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Biologically Inspired Intensity and Depth Image Edge Extraction

Kerr, Dermot, Coleman, SA and McGinnity, TM (2018) Biologically Inspired Intensity and Depth Image Edge Extraction. IEEE Transactions on Neural Networks and Learning Systems, 1 . [Journal article]

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DOI: 10.1109/TNNLS.2018.2797994


In recent years artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real-time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks we emulate functional computational aspects of biological visual systems. Results demonstrate that the proposed bio-inspired artificial vision system has increased performance over existing computer vision feature extraction approaches.

Item Type:Journal article
Keywords:depth image, spiking neural network, bio-inspired imaging
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:39556
Deposited By: Dr Dermot Kerr
Deposited On:21 Feb 2018 15:52
Last Modified:21 Feb 2018 15:58

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