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Digital images and videos are everywhere these days – in thousands of scientific (e.g., astronomical, bio-medical), consumer, industrial, and artistic applications. Moreover they come in a wide range of the electromagnetic spectrum - from visible light and infrared to gamma rays and beyond. The ability to process image and video signals is therefore an incredibly important skill to master for engineering/science students, software developers, and practicing scientists. Digital image and video processing continues to enable the multimedia technology revolution we are experiencing today. Some important examples of image and video processing include the removal of degradations images suffer during acquisition (e.g., removing blur from a picture of a fast moving car), and the compression and transmission of images and videos (if you watch videos online, or share photos via a social media website, you use this everyday!), for economical storage and efficient transmission.

This course will cover the fundamentals of image and video processing. We will provide a mathematical framework to describe and analyze images and videos as two- and three-dimensional signals in the spatial, spatio-temporal, and frequency domains. In this class not only will you learn the theory behind fundamental processing tasks including image/video enhancement, recovery, and compression - but you will also learn how to perform these key processing tasks in practice using state-of-the-art techniques and tools.

MATLAB (matrix laboratory) is a multi-paradigm numerical computing environment and fourth-generation programming language. MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include: Math and computation. Algorithm development. Etc.

Topics to Be Covered

Introduction to MATLAB

  • What is MATLAB
  • The dominance of MATLAB over other languages
  • Power of Matrix computations
  • The application of MATLAB in various fields of engineering
  • MATLAB Environment

Arithmetic Functions in MATLAB

  • Matrixes and Vectors
  • Creating Matrixes and Vectors
  • Matrix Operation
  • Array Operation
  • Indexing Matrix
  • Adding Elements to Vector or Matrix
  • Various Flow Control used in MATLAB
  • 2D & 3D graphical Plotting 

Introduction to Signal & System 

  • 2D and 3D Discrete Signals
  • Complex Exponential Signals
  • Linear Shift-Invariant Systems
  • 2D Convolution
  • Filtering in the Spatial Domain

 Introduction to Simulink 

  • Introduction to Simulink
  • Applications of simulink in System modelling
  • Modelling Basic electrical Circuit in Simulink and obtaining characteristic plots.

 Fourier Transform and Sampling

  • 2D Fourier Transform
  • Sampling
  • Discrete Fourier Transform
  • Filtering in the Frequency Domain
  • Change of Sampling Rate

 Introduction to Image Processing

  • What is Image Data
  • Image Processing Toolbox
  • Importing Image
  • How to build a matrix image
  • Image Display
  • Image Operations
  • Image Conversion

 Image Arithmetic

  • Adding Images
  • Subtracting Images
  • Multiplying Images
  • Dividing Images
  • Spatial Transformation
  • Resizing Images
  • Rotating Images
  • Cropping Images

 Image Filtration

  • What is Image Restoration
  • Noise and Images
  • Noise Models
  • Noise removal using spatial domain filtering
  • Periodic noise
  • Noise removal using frequency domain filtering

 Morphological Image Processing

  • Mathematic Morphology
  • Z2 and Z3
  • Basic set theory
  • Logic Operations
  • Structuring Element
  • How to describe Structuring Element
  • Basic Morphological Operations
  • Erosion
  • Dilation
  • Combining Erosion and Dilation
  • Filtering Application

 Introduction to Graphical User Interface

 Application and Demos 

Image Recovery

  • Examples of Image and Video Recovery
  • Image Restoration
  • Matrix-Vector Notation for Images
  • Inverse Filtering
  • Constrained Least Squares
  • Set-Theoretic Restoration Approaches
  • Iterative Restoration Algorithms
  • Iterative Least-Squares and Constrained Least-Squares
  • Spatially Adaptive Algorithms

 Image Compression 

  • Scalar Quantization
  • Vector Quantization
  • Differential Pulse-Code Modulation
  • Fractal Image Compression
  • Transform Coding
  • JPEG
  • Subband Image Compression

 Video Compression 

  • Motion-Compensated Hybrid Video Encoding
  • On Video Compression Standards
  • 261, H.263, MPEG-1 and MPEG-2
  • MPEG-4
  • 264
  • 265

 Image and Video Segmentation

  • Methods Based on Intensity Discontinuity
  • Methods Based on Intensity Similarity
  • Watersheds and K-Means Algorithms
  • Advanced Methods

Eligibility Criteria : Electrical, Electronics, Instrumentation Engineering Students. Students entering into 2nd Year to Final Year Students can participate in this training Program. However students from any branch can participate in this training program.

Certification Policy:

  • Certificate of Merit for all the workshop participants.
  • Certificate of Coordination for the coordinators of the campus workshops

Duration: 5 Days - The duration of this workshop will be five consecutive days, with 6-7 hour session each day.