SOFTWARE AND HARDWARE IMPLEMENTATION OF THE NEUROMORPHIC LGN BASED IMAGE PROCESSING AND FEATURE EXTRACTION
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Date
2016-12-09
Authors
Dorzhigulov, Anuar
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
The processing of the graphical data is popular methodology of obtaining
important information. However, there is a major drawback: it typically requires
large computational resources. The human brain is an excellent example of the
efficient image processing hardware, due to the fact the biological visual system
can allow to easily and quickly obtain the information of the world around, such
as object identification and movement detection.
In particular, there is one element of the biological visual system that has unique
functionality in image processing, which is lateral geniculate nucleus (LGN).
Ganglion cells, which are terminated in LGN, have high sensitivity to the image
spatial intensity difference. This cell feature is used for pre-processing of the
visual data before being modulated and relayed to the main processing module,
visual cortex. As a result, the processing load on cortex is reduced, due to the
pre-processing of the data.
The aim of the project is to develop the algorithm for visual features extraction,
such as edge detection, based on the structure and properties similar to the LGN,
with a possibility of the hardware model implementation. Preliminary results show the edge detection property of the proposed method. Moreover, the measured performance is comparable to other popular edge detection techniques, even exceeding expectations to small extent in the noisy environment.
Description
Keywords
human visual system, Biological visual system, Thresholding, Research Subject Categories::TECHNOLOGY::Information technology::Computer science::Software engineering
Citation
Dorzhigulov, Anuar (2016) SOFTWARE AND HARDWARE IMPLEMENTATION OF THE NEUROMORPHIC LGN BASED IMAGE PROCESSING AND FEATURE EXTRACTION. Nazarbayev University School of Engineering, Department of Electrical and Electronic Engineering.