Color Based Object Recognition

this paper, our aim is to analyze and evaluate various color models to be used for the purpose of recognition of multicolored objects according to the following criteria: -- Robustness to a change in viewing direction; -- Robustness to a change in object geometry; -- Robustness to a change in the direction of the illumination; -- Robustness to a change in the intensity of the illumination; -- Robustness to a change in the spectral power distribution (SPD) of the illumination. Next to defining color models which have: -- High discriminative power; -- Robustness to object occlusion and cluttering; -- Robustness to noise in the images. 2 It can be expected that two or more of the above criteria are interrelated. For example, Funt and Finlayson [2] show that when illumination is controlled Swain's color based recognition method performs better than object recognition based on illumination-independent image descriptors. However, Swain's method is outperformed when illumination varies across the scene. Supposedly, their is a tradeoff between the amount of invariance and expressiveness of the color models. To that end, our goal is to get more insight which color models to use under which imaging parameters. This is useful for object recognition applications where no constraints on the imaging process can be imposed as well as for applications where one or more parameters of the imaging process can be controlled such as for robots and industrial inspection (e.g. controlled object positioning and lightning conditions). For such a case, color models can be used for object recognition which are less invariant (at least under the given imaging conditions), but having higher discriminative power. The paper is organized as follows. In Section 2, basic color models are defined for c..