Computer Vision Content Workshop

Computer Vision Content

"Computer vision is the science of endowing computers or other machines with vision, or the ability to see. Seeing is more than the process of recording light in a form that can be played back, like the recording of a video camera. It is actually the interpretation of images that leads to actions or decisions, as in the navigation of an autonomous robot. Like intelligence, there are many components to vision, including memory, retrieval, reasoning, estimation, recognition, and coordination. The goal of this workshop is to apply computer vision theories and models to the construction of computer vision systems.

Course Contents

  • Day 1-Session1-LAB
    • 1.Image Processing using Matlab
    • Introduction
    • What is computer vision, what is image processing?
    • Background
    • How images are captured in a camera. What are colors? How images are digitized.
    • What is Digital Image Processing?
    • What are pixels? How are they processed?
    • Video showing what all can be done with image processing.
    • Video showing what all we are going to teach.
    • Background on MATLAB and Image Processing Toolbox
    • What is Matlab? Why do we use it? What are the advantages?
    • The Matlab Working Environment
    • The Matlab Desktop
    • Using the MATLAB Editor to Create M-Files
    • Getting Help
    • Saving and Retrieving a Work Session
    • Fundamentals
    • Digital Image Representation
    • Coordinate Conventions
    • Image as Matrices
  • Day 1 - Session2 -LAB:
    • Introduction to matrices. Initializing. And basic operations
    • Introduction to M-File Programming
    • M-Files
    • Operators
    • Flow Control
    • Code Optimization
    • Interactive I/O
    • A Brief Introduction to Cell Arrays and Structures
    • Some Important Standard Arrays
    • Reading Images
    • 4 programs describing the various uses of imread function.
    • Displaying Images
    • Examples for imshow and imread.
    • Writing Images
    • Examples for imwrite. 4. Different syntaxes.
    • Data Classes
    • Image data classes. Uint8, then double etc. 4 programs on them.
    • Converting between Data Classes and Image Types
    • Conversions. Type casting.
    • Converting between Data Classes
    • Converting between Image Classes and Types
    • Use imtool and display two images. One in double and one in uint8. Show the difference.
    • Array Indexing
    • Vector Indexing
    • Matrix Indexing
    • Selecting Array dimensions
    • Accessing array elements.
    • Intensity Transformation and Spatial Filtering
    • Image Restoration
    • Color Image Processing
    • Color Image Representation in Matlab
    • RGB Images
    • Read a colour image, extract the three planes from it, and then display them.
    • Do some operations like interchange the planes to change the colors of the image.
    • Indexed Image
    • IPT Functions for Manipulating RGB and Indexed Images
    • Converting to Other Color Spaces
  • Day 2 - Session1 -LAB:
    • Examples for each conversion
    • Advantages of each over the others
    • NTSC Color Space
    • The YCbCr Color Space
    • The HSV Color Space
    • The CMY and CMYK Color Space
    • The HSI Color Space
    • The Basics Of Color Image Processing
    • Color Transformation
    • Spatial Filtering of Color Images
    • Filtering examples : smoothing and all.. .see fspecial function.
    • Color Image Smoothing
    • Color Image Sharpening
    • Sharpening examples
    • Working Directly in RGB Vector Space
    • Color Edge detection Using the Gradient
    • Examples on edge detection
    • Image Segmentation in RGB vector Space Day 2 - Session1 - LAB:
    • Examples programs for each.
    • Image Segmentation
    • Point, Line and Edge Detection
    • Point Detection
    • Line Detection
    • Edge Detection Using Function Edge
    • Thersholding
    • Global Thersholding
    • Local Thersholding
    • Region Based Segmentation
    • Basic Formulation
    • Region Growing
    • Region Splitting and Merging
  • Day 2 - Session 2 - LAB
    • 1.Object Tracking
    • Back Ground Subtraction
    • Tracking
    • 2.Applications.
    • Watermarking
    • Image retrieval
    • Data hiding
    • Face recognition
    • Gesture recognition
    • 3d technology
    • Vision based robots




Hands on Hours

12 Hours

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Praneeth Naidu

Technical Trainer


Technical Trainer

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