  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.

Neural and Evolutionary Computing;Artificial Intelligence;Computer Vision and Pattern Recognition