Abstract
This paper presents a low power near real-time pattern recognition technique based on Mathematical Morphology-MM implemented on FPGA (Field Programmable Gate Array). The key to the success of this approach concerns the advantages of machine learning paradigm applied to the translation invariant template-matching operators from MM. The paper shows that compositions of simple elementary operators from Mathematical Morphology based on ELUTs (Elementary Look-Up Tables) are very suitable to embed in FPGA hardware. The paper also shows the development techniques regarding all mathematical modeling for computer simulation and system generating models applied for hardware implementation using FPGA chip. In general, image processing on FPGAs requires low-level description of desired operations through Hardware Description Language-HDL, which uses high complexity to describe image operations at pixel level. However, this work presents a reconfiguring pattern recognition device implemented directly in FPGA from mathematical modeling simulation under Matlab/Simulink/System Generator environment. This strategy has reduced the hardware development complexity. The device will be useful mainly when applied on remote sensing tasks for aerospace missions using passive or active sensors.
Highlights
Space missions for deep space exploration or remote sensing using small satellites faces major difficulties regards energy consumption and communication downlinks for big volumes of data
One of the most promising proposals to overcome these difficulties is the development of new low power embedded intelligent devices that can provide smart compression method for images and/or that can recognize patterns near real-time using low power consumption on the spacecraft
The focus of the current work are items (e) and (f), concerning the techniques for mathematical modeling implementation of morphological operators applied to near real time pattern recognition embedded in FPGA
Summary
Space missions for deep space exploration or remote sensing using small satellites faces major difficulties regards energy consumption and communication downlinks for big volumes of data. In the specialized literature there are other important papers with contributions from Mathematical MorphologyMM and machine learning hybridization techniques (Nogueira et al, 2021; Franchi et al, 2020; Jouni et al, 2020; Shen et al, 2019; Mellouli et al, 2019; Hao et al, 2019) None of these works has implemented in hardware any morphological operators based on Elementary Look-Up Tables–ELUTs paradigm as proposed in Silva (1998) and summarized at Section 2.
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