• Trity Course Scilab AI

    Artificial Intelligence with Scilab

    28-29 Jun 2018/ 26-27 July 2018Read more
  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

    6-7 Jun 2018/ 30-31 July 2018Read more
  • Trity Course Scilab DM

    Scilab for Data Mining

    4-5 July 2018Read more
  • Python Deep Learning

    Python for Machine and Deep Learning

    26-27 July 2018Read more
  • Python Data Science

    Python for Data Science Fundamentals

    12-13 July 2018Read more
  • Python for IPCV

    Python for Image Processing and Computer Vision

    19-20 July 2018Read more

Scilab Courses

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Scilab is an open source, cross-platform numerical computational package and a high-level, numerically oriented programming language. It can be used for signal and image processing, statistical analysis, Internet of Things, data mining, etc. In Trity Technologies we have developed more than 20 courses based on Scilab since last few years.

More about Scilab Courses

 

Raspberry Pi Courses

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The Raspberry Pi is a series of credit card–sized single-board computers developed in the United Kingdom by the Raspberry Pi Foundation with the intent to promote the teaching of basic computer science in schools and developing countries. Our very first Raspberry Pi Training is the aplication in IoT, and we are extending the training into other fields from time to time. 

More about Raspberry Pi Courses

E4Coder - Automatic Code Generation

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E4Coder is a set of tools that can be used to simulate control algorithms and to generate code for embedded microcontrollers running with or without a realtime operating system. Our course focus on using the block diagram for algorithms development and the codes would be automatically generated and downloaded into the embedded boards such as Arduino Uno. A mobile robot application would be used for the training for practical hands-on. 

More about CG Courses


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! 

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 “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

 

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