• 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.

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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. 

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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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Scilab for Image Processing & Computer Vision

Fundamental and Practical Hands-On with Scilab
Our IPCV module is base on famous OpenCV library, in which the power of both Scilab and OpenCV has been integrated to provide a quick solution for image processing and computer vision.
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More than 100 functions of Image and Video Processing are now available for Scilab, making Scilab a great tool for Image Processing.”
 Course Synopsis

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a sub-field of digital signal processing, digital image processing has many advantages over analogue image processing; it allows a much wider range of algorithms to be applied to the input data, and can avoid problems such as the build-up of noise and signal distortion during processing. Computer vision deals with how computers can be used for gaining high level understanding from digital images or video with the aim to automate human visual system.

This training provides the foundation concepts and techniques for digital image processing and computer vision with Scilab with the home-grown use of the IPCV module (Image Processing and Computer Vision module), The IPCV module extends the capabilities of its predecessors on principles, theories, algorithms and practical implementation.

The IPCV has been one of the most downloaded modules for Scilab in recent months. By participating in this training, your exploration into IPCV is complete with the training materials, illustrated with numerous examples of practical significance by our very own developers.

Come and participate in this training program to prepare yourself in dealing with image processing and computer vision. Join us now!

Course Objectivesimage004
This two-day course provides a fundamental concepts and techniques for digital image processing and the software principles used in their practical implementation. The material will be illustrated with numerous examples of practical significance.
Who Must Attend
Engineer, technical support officers, and managers from the manufacturing, government and defense sectors who want to use or plan to use imagel processing, to learn the fundamental knowledge and how to use SCILAB for image processing.
Basic knowledge of computer operation and image processing.

Course Outline

Fundamental of Images Processing and Computer Vision

  • Image representation in Scilab
  • Importing and exporting images
  • Displaying the image with Scilab
  • What is image pixel and what does the value means
  • Converting image between Data Classes and Image Types
  • Image Characteristics
  • Image Processing vs Computer Vision 

Source of Images, from Pictures, Video and Camera

  • Importing image from pictures
  • Importing video to Scilab
  • Acquiring image from usb webcam to Scilab
  • Viewing the acquired image

Image Enhancement

  • Adjusting image intensity
    • Histogram stretching
    • Histogram equalization
    • Histogram adjustment
  • Enhancing images using arithmetic operations
    • Addition – Increase brightness
    • Multiplication – Increase sharpness
    • Subtraction – Detect change
    • Division – Detect change
  • Correcting image alignment
    • Rotating images
    • Affine Transform
    • Perspective Transform
  • Cropping and resizing images

Filtering Images

  • Block processing
    • Defining block processing
    • Distinct block operations
    • Sliding neighborhood operations
  • Image convolution and correlation
  • Spatial domain filtering
  • Frequency domain filtering
  • Region of interest processing

Image Restoration

  • Reducing noise
    • Modeling noise
    • Filtering noise
  • Deblurring images
  • Correcting background illumination

Image Feature Extraction

  • Image thresholding
  • Edge detection
    • Edge detection function
    • Radon transform
  • Morphological segmentation
    • Structuring element
    • Dilation and erosion
    • Measuring region properties
  • Color-based image segmentation

Real-time Frames Processing

  • Real-time frames processing with webcam


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