Nº
647
DATE: CALL
PRICE NIS: 5960 + VAT /16 Tcs
DURATION: 4 Days
Course Overview:Attending this course will give you principles in using and designing Digital Image Processing algorithms used in the academy and industry today. Some ®MATLAB tools will be demonstrated as part of the training.
Who should attend:This course is intended for engineers having some mathematical background in Signal Processing that want to broaden their knowledge in Image Processing theory
Prerequisite:Familiarity with Basic Signal Processing Theory. Some experience with ®MATLAB programming
Tools used:
®MATLAB
Topics Include:
Course Outline:
1. Introduction
In this chapter we give an introduction to Digital Image Processing followed by some examples

What is Digital Image Processing?

The Origin of Digital Image Processing

Examples of fields that use Digital Image Processing

Fundamental steps in Digital Image Processing

Components of an Image Processing System
2. Digital Image Fundamentals
In this chapter we learn about the fundamentals of Digital Images and the connection to the Visual Perception

Elements of visual perception

Light and ElectroMagnetic spectrum

Image sampling and quantization

Same basic relationship between pixels
3. Image Enhancement in the Spatial Domain
In this chapter we learn about the Image Enhancement using some Spatial Domain Techniques

Background

Some basic gray level Transformations

Histogram Processing

Enhancement Arithmetic/Logic operations

Basics of Spatial filtering

Smoothing Spatial filtering

Sharpening Spatial filtering
4. Image Enhancement in the Frequency Domain
In this chapter we learn about the Image Enhancement using some Frequency Domain Techniques

Background

Introduction to the Fourier Transform and the Frequency Domain

Smoothing Frequency Domain filters

Sharpening Frequency Domain filters

Homomorphic filtering

Implementation
5. Image Restoration
In this chapter we look at the problem of image degradation and the process of restoration to solve this problem

A model of the Image Degradation/Restoration process

Noise models

Periodic noise reduction by frequency domain filtering

Linear Position – Invariant degradation

Estimation the degradation function

Inverse filtering

Constrained Least Squares filtering

Geometric mean filtering

Geometric transformations
6. Multiresolution Processing
In this chapter we look at the mathematics of multiresolution analysis with the use of wavelet Transform

Background

Multiresolution Expansion

Wavelet Transform

Fast Wavelet Transform

Wavelet Packet
7. Image Compression
In this chapter we learn about Image compression techniques

Fundamentals

Image compression models

Elements of Information Theory

ErrorFree compression

Lossy compression
8. Image Segmentation
In this chapter we look at techniques for Image Segmentation

Detection of discontinuities

Edge Linking and Boundary Detection

Thresholding

Regionbased segmentation

Segmentation by Morphological Watersheds

The use of Motion in segmentation
9. Object Recognition
In this chapter we look the mathematics of multiresolution analysis with the use of wavelet Transform

Patterns and Pattern classes

Recognition based on DecisionTheoretic methods

Neural Networks

Structural methods
10. Advanced Topics

Radon Transform

Hough Transform

Machine Vision

Machine Learning
11. Summary