288 pp. per issue
6 x 9, illustrated
2014 Impact factor:

Neural Computation

February 2001, Vol. 13, No. 2, Pages 389-410
(doi: 10.1162/089976601300014583)
© 2001 Massachusetts Institute of Technology
Learning Object Representations Using A Priori Constraints Within ORASSYLL
Article PDF (1.23 MB)

In this article, a biologically plausible and efficient object recognition system (called ORASSYLL) is introduced, based on a set of a priori constraints motivated by findings of developmental psychology and neuro-physiology. These constraints are concerned with the organization of the input in local and corresponding entities, the interpretation of the input by its transformation in a highly structured feature space, and the evaluation of features extracted from an image sequence by statistical evaluation criteria. In the context of the bias-variance dilemma, the functional role of a priori knowledge within ORASSYLL is discussed. In contrast to systems in which object representations are defined manually, the introduced constraints allow an autonomous learning from complex scenes.