**Price**6010 + 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

Image Processing theory

- Visualizing and Analyzing processing results
- Improving algorithm performance

### 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 Electro-Magnetic 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
- Error-Free 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
- Region-based 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 Decision-Theoretic methods
- Neural Networks
- Structural methods

**10. Advanced Topics**

- Radon Transform
- Hough Transform
- Machine Vision
- Machine Learning

**11. Summary**