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Readme file for image data from the IJCV publication "Learning the 
statistics of people in images and video", Sidenbladh & Black.
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IMAGES AND CONFIGURATIONS

The training images are located in ImageData. Each image has a corresponding 
manually determined edge configuration, in ManualEdgeData.
The configuration is in MAT format. When loaded into a Matlab program (for a 
file called <name>.mat, do "load <name>"), a matrix named edges is obtained. 

The first dimension (row) corresponds to limb number: 
    (1=torso, 2=thigh 3=calf, 4=upper arm, 5=lower arm, 6=head)
For each row, the four columns (second dimension) corresponds to the four 
    corners of the limb (endpoints of two edges).
Each corner has an x and y location in the image (third dimension):
    (1=x location, 2=y location)



LEARNED DISTRIBUTIONS

In Distributions, mat files containing the learned distributions can be 
found. When loading edge.mat or ridge.mat, a matrix RATIO is obtained. 
The first dimension is the filter response. The filter response is a 
    real-valued number, and the bin corresponding to filter response x is 
    found as:
         EDGE_NUNITS*EDGE_NBINS +  floor(x*EDGE_NBINS) + 1
    or, in the case of ridge:
         RIDGE_NUNITS*RIDGE_NBINS +  floor(x*RIDGE_NBINS) + 1
    The constants are:
         EDGE_NBINS = 40;      % number of bins per unit edge contrast      
         EDGE_NUNITS = 2;      % number of units edge contrast covered
         RIDGE_NBINS = 20;     % number of bins per unit ridge contrast
         RIDGE_NUNITS = 4;     % number of units ridge contrast covered
The second dimension is the image level. Since all levels have the same 
    distribution, this dimension is of length 1.
The third dimension is the limb number:
    (1=torso, 2=thigh 3=calf, 4=upper arm, 5=lower arm, 6=head)

The file flow.mat contains the two matrices BG and FG (since ratio cannot be 
computed, see Section 3.3 in the paper).
The first dimension is filter response as with ridge and edge. It is obtained
    in the same way: 
         FLOW_NUNITS*FLOW_NBINS +  floor(x*FLOW_NBINS) + 1
    where
         FLOW_NBINS = 0.2;     % number of bins per unit flow difference
         FLOW_NUNITS = 255;    % number of units flow difference covered
The second dimension is image level:
    (1=level 0, 2=level 1, 3=level 2, 4=level 3)
The third dimension  is the limb number:
    (1=torso, 2=thigh 3=calf, 4=upper arm, 5=lower arm, 6=head)
    Of coourse, BG has only one distribution, and the length of the third 
    dimension is thus 1.
