Combined geometric transformation and illumination invariant object recognition in RGB color images

This paper presents a novel approach for object recognition in RGB color images using features based on the theories of geometric and complex moments. By effectively combining the properties of the RGB color space and the normalization procedures and properties of the geometric and complex moments we have implemented a feature vector that is invariant to geometric transformations (i.e. translation, rotation and scale) and changes in both the illumination color and illumination intensity. The experimental results presented demonstrates the performance of the proposed feature set and investigate its tolerance to image distortions.