Image-based object detection under varying illumination in environments with specular surfaces
Image-based environment representations capture the appearance of the surroundings of a mobile robot and are useful for the detection of novelty. However, image-based novelty detection can be impaired by illumination effects. In this paper we present an approach for the image-based detection of novel objects in a scene under varying lighting conditions and in the presence of objects with specular surfaces. The computation of an illumination-invariant image-based environment representation allows for the extraction of the shading of the environment from camera images. Using statistical models infered from the luminance and the saturation component of the shading images, secularities and shadows are detected and suppressed in the process of novelty detection. Experimental results show that the proposed method outperforms two recently presented reference approaches for illumination-invariant change detection in images.