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Title Details:
Laboratory Exercise: Implementing an image algorithm on Nvidia Tegra X1 GPU SoC
Authors: Dasygenis, Minas
Soudris, Dimitrios
Description:
Abstract:
In recent years, the continually advancing technological developments have led to the creation of more complex and computationally intensive image processing algorithms. Many of these algorithms have been adopted in today’s embedded systems targeting various applications such as the automotive industry, 3D navigation, surveillance etc. However, in real-time embedded systems, where latency and power play a crucial role, software-oriented implementations for general-purpose CPUs may not offer satisfactory solutions. The purposes of this project are to design an image processing system for embedded applications, develop it on a System-on-Chip (SoC) platform and evaluate the developed System. The Harris Corner Detector algorithm was chosen to be implemented in a real-time system that captures the input image from a camera. The main goal of the project is to implement the Harris-Corner algorithm on a Tegra X1 GPU SoC. Optimizations of the algorithm for increased speed will also be studied.
Linguistic Editors: Kagiadaki, Sofia
Technical Editors: Dasygenis, Minas
Graphic Editors: Dasygenis, Minas
Type: Chapter
Creation Date: 28-08-2024
Item Details:
License: Attribution - NonCommercial - ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Handle http://hdl.handle.net/11419/13882
Bibliographic Reference: Dasygenis, M., & Soudris, D. (2024). Laboratory Exercise: Implementing an image algorithm on Nvidia Tegra X1 GPU SoC [Chapter]. In Dasygenis, M., & Soudris, D. 2024. Internet of Things Computing [Undergraduate textbook]. Kallipos, Open Academic Editions. https://hdl.handle.net/11419/13882
Language: Greek
Is Part of: Internet of Things Computing
Publication Origin: Kallipos, Open Academic Editions