Superpixel Segmentation based a Novel Approach for Colour Correction of Depth 3-D Image in RGB-D Systems

Today image processing and utilizations based on RGB-D data have huge interest in various research areas. Regarding segmentation, colour correction, depth information, comprehensive description of 3-Dimension scenes in RGB-Depth data provides useful hints from another dimension to segregate substance in close colours. Here, super pixel segmentation based a new method is discussed to resolve such mixed, lost, and noise level increase of pixel problems of the RGB-D system and has been simulated using MATLAB software. The advantage of MATLAB is, it is widely available, continuously updated and has wider reach. In our approach, mixed pixel areas are detected using super pixel segmentation of colour and depth, and consolidate them to lost pixel regions. The consolidated regions are replete with neighbouring depth information based on a Robust Edge-Stop (RES) Function; distance transform values of colour edge pixels are used to form this function. In addition, a hybrid filter is used to remove noise level of pixels. Experimental results shows the proposed method gives better performance on Mean, Intensity, Area, Perimeter, Centroid, and Diameter.