Training - Courses

MLIP: Image Processing with MATLAB

This two-day course shows how to use Image Processing Toolbox™ to perform various image processing techniques. The course explores the different types of image representations as well as how to enhance image characteristics, filter an image, and 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 techniques for reconstructing images and objects.

Note: A 1 hour test session will be scheduled one day prior to the first day of class. This session is to verify that the visual and audio connection is working properly on your computer. The required product software should be installed for the test session. It is highly recommended that you attend this session to ensure a successful and timely class start.

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	Detailed course outline

Day 1

Working with Images

Objective: Understand different image types available in MATLAB, and how they can be read into MATLAB.

Image types
Supported MATLAB data types for representing images
Binary images
Grayscale images
Indexed images
RGB images
Importing and exporting images in MATLAB
Viewing images
Single image
Multiple image frames
Finding image pixel values
Calculating image statistics
Converting image formats

Image Enhancement Techniques

Objective: Enhance image characteristics by adjusting the image intensity and isolating a region of interest.

Adjusting image intensity
Histogram stretching
Histogram equalization
Histogram adjustment
Using arithmetic functions to enhance images
Correcting image alignment: rotating
Cropping and resizing images
Exploring the basics of image registration
Selecting control points
Registering an image
Correcting lens distortion

Filtering Images

Objective: Understand how block processing works; implement spatial-domain and frequency-domain filters; and use filtering techniques to reduce the effects of unwanted distortions such as noise, blurring, and background illumination or to enhance an image.

Defining filtering
Filtering process
Performing filtering
Filtering applications: smoothing, edge detection, and sharpening
Frequency-domain filter design
Modeling and removing noise
General block operations
Region-of-interest processing
Specific applications of filtering

Day 2 of 2

Feature Extraction and Segmentation

Objective: Extract image features and measurements using different segmentation methodologies.

Isolating image features using thresholding
Performing morphological segmentation
Creation of structuring elements
Erosion and dilation
Measurement of region properties
Reconstructing images and objects
Performing morphological reconstruction
Detecting edges in an image
Hough transform
Applying color-based image segmentation
Isolating objects using watershed segmentation
Segmenting images based on texture

Prerequisites

MATLAB Fundamentals or equivalent experience using MATLAB^®. A basic knowledge of image processing concepts is strongly recommended.

Course Length - 2 days