Читать книгу Rethinking Prototyping - Группа авторов - Страница 126

The present study offers a way to encode large amounts of image data in an effective set of limited size. This limitation does not mean a linear loss of information but compiles the most effective parameters in a very productive set of steering handles. The applied method recognizes connections among spatially distributed but periodically reappearing features. The goal is to build up a model space from the instances (and not for them). Within this model space, one is able to compute with the instances and fluently fade between instead of categorizing them. This can be accomplished by abstraction and by transferring the objects into one specific body of thinking, in the present case into the frequency domain.

Architecture among many other sciences applies models to develop, test and compare ideas. A model view on something existing in the real world always involves certain degrees of abstraction in order to be successful. One has to squint the eyes – also in a metaphorical sense – to be able to distinguish aspects that matter from rather secondary features. Aspects that matter may be differences between instances that allow them to be distinguished as well as things all have in common or are at least similar. Similarity is something like the reciprocal value of difference.