Real-Time Image Segmentation using Neuromorphic Pixel Array

Image segmentation is a critical step in achieving computer vision. For fast and near real time image segmentation it is important to use dedicated hardware as it works much faster as compared to software based solutions. This paper presents a CMOS based real time image segmentation analog circuit using semi-supervised learning scheme. The proposed circuit ensures minimal operating power required when compared to its digital hardware implementation due to sub-threshold region operation of MOSFET and much lesser number of transistors being used. The circuit operation is based upon an anisotropic current spreading in non linear circuits. An analog pixel array is created which takes in weights and initial seed as input and segments the image based on them. Weights are generated from an image based on the feature similarity and are then fed to the pixel array. These weights dictate the anisotropic diffusion of current in the analog array and thus required segmentation is achieved. The proposed analog circuit based segmentation scheme is highly power efficient as compared to digital hardware implementation. Thus this scheme can be used in a power constrained environment as in case of Internet of Things (IoTs) network with independent battery-less nodes. Our approach will also be suitable for applications that require highperformance computing to run in real time, such as biomedical image segmentation for image-guided surgery.