## 4 Materials and Methods  
Twenty-nine photographic images (photographs by A.A. Lavdas, processing performed using Adobe Photoshop software), six geometrical patterns, and four pattern drawings and one Lion drawing (sketches by N.A. Salingaros) were processed using the 3M-VAS. In general, the software provides five different results of visualizations of each image as follows [1]:  
1. Areas of Interest. These can be specified by the user, and each of them has a numeric score which is the probability that a person will look somewhere within that area during the pre-attentive period. We did not use this feature.
2. Heatmap. This is a color-coded probability map that a certain part of the image will attract the gaze during the pre-attentive period. We used this feature in all the scans, adopting it as the most direct and useful diagnostic tool for our analysis.
3. Hotspots. A simplified version of the heatmap results shows only the areas that are most likely to be seen during the pre-attentive scan, with a numeric score indicating the probability that a person will look somewhere in that region during the pre-attentive period.  
Gaze Sequence. This indicates the four most likely gaze locations, in their most probable viewing order.
* Visual Features. This visualization gives an insight to how the algorithm works, by extracting those same features that drive pre-attentive processing in our visual system [25]; namely edges, intensity, red/green color contrast, blue/yellow color contrast, and faces. We used this feature only in the first scan, for demonstration purposes.  
Buildings and patterns of interest of either the same or of different photographs were used for scanning. Some of the images were cropped parts of the same photograph. Image selection and comparison among the results aimed both to derive general practical results, and to learn how best to use the 3M-VAS software.  
We scanned different building types. Following on from the recent pilot study by two of the present authors [2], here we went one step further to examine the contribution of more elements, in addition to window size, shape, and arrangement. We undertook a series of scanning experiments to robustly understand the interaction of image and subject, and how that depends upon various factors. These trials shed light on how to technically optimize the conditions for analysis, as follows.  
* Framing: Adding a surrounding frame, when the building of interest occupies most of the photographic field.
* Brightness/contrast/saturation: Adjusting these parameters, either overall or locally, influences the way that the software registers the examined structures.
* Distance from the building: Examining whether approaching a structure reveals more details, resulting in sustained coherence, or if the coherence disintegrates.  
In the building photographs, cropping was performed only to isolate specific features when needed; in all other cases there was no effort to exclude elements like people or cars, as we wanted to be able to experiment with real-life images. Since what is described here is a novel material, it was equally important to investigate how to avoid pitfalls, which is connected to but distinct from the above points. This is a significant challenge. Failure to optimize some of the image parameters may give scans that could lead to erroneous conclusions. We believe that our results, obtained after interpreting a large number of scans, help attribute scanning behavior to the presence of specific patterns.