The recent development of low cost cameras that capture 3-dimensional images has changed the focus of computer vision research from using solely intensity images to the use of range images, or combinations of RGB, intensity and range images. The low cost and widespread availability of the hardware to capture these images has realised many possible applications in areas such as robotics, object recognition, surveillance, manipulation, navigation and interaction. Given the large volumes of data in range images, processing and extracting the relevant information from the images in real time becomes challenging. To achieve this, much research has been conducted in the area of bio-inspired feature extraction which aims to emulate the biological processes used to extract relevant features, reduce redundancy, and process images efficiently. Inspired by the behaviour of biological vision systems, an approach is presented for extracting important features from intensity and range images, using biologically inspired spiking neural networks in order to model aspects of the functional computational capabilities of the visual system.
