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A Python-based laboratory course for image and video signal processing on embedded systems

机译:基于Python的实验室课程用于嵌入式系统上的图像和视频信号处理

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摘要

The usage of embedded systems is omnipresent in our everyday life, e.g., in smartphones, tablets, or automotive devices. These devices are able to deal with challenging image processing tasks like real-time detection of faces or high dynamic range imaging. However, the size and computational power of an embedded system is a limiting demand. To help students understanding these challenges, a new lab course "Image and Video Signal Processing on Embedded Systems" has been developed and is presented in this paper. The Raspberry Pi 3 Model B and the open source programming language Python have been chosen, because of low hardware cost and free availability of the programming language. In this lab course the students learn handling both hard- and software, Python as an alternative to MATLAB, the image signal processing path, and how to develop an embedded image processing system, from the idea to implementation and debugging. At the beginning of the lab course an introduction to Python and the Raspberry Pi is given. After that, various experiments like the implementation of a corner detector and creation of a panorama image are prepared in the lab course. Students participating in the lab course develop a profound understanding of embedded image and video processing algorithms which is verified by comparing questionnaires at the beginning and the end of the lab course. Moreover, compared to a peer group attending an accompanying lecture with exercises, students having participated in this lab course outperform their peer group in the exam for the lecture by 0.5 on a five-point scale.
机译:嵌入式系统的使用在我们的日常生活中无处不在,例如在智能手机,平板电脑或汽车设备中。这些设备能够处理具有挑战性的图像处理任务,例如面部实时检测或高动态范围成像。但是,嵌入式系统的大小和计算能力是一个有限的需求。为了帮助学生理解这些挑战,本文开发了一个新的实验课程“嵌入式系统上的图像和视频信号处理”。已选择Raspberry Pi 3 Model B和开源编程语言Python,因为其硬件成本低且该编程语言免费提供。在本实验课程中,学生将学习如何处理硬件和软件,从Python到MATLAB的替代方法,图像信号处理路径以及如何开发嵌入式图像处理系统(从构思到实现和调试)。在实验课程的开始,将介绍Python和Raspberry Pi。此后,在实验过程中准备了各种实验,如实现拐角检测器和创建全景图像。参加实验课程的学生对嵌入式图像和视频处理算法有了深刻的理解,可以通过比较实验课程开始和结束时的问卷来验证这一点。此外,与参加附带练习的同伴小组的学生相比,参加本实验课程的学生在同堂考试中的五分制考试成绩优于同伴小组。

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