have shown positive results.