Titlebook: Machine Learning and Image Interpretation; Terry Caelli,Walter F. Bischof Book 1997 Springer Science+Business Media New York 1997 computer

雄偉

SOO-PIN: Picture Interpretation Networks,er, the procedural object-oriented language Java. Both systems propagate uncertainty through the agents using Baldwin’s (1986) formulation [16]. The system is illustrated with interpretations of traffic intersection images.

去掉

Invariance Signatures for Two-Dimensional Contours,ate spaces. MBNNs can be trained with much smaller training sets than are required by TNNs. This means that MBNNs are much less computationally-expensive to train than TNNs. Experiments demonstrate that such Invariance Signature networks can be employed successfully for shift-, rotation- and scale-invariant optical character recognition.

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Deadpan

Fuzzy Conditional Rule Generation for the Learning and Recognition of 3D Objects from 2D Images,st is the use of a recent ML technique (specifically CRG - Conditional Rule Generation) which generates descriptions of objects in terms of object parts and part relational attribute bounds. We show how this technique can be combined with intensity-based model and scene views to locate objects and t

CROAK

braggadocio

innovation

不能平靜

Postmenopause

esoteric