Machine learning paradigms for pattern recognition and image understanding.
In this paper some issues are considered related to the encoding of spatial information and associated perceptual learning algorithms which, it is claimed, are necessary for robust pattern and object recognition in multi-object (natural) scenes. The types of learning requirements within a 'recognition-by-parts' paradigm are contrasted with findings from alternative models.
Caelli
T
T
Department of Computer Science, Curtin University of Technology, Perth, Australia.
Bischof
W F
WF
D005556:Form Perception / Q000502:physiology*; D006801:Humans; D007858:Learning; D016571:Neural Networks, Computer; D010364:Pattern Recognition, Visual / Q000502:physiology*; D013028:Space Perception / Q000502:physiology