From: airdg@castle.ed.ac.uk (Richard Grey)
Newsgroups: comp.graphics.visualization
Subject: Visual Force Detection in Landscape
Date: 12 Nov 92 13:05:33 GMT
Organization: Edinburgh University

I hope this is the right place to post, and that somebody out there may
be able to help me, (no matter what small a way !).

The following is an introduction to a project I am undertaking for the
Forestry Commission here in Scotland :

Abstract
--------
                The production of an automatic system   
                capable of identifying the visual force
                aspects of landscape using contour data.

Description
-----------
        Visual force is a principle which is embodied in art,
graphic design, and architecture.  The eye and mind respond to visual
force in a predictable and dynamic way.  The visual forces in landform
draw the eye down convex slopes and up concave ones, the strength of
the force depending on the scale and irregularity of the landform.

Steps
-----
        The starting point for this project is with contour data for a
specific region, ideally an array of National Survey coordinate /
altitude points. From the input, we must then be able to identify the
convex and concave features (ie. be able to label each pixel as being
one of : valleys, ridges, hollows, peaks and saddles etc.) and then
grouping these to form connected patches and remove trivial (single
pixel) patches. I hope to be able to use something like the Canny
non-maximal suppression (with Du Li's edge tracking) process for then
finding and connection the peaks and troughs of the ridges and valleys.

From all this, hopefully then we can estimate the scale and irregularity
of the landform features and so calculate the visual saliency.

----------

        My first main problem is the classification of each
        pixel into either ridges, valleys etc. At the moment, 
        I am looking into Besl & Jain's method of using the
        Mean and Gaussian curvatures (H & K values) to classify
        non-planar patches, and hence determine whether they
        are concave or convex, where the H & K values are found
        using a biquadratic fit.

        From this information, I need to group pixels into
        features so I can proceed with the Canny suppression and
        track edges.

        Is there any better way I might approach the two above ?
        Does anybody have any further enlightening information
        on my best approach (references seem to be virtually
        non-existent), or, better still, algorithms to do the
        above (I have some Canny stuff) ?

I would much appreciate any email replies that may be of use, and, if
there is enough interest / doubts/ queries, I will post some sort of
summary.

Cheers.

Richard.
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                         airdg@castle.ed.ac.uk
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        { Edinburgh University Dept. of Artificial Intelligence }
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Newsgroups: comp.graphics.visualization