1. Applications of Cellular Neural Networks to Image Understanding
Experimental results on Shape/motion Estimation and Recognition

2. Experimental Results
Figure : Estimating shape and motion using Cellular Neural
Nerwork (
input image on the left, result on the right)

3. SELECTING FEATURE VECTORS FOR RECOGNITION
image
sequence
optical
flow
features of
flow
field
rearranged
features
of time series

4. Experiments using CNN as a associate memory
For experiments we are currently using images of the Columbia
image database. We take image sequences of 36 images each of
several selected objects. To speed up the flow computation and to
handle the data amount, we reduced the image resolution to 32x32
pixels.
Figure: Some images of the Columbia image database
For a better visualisation they are shown as
normalized
gray images.
We show the different features in x-direction, the time in y-direction.
The associative memory is used to restore incomplete sequences and
to classify them.
Figure : Feature vectors of five objects
We find that using 8 features out of the full set of 13 features leads to
excellent discrimination.
Further experiments will be done concerning different resolutions of
the images and concerning variable lengths of the image sequences.