Computer vision using biomimicking AI system

Abstract: The aim of this thesis was to develop a biologically inspired model for unsupervised learning on visual data. A preprocessor inspired by the retina was developed, which generated event-camera like data. This can be used as a low-cost alternative to an event-camera. Further, a neuron model was developed which showed ability to learn useful features from video data in combination with the preprocessor. A number of aspects of the system were also explored to probe its properties. A data set consisting of videos of moving 3D-printed objects was created. This was used to compare the biological model in combination with a small LSTM network against a much larger convolutional LSTM, in classifying the different objects. This gave promising results, where the biological model performed as well or better in most aspects, compared with the large convolutional LSTM.