SectorAnalysisToolbox Documentation

From BanghamLab
Revision as of 10:34, 8 February 2012 by AndrewBangham (talk | contribs)
Jump to navigation Jump to search

Go back to software

The models shown in these tutorials illustrate features of the SectorAnalysisToolbox software. Viewing these pages. Some versions of Firefox and Explorer do not create satisfactory prints even though you can view the pages with no problems. Chrome does appear to produce good printouts.

Four ways to use SectorAnalysisToolbox

1) [SectorAnalysisToolbox Documentation#2 Creating shape models in 2D| Analysing shapes.]] i.e. the arrangement of points around a shape

2) Comparing shapes from samples of different groups for example, comparing faces from different cartoon characters

3) Analysing shape and appearance. In addition to the points around a shape, analyse the appearance (grey scale or colour) within the shape.

4) Analysing 3D shapes

1 Analysing 2D shapes using the Graphical User Interface

How to use the tutorial. First download and install the SectorAnalysisToolbox . A zip file containing the project (PRJ_CartoonFaces) is available here. Download and unzip into a directory. Then, from Matlab, change directory into the project and launch the SectorAnalysisToolbox

cd PRJ_CartoonFaces
SectorAnalysisToolbox 

This project contains as set of faces that have been analysed using 2D shape models

The process of analysing a set of images is:-

  1. Create a new project. AAMToolbox project names are automatically prefaced with PRJ_. They have a particular directory structure and the images to be analysed need to be copied into the subdirectory called Cropped. It is best if they are all the same size.
  2. Create a point model template. Points are placed around the object of interest, i.e. around a face or leaf. The set of points constitute the point model. Every image will be marked up in the same way.
  3. Move the points to the corresponding positions in each image in turn. The positions must correspond to the same material points in each image, i.e. the tip of the leaf, the corner of an eye, or halfway along a line between the two ends of the mouth.
  4. Generate the shape model using principal component analysis (PCA)
  5. View the result by varying each important component in turn. We call this walking the shape model. This movie shows a walk.
<wikiflv width="300" height="300" logo="false" loop="true" background="white">ShapeVectorWalkShowShape-21-Jun-2011-14-28-24 VD.flv|ShapeVectorWalkShowShape-21-Jun-2011-14-28-24 VD_First.png</wikiflv>

1 B