Using Morphological Neural Networks for
Robotic Vision Object Recognition
Students: Donna S. Haun and Kristi M. Hummel
February-May, 2000
Dr. Marjorie Skubic, Advisor
This project is based on the
OMNN work done by Yonggwan Won and his PhD thesis,
"Nonlinear Correlation Filter and Morphology Neural Networks for Image Pattern and
Automatic Target Recognition", August 1995.
The goal of this project was to detect a target in varied environments and at
varying angles.
For the project, the training network has one feature map layer and one hidden
feed-forward layer, and one output layer which produces the detection image plane (DIP).
The training set consisted of 32 images, with the target on light and dark
backgrounds with varying degrees of clutter and occlusion.
Four different networks were trained using these 32 images, varying the size of the
structuring elements and the number of feature maps (7x7 sub element with 2 feature
maps, 7x7 sub element with 4 feature maps, 5x5 sub element with 4 feature maps, 3x3
sub element with 4 feature maps).
Four different images, with slight foreground clutter, were scanned for target
recognition using the above four networks.
Evaluation was done by viewing a superposition of the scan results on the original
images. Training and scanning times were also measured.
The preliminary conclusions are:
-- a 7x7 structuring element with 4 feature maps produces the best results,
but has the longest training time;
--the 7x7 structuring element with 2 features maps had less good results, but
the briefest scanning time;
-- a 3x3 structuring element is insufficient for a target this small,
but has the briefest training time;
Pictures
The target: a small robot. A few views are shown.
Sample results: 4 different pictures run using the 7x7 element with 4 feature
maps
Results showing the 7x7 element with 2 feature maps
Results showing the 5x5 element with 4 feature maps
Results showing the 3x3 element with 4 feature maps
Home
skubic@cecs.missouri.edu
May, 2000