A neuromorphic approach to image processing and machine vision

TL;DR

This paper explores neuromorphic engineering for image processing, focusing on visual tasks like segmentation and recognition, employing memristors and neuromorphic sensors to enhance artificial visual systems and their hardware acceleration.

Contribution

It introduces a novel memristor-based approach for image segmentation and discusses the integration of neuromorphic sensors and non-volatile memory in visual systems.

Findings

Memristor-based image segmentation method proposed.

Neuromorphic sensors enable asynchronous visual signal transmission.

Progress in memory technology can directly benefit computer vision.

Abstract

Neuromorphic engineering is essentially the development of artificial systems, such as electronic analog circuits that employ information representations found in biological nervous systems. Despite being faster and more accurate than the human brain, computers lag behind in recognition capability. However, it is envisioned that the advancement in neuromorphics, pertaining to the fields of computer vision and image processing will provide a considerable improvement in the way computers can interpret and analyze information. In this paper, we explore the implementation of visual tasks such as image segmentation, visual attention and object recognition. Moreover, the concept of anisotropic diffusion has been examined followed by a novel approach employing memristors to execute image segmentation. Additionally, we have discussed the role of neuromorphic vision sensors in artificial visual systems and the protocol involved in order to enable asynchronous transmission of signals. Moreover, two widely accepted algorithms that are used to emulate the process of object recognition and visual attention have also been discussed. Throughout the span of this paper, we have emphasized on the employment of non-volatile memory devices such as memristors to realize artificial visual systems. Finally, we discuss about hardware accelerators and wish to represent a case in point for arguing that progress in computer vision may benefit directly from progress in non-volatile memory technology.