MATLAB & Simulink

IMAGE PROCESSING AND COMPUTER VISION

Image Processing with MATLAB

This two-day course provides hands-on experience with performing image analysis. Examples and exercises demonstrate the use of appropriate MATLAB® and Image Processing Toolbox™ functionality throughout the analysis process.
Topics include:

• Importing and exporting images

• Removing noise

• Aligning images

• Detecting, extracting, and matching image features

• Detecting edges, lines, and circles in an image

• Segmenting objects based on their color and texture

• Modifying objects' shape using morphological operations

• Measuring shape properties

• Performing batch analysis over sets of image

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Who Should Attend

Engineers, researchers, scientist who are working to enhance, segment and extract features from images 

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Course Prerequisites

MATLAB Fundamentals

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Course Benefits

Upon the completion of the course, the participants will be able to

- explore images - enhance images

- perform image registration - segment regions in image

- extract features from image 

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Course Outline

Day 1 of 2

Importing and Visualizing Images

Objective: Import and display different types of images. Convert between image data types and formats to facilitate subsequent analysis steps. 

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• Importing and displaying images

• Converting between image types

• Visualizing results of processing

• Exporting images

Preprocessing Images

Objective: Enhance images to simplify analysis and processing. Use common preprocessing techniques such as histogram-based contrast adjustment and noise filtering to improve segmentation.  

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• Improving image contrast 

• Reducing noise in an image

• Equalizing inhomogeneous background 

• Processing images block by block 

Spatial Transformation and Image Registration

Objective: Compare two images with different scales and orientations by geometrically aligning them. 

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• Applying geometric transformations to images 

• Aligning images using point mapping 

• Automating intensity-based registration 

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Automating Image Registration with Image Features

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Objective:  Detect, extract, and match image features to automate image registration. 

• Detecting and extracting features

• Matching features to estimate geometric transformation between two images 

Day 2 of 2

Edge and Line Detection

Objective:  Detect edges of objects and extract boundary pixel locations. Detect objects by their shape. 

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• Detecting object edges 

• Segmenting objects by finding straight lines and circles 

• Performing batch analysis over sets of images 

Color and Texture Segmentation

Objective: Segment objects based on color and texture. Use statistical measures such as contrast and correlation to characterize texture features for image classification. 

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• Transforming between image color spaces 

• Segmenting objects based on a color difference metric 

• Using non-linear filters to segment objects based on texture 

• Analyzing image texture using statistical measures 

• Measuring texture similarity 

Feature Extraction

Objective: Analyze and modify objects' shape to improve segmentation results. Count and label the objects detected in an image. Extract object properties like area or centroid. 

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• Counting objects from a segmentation 

• Measuring shape properties 

• Using morphological operations to refine segmentation results 

• Performing watershed segmentation to improve object separation