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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Embedded Implementation of VHR Satellite Image Segmentation.

Chao Li¹, Souleymane Balla-Arabé², Dominique Ginhac³

¹Laboratory LE2I UMR 6306, CNRS, Arts et Métiers, University Bourgogne Franche-Comté, 21000 Dijon, France. chao.li.1986@ieee.org.

Sensors (Basel, Switzerland)

|May 31, 2016

Summary

This summary is machine-generated.

This study introduces an efficient texture region segmentation method for very high resolution (VHR) satellite images. The novel approach leverages parallel computing on Field Programmable Gate Arrays (FPGAs) for faster, cost-effective image analysis.

Keywords:

FPGA SW/HW co-design active contour model embedded system high performance computing high-level synthesis image segmentation lattice Boltzmann method level set method remote sensing

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Area of Science:

Remote Sensing
Computer Vision
Embedded Systems Engineering

Background:

Very High Resolution (VHR) satellite imagery offers critical data for urban planning, security, and environmental monitoring.
Processing VHR images is computationally intensive, posing challenges for time-sensitive applications like disaster management.
Advances in parallel computing and embedded systems, particularly Field Programmable Gate Arrays (FPGAs), offer efficient, cost-effective solutions.

Purpose of the Study:

To design and implement an efficient texture region segmentation method for VHR satellite images.
To leverage the parallel processing capabilities of embedded systems for high-performance image analysis.
To reduce the computational cost and processing time associated with VHR image segmentation.

Main Methods:

Developed a texture region segmentation algorithm using the level set algorithm and multi-kernel theory.
Implemented the algorithm in a high-abstraction C environment.
Achieved register-transfer level implementation using a novel high-level synthesis-based design flow on FPGAs.

Main Results:

The proposed design successfully segments texture regions in VHR satellite images with high quality.
Demonstrated significant advantages in terms of running costs compared to traditional methods.
Achieved high efficiency performance suitable for time-critical applications.

Conclusions:

The developed high-level synthesis-based design flow enables efficient FPGA implementation of complex image processing algorithms.
The texture region segmentation method offers a practical and cost-effective solution for VHR satellite image analysis.
This approach significantly enhances the feasibility of using VHR imagery for applications requiring rapid and accurate data processing.