Recognition of Cylindrical Objects Using Occluding Boundaries Obtained from Colour Based Segmentation

This paper describes a method for model-based recognition of cylindrical objects from occluding boundaries obtained by computationally efficient colour segmentation of a 2D image. The models are invoked by combining geometric and colour features. Occluding boundaries of hypothesized objects are generated using colour segmentation and ground plane constraint. Hypothesis verification is achieved by evaluating the fit between occluding boundary generated by the hypothesised object and the edge data. This method differs from existing methods in that it integrates multiple measurements and prior knowledge to achieve robust object recognition. Experiments with real images have been carried out and the results are promising.