Abstract-This study is part of a project which investigates computational principles which underlie perception and representation of architectural streetscape character. Some of the principles can be associated with fundamental concepts in brain theory and Gestalt psychology. For the experimental analysis streetscapes were represented by sequences of digital images of house façades which were prepared by a team of researchers from architecture. Two methods for non-linear dimensionality reduction, isomap and maximum variance unfolding, were applied to a set of Hough arrays (for lines) of the given images. An analysis of the extracted "streetmanifolds" revealed groupings of house façades with similar visual character and proportions. Comparative tests were conducted on a simple cylinder shaped example manifold to evaluate the geometric stability of the two dimensionality reduction methods. All experiments addressed variations of the distance metric and the neighbourhood parameter.

This paper addresses the topic of how architectural visual experience can be represented and utilised by a software system. The long-term aim is to equip an artificial agent with the ability to make sensible decisions about aesthetics and proportions when creating its environment. The focus of the investigation is on the feature of line distributions extracted from digital images of house facades. It is shown how non-linear "streetmanifolds" can be calculated where each point on the manifold corresponds to a house façade. Through interpolation between manifold points and the application of an inverse Hough transform basic structure plans for new house façades are obtained. If the interpolated points are close to the manifold it can be argued that the new plans reflect the character of the surrounding streetscape. The method is also demonstrated using basic examples which can be represented by circles.