Image Processing Applications on Raspberry Pi - From Scratch
Details
Image Processing Applications on Raspberry Pi is a beginner course on the newly launched Raspberry Pi 3 and is fully compatible with Raspberry Pi 2 and Raspberry Pi Zero.
The course is ideal for those who are new to the Raspberry Pi and want to explore more about it.
You will learn the components of Raspberry Pi, connecting components to Raspberry Pi, installation of NOOBS operating system, basic Linux commands, Python programming and building Image Processing applications on Raspberry Pi.
This course will take beginners without any coding skills to a level where they can write their own programs.
Basics of Python programming language are well covered in the course.
Building Image Processing applications are taught in the simplest manner which is easy to understand.
Users can quickly learn hardware assembly and coding in Python programming for building Image Processing applications. By the end of this course, users will have enough knowledge about Raspberry Pi, its components, basic Python programming, and execution of Image Processing applications in the real time scenario.
The course is taught by an expert team of Electronics and Computer Science engineers, having PhD and Postdoctoral research experience in Image Processing.
Anyone can take this course. No engineering knowledge is expected. Tutor has explained all required engineering concepts in the simplest manner.
The course will enable you to independently build Image Processing applications using Raspberry Pi.
This course is the easiest way to learn and become familiar with the Raspberry Pi platform.
By the end of this course, users will build Image Processing applications which includes scaling and flipping images, varying brightness of images, perform bit-wise operations on images, blurring and sharpening images, thresholding, erosion and dilation, edge detection, image segmentation. User will also be able to build real-world Image Processing applications which includes real-time human face eyes nose detection, detecting cars in video, real-time object detection, human face recognition and many more.
The course provides complete code for all Image Processing applications which are compatible on Raspberry Pi 3/2/Zero.
Who is the target audience?
- Anyone who wants to explore Raspberry Pi and interested in building Image Processing applications
- Only High School Maths
- No prior programming knowledge is expected
- All the code files and images used in this course will be provided
- Hardware needed: Raspberry Pi 3/2/Zero, Monitor, Mouse, Keyboard, HDMI-VGA connector, USB flash drive (minimum storage capacity 2 GB), Micro SD card (minimum storage capacity 8 GB), Micro SD card reader, Power adapter (2 Amp, Micro-USB charger is preferred), USB Webcam (minimum 5 Megapixel resolution)
- What is Raspberry Pi? and what are its components?
- Understand peripherals that need to be connected to Raspberry Pi
- Wire up your Raspberry Pi to create a fully functional computer
- Easily learn preparing SD Card to load Operating System for Raspberry Pi
- Install packages needed to build Image Processing applications
- Learn basic programming aspects of Python
- Create simple Image Processing applications using Python and OpenCV
- Build real-world Image Processing applications on Raspberry Pi
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Outline
Speaker/s
Sharing his passion for passion for his pet subjects Machine Learning, Computer Vision and Data Science comes naturally to Dr. Steven Lawrence Fernandes. It is his rich researching skills gained while doing his PhD in the area of Deep Learning for Medical Image Classification at University of Alabama Birmingham that comes into play at the courses he teaches at Simpliv.
Dr. Fernandes specializes in researching and teaching Raspberry Pi. This is natural, considering that he has five years of experience in developing Computer Vision applications in Raspberry Pi. Dr. Fernandes loves to immerse himself head-on with developing various innovative Computer Vision applications on Raspberry Pi, a passion he is passionate about sharing with his students at Simpliv.
Dr. Fernandes has jointly published more than 50 Research Articles for sources such as Science Citation Indexed (SCI) Journals, Emerging Sources Citation Indexed (ESCI) Journals, and Scopus (Elsevier) indexed Journals. Contributed
into 22 Research Chapters for Springer Advances in Intelligent Systems and Computing (AISC) book series and presenting 18 Research Articles in various IEEE, ACM, Elsevier International Conferences are some of his other academic achievements.
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