Learning Computer Vision with Raspberry Pi by Examples 

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A Quick Prototype Approach with Scilab!

Learning Computer Vision with Scilab via Examples! 

 


 “Learning Computer Vision with Scilab in Pi is Much Easier!”

 

Course Synopsis

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Raspberry Pi has become a hot topic for researchers and hobbyists in various fields due to its wide usage. While Python/C++ has becoming the common programming languages for Pi development, other high level languages are able to find their place with the Pi as its processing ability is getting more powerful.

 

Cousrse Objectives

This course is specially designed for those who has knowledge of image processing and would like to explore how to use Raspberry Pi for Computer Vision. The course would use 4 examples/projects to let the participants diving direct into the computer vision world with Pi.

Taking the advantage of Scilab able to integrate with other languages, we develop a module to utilize the power of OpenCV but with the easy-to-learn Scilab syntax. 

 

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Who Must Attend

This course is suitable for participants who would like to embrace Computer Vision by using the Raspberry Pi as the platform.

Prerequisites

Participants should have knowledge on Image Processing, and programming backgroup such as using Scilab, Python, C/C++, Matlab, or others. 

 

Course Methodology

This course is presented in a workshop style with example-led lectures interlaced with demonstrations and hands-on practical for maximum understanding.

 


Course Outline

  • Introduction
    • Briefing on how the courses would be conducted
    • Overview of the software chain for the course
    • Some comparison, pros and cons of the software chain used.
  • Object Counting
    • Count the objects by using morphological operation
    • Count the objects by color
  • Motion Detection
    • Detect of the motion with image arithmetic
    • Saving motions to the disk
  • Simple Machines Learning
    • Training the neural network to classify color object
    • Using the LAB color space for color differentiation
  • Line following Robot
    • Using image processing technique to detect line
    • Sending command to a pre-programmed Arduino to move the mobile robot

 

To obtain details of the course (fee, location and etc.), kindly obtain a registration form by email tina@tritytech.com

Provide us with your name, organization & mobile contact number.

You may also call us at +603-80637737 or fill up our Training Enquiry form.

 

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