Scilab for Image Processing

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Fundamental and Practical Hands-On with Scilab
Our AIVP 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.
 
 

More than 100 functions of Image and Video Processing are now available for Scilab, making Scilab a great tool for Image Processing.”
 Course Synopsis
image003Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subfield of digital signal processing, digital image processing has many advantages over analog 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.
Since images are defined over two dimensions (perhaps more) digital image processing can be modeled in the form of Multidimensional Systems. Digital image processing allows the use of much more complex algorithms for image processing, and hence can offer both more sophisticated performance at simple tasks, and the implementation of methods which would be impossible by analog means.
 
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.
Pre-requisties
Basic knowledge of computer operation and image processing.

Course Outline

Fundamental of Images Processing

  • 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

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

Overview of Advance Topics

  • Image Registration
  • Compression
  • Pattern Matching

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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