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

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

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

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.