SOFTWARE AND HARDWARE IMPLEMENTATION OF THE NEUROMORPHIC LGN BASED IMAGE PROCESSING AND FEATURE EXTRACTION

摘要

The processing of the graphical data is popular methodology of obtainingimportant information. However, there is a major drawback: it typically requireslarge computational resources. The human brain is an excellent example of theefficient image processing hardware, due to the fact the biological visual systemcan allow to easily and quickly obtain the information of the world around, suchas object identification and movement detection.In particular, there is one element of the biological visual system that has uniquefunctionality in image processing, which is lateral geniculate nucleus (LGN).Ganglion cells, which are terminated in LGN, have high sensitivity to the imagespatial intensity difference. This cell feature is used for pre-processing of thevisual 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 thepre-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.