Bio-Inspired Computer Vision: Towards a Synergistic Approach of Artificial and Biological Vision

To appear in CVIUStudies in biological vision have always been a great source of inspiration for design of computer vision algorithms. In the past, several successful methods were designed with varying degrees of correspondence with biological vision studies, ranging from purely functional inspiration to methods that utilise models that were primarily developed for explaining biological observations. Even though it seems well recognised that computational models of biological vision can help in design of computer vision algorithms, it is a non-trivial exercise for a computer vision researcher to mine relevant information from biological vision literature as very few studies in biology are organised at a task level. In this paper we aim to bridge this gap by providing a computer vision task centric presentation of models primarily originating in biological vision studies. Not only do we revisit some of the main features of biological vision and discuss the foundations of existing computational studies modelling biological vision, but also we consider three classical computer vision tasks from a biological perspective: image sensing, segmentation and optical flow. Using this task-centric approach, we discuss well-known biological functional principles and compare them with approaches taken by computer vision. Based on this comparative analysis of computer and biological vision, we present some recent models in biological vision and highlight a few models that we think are promising for future investigations in computer vision. To this extent, this paper provides new insights and a starting point for investigators interested in the design of biology-based computer vision algorithms and pave a way for much needed interaction between the two communities leading to the development of synergistic models of artificial and biological vision

Sensor Fusion in the Perception of Self-Motion

This dissertation has been written at the Max Planck Institute for Biological Cybernetics (Max-Planck-Institut für Biologische Kybernetik) in Tübingen in the department of Prof. Dr. Heinrich H. Bülthoff. The work has universitary support by Prof. Dr. Günther Palm (University of Ulm, Abteilung Neuroinformatik). Main evaluators are Prof. Dr. Günther Palm, Prof. Dr. Wolfgang Becker (University of Ulm, Sektion Neurophysiologie) and Prof. Dr. Heinrich Bülthoff.amp;lt;bramp;gt;amp;lt;bramp;gt; The goal of this thesis was to investigate the integration of different sensory modalities in the perception of self-motion, by using psychophysical methods. Experiments with healthy human participants were to be designed for and performed in the Motion Lab, which is equipped with a simulator platform and projection screen. Results from psychophysical experiments should be used to refine models of the multisensory integration process, with an mphasis on Bayesian (maximum likelihood) integration mechanisms.amp;lt;bramp;gt;amp;lt;bramp;gt; To put the psychophysical experiments into the larger framework of research on multisensory integration in the brain, results of neuroanatomical and neurophysiological experiments on multisensory integration are also reviewed