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Please use this identifier to cite or link to this item: http://hdl.handle.net/1959.13/808890
- Representations of streetscape perceptions through manifold learning in the space of Hough arrays
Chalup, Stephan K.;
Ostwald, Michael J.
- 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 facades 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 facades 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.
- 2007 IEEE Symposium on Artificial Life (ALIFE ’07). Proceedings of the IEEE Symposium on Artificial Life, 2007 (ALIFE '07) (Honolulu, HI 1-5 April, 2007) p. 362-369
- Publisher Link
- Institute of Electrical and Electronics Engineers (IEEE)
architectural streetscape character;
maximum variance unfolding;
nonlinear dimensionality reduction;
- Resource Type
- conference paper
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