• Trity Course Scilab IoT

    Scilab for the Internet of Things

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  • Trity Course RPi IoT

    Raspberry Pi for the Internet of Things (with Pi-3)

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  • Trity Course Scilab AI

    Artificial Intelligence with Scilab

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  • Trity Course Scilab NCV

    Numerical Computation and Visualization with Scilab

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  • Trity Course Scilab IP

    Scilab for Image Processing and Computer Vision

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  • Trity Course Scilab BDA

    Big Data Training Series : Practical Guide to Big Data Analytics with Pig Latin, Hive and Scilab

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  • Trity Course Scilab DM

    Scilab for Data Mining

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  • Python Deep Learning

    Python for Machine and Deep Learning

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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. 

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Computer Vision with Raspberry Pi


A Quick Prototype Approach with Scilab!

Learning Computer Vision with Scilab in Pi is Much Easier! 

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


Course Synopsis


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 introduces the Raspberry Pi as a platform for Computer Vision with Scilab - The Open Source Numerical Software which has been widely used in many platforms for research and development. Some prominent use case for Scilab can be found at http://scilab.io/.

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.



Who Must Attend

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


Participants should have basic background in programming and electronics


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
    • Overview of Raspberry Pi
    • Overview of Scilab
    • RaspPi + Scilab + OpenCV for Computer Vision
    • How Pi Sees images
  • Working with Images
    • Importing image from pictures
    • Acquiring image from usb webcam
    • Viewing the acquired image
    • Image pixel and its' values
    • Converting image between Data Classes and Image Types
  • Fundamental of Image Processing
    • Converting Colour to Grayscale
    • Adjusting image intensity
    • Enhancing images using arithmetic operations
    • Correcting image alignment
    • Cropping and resizing images
    • Converting Grayscale to Binary
  • Filtering Images
    • Image convolution and correlation
    • Spatial domain filtering
    • Frequency domain filtering
  • Feature Extraction
    • Image thresholding
    • Edge detection
    • Radon transform
    • Morphological segmentation
  • Color-based Image Processing
    • Procesing RGB Images
    • Color space conversion
  • RaspPi GPIO from Scilab
    • Control GPIO on Raspberry Pi with Scilab
  • Mini Project 1 - Objects Counting
    • Counting preferred objects
  • Mini Project 2 - Motion Detection
    • Object existence detection
  • Mini Project 3 - Machines Learning
    • Object identification with neural network
    • Decision making and controlling external devices


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