FPGA based Parallelized Architecture of Efficient Graph based Image Segmentation Algorithm

TL;DR

This paper presents FPGA-based parallel architectures for an efficient graph-based image segmentation algorithm, significantly improving speed and power efficiency for real-time applications in mobile robotics.

Contribution

It introduces three novel FPGA architectures for the graph-based segmentation algorithm, achieving at least 2X speedup and lower power consumption compared to software implementations.

Findings

Achieved at least 2X speed gain over existing implementations.

Optimized for low power consumption and real-time processing.

Enabled deployment on mobile robotic systems.

Abstract

Efficient and real time segmentation of color images has a variety of importance in many fields of computer vision such as image compression, medical imaging, mapping and autonomous navigation. Being one of the most computationally expensive operation, it is usually done through software imple- mentation using high-performance processors. In robotic systems, however, with the constrained platform dimensions and the need for portability, low power consumption and simultaneously the need for real time image segmentation, we envision hardware parallelism as the way forward to achieve higher acceleration. Field-programmable gate arrays (FPGAs) are among the best suited for this task as they provide high computing power in a small physical area. They exceed the computing speed of software based implementations by breaking the paradigm of sequential execution and accomplishing more per clock cycle operations by enabling hardware level parallelization at an architectural level. In this paper, we propose three novel architectures of a well known Efficient Graph based Image Segmentation algorithm. These proposed implementations optimizes time and power consumption when compared to software implementations. The hybrid design proposed, has notable furtherance of acceleration capabilities delivering atleast 2X speed gain over other implemen- tations, which henceforth allows real time image segmentation that can be deployed on Mobile Robotic systems.