Image transformation by spatial inhibition and local association
Takashi Omori · 1991
The author proposes a model of image transformation that can modulate any unlearned object with a general transformation. That is, the transformation is independent of an object's shape. The local associative neural network model can transform a figure represented by a local feature set. The model transforms a figure satisfying constraints that are given as external inhibition and completing conditions that any figure should satisfy to be a reasonable shape. The basic methods are a figure representation with local features, feature transformation with spatial inhibition, and figure restoration with their interactions. With this model, one can realize an elemental function that will lead to a general figure transformation model without learning or experience.>