This paper presents a model-based method for colour-based recognition of objects from a large database. The algorithm is based on the assumption that surface reflectances of objects in the model database follow the extended dichromatic model proposed by Shafer [Sha84]. Adoption of the dichromatic model allows recovery of body colour - the component, of sensor responses (RGB-values) that is independent of scene geometry and illumination intensity. Both theoretical studies [Hea89b] and experiments [LB90][KSK88] confirm that Shafer's model gives a suitable approximation for reflectances of a wide range of materials. Instead of using traditional techniques (eg. clustering, split-and-merge) to obtain regions of 'similarly' coloured pixels followed by classification a novel approach is argued for. First, for each pixel a list of models with nonzero aposteriori probabilities P(modeU\body colotir) is computed using Bayes formula. Next, regions are formed by grouping pixels with identical most probable hypothesis. Probabilities P(modeli\region) are obtained trough a standard group decision rule [FT80]. We show that the proposed scheme can be used for a number of visual tasks - localization of objects, generation and verification of object hypotheses. Experiments on images of complex indoor scenes confirm that the proposed method can provide reliable information about the surrounding environment.
['Jiri Matas', 'Radek Marik', 'Josef Kittler']
Generation, Verification and Localization of Object Hypotheses based on Colour