# StartFromHere

## PURPOSE

StartFromHere - The entry point of this project.

## SYNOPSIS

This is a script file.

## DESCRIPTION

     StartFromHere - The entry point of this project.

       StartFromHere tests retrieval image visual objects from a shape script template.

       The shape script template is composed of 4 shape motifs (basic shapes,
       e.g. ellipsoid, angle, parallel bars), which are learned from a single
       clean example image. The search for the shape script in the testing
       image is performed by a dynamic programming approach, which we call
       recursive SUM/MAX procedure. It results in SUM1/MAX1, SUM2/MAX2 and
       SUM3 maps. And a best location is found in SUM3 map that has the
       maximum of matching score.

## CROSS-REFERENCE INFORMATION

This function calls:

- ConstructEgretShapeScript ConstructEgretShapeScript - Construct egret shape script template.
- DisplayS3ScoreHistogram DisplayS3ScoreHistogram - Compare the distribution of S3 scores on natural images, vs.
- FindEgret FindEgret - A simple test case of detection using shape script.
- FindPelican FindPelican - A simple test case of detection using shape script.
- GenerateHtml GenerteHtml - Generates html page for experimental results
- LearnS2Template LearnS2Template - Learns an active basis template for Ellipsoid, angle,
- dumpNeg dumpNeg - Precompute Gabor response maps on background/negative images.
- generateTransformedS2Templates generateTransformedS2Templates - Affine transforms of the S2
- getNaturalStat_multiscale getNaturalStat_multiscale - Obtain natural statistics for multiscale Gabor responses

This function is called by:

## SOURCE CODE

    0001 % StartFromHere - The entry point of this project.
    0002 %
    0003 %   StartFromHere tests retrieval image visual objects from a shape script template.
    0004 %
    0005 %   The shape script template is composed of 4 shape motifs (basic shapes,
    0006 %   e.g. ellipsoid, angle, parallel bars), which are learned from a single
    0007 %   clean example image. The search for the shape script in the testing
    0008 %   image is performed by a dynamic programming approach, which we call
    0009 %   recursive SUM/MAX procedure. It results in SUM1/MAX1, SUM2/MAX2 and
    0010 %   SUM3 maps. And a best location is found in SUM3 map that has the
    0011 %   maximum of matching score.
    0012 %
    0013
    0014 %% compilation
    0015 cd common
    0016 mex Chistogram.c
    0017 mex CcomputeMAX1.c
    0018 mex CgetMax1track.c
    0019 mex CcomputeSUM2.c
    0020 mex CcomputeMAX2.c
    0021 mex CcomputeSUM3_loglinear.c
    0022 mex CsharedSketch.c
    0023 mex Csigmoid.c
    0024 mex ClocalNormalizeSingle.c
    0025 cd ..
    0026
    0027 %% set parameters
    0028 setTemplateParameters;
    0029
    0030 %% precompute S1 maps on natural images
    0031 if ~exist('feature/neg/neg02.jpg.mat','file')
    0032     cd common
    0033     dumpNeg;
    0034     cd ..
    0035 end
    0036
    0037 %% obtain natural statistics
    0038 if ~exist('result/natural_stat.mat','file')
    0039     cd common
    0040     getNaturalStat_multiscale;
    0041     cd ..
    0042 end
    0043
    0044
    0045 %% learn shape motifs
    0046 cd common
    0047 LearnS2Template;
    0048 generateTransformedS2Templates;
    0049 cd ..
    0050
    0051 %% shape script
    0052 ConstructEgretShapeScript;
    0053
    0054 %% Detect
    0055 FindEgret;
    0056 FindPelican;
    0057
    0058 %% generate figures
    0059 % GenerateEgretFigure;    % (obsolete) generate a single eps figure with detection results
    0060 % GenerateLatex;      % (obsolete) generate a simle latex file together with a folder of eps detection results
    0061
    0062 %% generate project page
    0063 GenerateHtml;       % generate a simle latex file together with a folder of eps and jpg detection results
    0064
    0065 %% Compare the distribution of S3 scores on positive/negative examples
    0066 DisplayS3ScoreHistogram;