Recognition of Partially Occluded Objects Based on the Three Different Color Spaces (RGB, YCbCr, HSV)
The aim of this study is to propose an algorithm that can recognize partially occluded objects under different variations by computing three histograms of colour spaces (RGB, HSV, YCbCr). The dataset used in this research are from kitchen apparatuses. It is created by the researcher and include two parts: referenced objects (18 single objects) and tested objects (occluded objects) made from two single objects to represent the occluded object under different variations (scale, rotation, transformation) with varying percentage of occlusion (3090%). Three different colour spaces histogram (RGB, HIS, YCbCr) are used for extracting the features. Histogram intersection distance works for matching objects. Computation histograms and matching process are used to each block of image that given by image division process and finally compared the performance of each colour space by evaluating the accuracy. The experimental results demonstrate that the proposed algorithm is robust for identifying occluded objects and it could work at high occlusion.
occluded objects features mining objects classification color space histogram