IMAGE SEGMENTATION USING THRESHOLDING TECHNIQUE FPGA NEXYS A7 BOARD

Abstract

Nowadays, with the introduction of Graphics Processing Unit for general purpose issues, not only graphical ones, there has been an increasing attention towards exploiting GPU processing power for deep learning algorithms. In the world of technology as considered by many scientist and analysts everything is going very fast.In this thesis I will use FPGA boards rather than graphical card processing using like NVidia, as a case study in observation of behavior regarding with image segmentation.

Therea are several qualities that distinguish both processors, with classical graphical processing units being more flexible, not much complex and vice versa with FPGA processors offering programmability, reducing time latency, optimized energy in computation process. For while, it has been an enigma the comparison between two different mentalities ( software vs. hardware ) engineering mentalities will occur, thus the results will be compared to each-other like energy used, flip-flops, LUT-s etc. The whole system will be implemented in Xilinx Nexus A7 FPGA board and Vivado HLS 2018.2 software framework.