This dissertation describes a 3D object recognition system that learns to recognise 3D objects from 2D intensity images. Input images are segmented using a novel segmentation algorithm, and depth data is extracted using a novel stereo algorithm. Attributes (including depth attributes) are extracted from each of the parts produced by the segmentation algorithm, and these are used by the Fuzzy Conditional Rule Generation (FCRG) machine learning classifier to either learn object models in the training samples, or to classify object models in test scenes containing one or more objects. A final hypothesis verification routine is used to verify the classifications produced by FCRG.
The system has been demonstrated using a database of 18 non-trivial objects, for which a classification rate of up to 78% was achieved. This dissertation demonstrates that learning to recognise 3D objects from 2D intensity images is a viable alternative to recognising objects from dense range data, that sparse range data obtained from depth-from-X techniques (in this case, depth-from-stereo) can be used to aid the recognition process, and that it is possible to recognise objects in complex scenes without prior object partitioning.