Training - Courses

IP01: MATLAB for Image Processing

This two-day course shows how to perform various image processing techniques using the Image Processing Toolbox. The course explores the different types of image representations, how to enhance image characteristics, image filtering, and how to reduce the effects of noise and blurring in an image. It also introduces different methods used to extract features and objects within an image, image registration, and a few techniques for reconstructing images/objects.

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	Detailed course outline

Day 1 of 2
Introduction	Objective: Understand MathWorks products, a brief company history, and course schedule Introduction to The MathWorks Product family overview Product overview Course outline Computer setup
Working with Images	Objective: Understand different image types available in MATLAB, and how they can be read in Exploring image types Supported MATLAB data types for representing images Binary image Intensity image Indexed image RGB image Importing and exporting images in MATLAB Viewing the image Single image Multiple image frames Finding image pixel values: IMPIXVALINFO Calculating image statistics Converting image formats
Applying Image Enhancement Techniques	Objective: Enhance image characteristics by adjusting the image intensity and isolating the region of interest Adjusting image intensity Histogram stretching Histogram equalization Histogram adjustment Using arithmetic functions to enhance images Addition – increase brightness Multiplication – increase sharpness Subtraction – detect change Division – detect change Correcting image alignment: rotating Cropping and resizing images
Filtering Images	Objective: Understand how block processing works and investigate the implementation of both spatial domain and frequency domain filters Processing an image as blocks Block processing definition Distinct block operations Sliding neighborhood operations Performing image convolution and correlation Designing and implementing spatial domain filters Averaging filter Designing and implementing frequency domain filters Highpass Lowpass Bandpass Processing the region of interest

Day 2 of 2
Image Restoration Techniques	Objective: Reduce the effects of unwanted distortions, such as noise, blurring, and background illumination Reducing noise from images Modeling noise Filtering noise Deblurring images Correcting background illumination
Feature Extraction Using Segmentation and Edge Detection	Objective: Extract image features and measurements using different segmentation and edge detection methodologies Isolating image features using thresholding Detecting edges in an image Edge detection functions Radon transform Hough Transform Performing morphological segmentation Creation of structuring elements Dilation and erosion Measurement of region properties Applying color-based image segmentation
Image Registration and Image Reconstruction	Objective: Implement image registration using the Image Processing Toolbox and review examples of reconstruction methodologies Exploring the basics of image registration Selecting control points Register an image Reconstructing images and objects Performing morphological reconstruction
Case Studies	Objective: This section is intended for investigation and problem solving using a set of case studies. This section discusses the following cases: Motion detection Text recognition Finding particles Bouncing ball Ball tracking Microarray analysis

Prerequisites

MATLAB Fundamentals and Programming Techniques (ML01) or equivalent experience using MATLAB.

Course Length - 2 days

Price - $1,300.00

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Training - Courses

IP01: MATLAB for Image Processing

Prerequisites

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