- Title
- Representations of streetscape perceptions through manifold learning in the space of Hough arrays
- Creator
- Chalup, Stephan K.; Clement, Riley; Marshall, Joshua; Tucker, Chris; Ostwald, Michael J.
- Relation
- 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
- http://dx.doi.org/10.1109/ALIFE.2007.367818
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Resource Type
- conference paper
- Date
- 2007
- Description
- 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.
- Subject
- Gestalt psychology; Hough arrays; architectural streetscape character; brain theory; manifold learning; maximum variance unfolding; nonlinear dimensionality reduction; streetscape perception
- Identifier
- http://hdl.handle.net/1959.13/808890
- Identifier
- uon:7778
- Identifier
- ISBN:142440701X
- Rights
- Copyright © 2007 IEEE. Reprinted from the IEEE Symposium on Artificial Life, 2007 (ALIFE '07). This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of University of Newcastle's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.
- Language
- eng
- Full Text
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