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

  • Detecting, extracting, and matching image features

  • Removing noise

  • Aligning images

  • Detecting edges, lines, and circles in an image

  • Segmenting objects based on their color and texture

  • Modifying objects' shape using morphological operations

  • Performing batch analysis over sets of images

  • Measuring shape properties

Prerequisites

MATLAB Fundamentals or equivalent experience using MATLAB. Basic knowledge of image processing concepts is strongly recommended.​

Partners 

Upcoming Program

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Course Outline

Day 1 of 2

Importing and Visualizing Images

 

Objective: Import images into MATLAB and visualize them. Convert the images to a format that is useful for subsequent analysis steps.

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  • Importing and displaying images

  • Converting between image types

  • Exporting images

 

Preprocessing Images

 

Objective: Preprocess images by filtering, and using contrast adjustment to simplify or allow for image analysis steps. 

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  • Adjusting image contrast

  • Reducing noise in an image by filtering

  • Handling inhomogenous background

  • Applying custom functions block by block

 

Spatial Transformation and Image Registration

 

Objective: Compare images with different scaling and orientation by aligning them. 

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  • Geometric transformations

  • Image registration using point mapping

  • Image registration using phase correlation

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Automating Image Registration with Image Features

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

  • Detecting and extracting features

  • Matching features

  • Estimating geometric transformations between images

Day 2 of 2

Edge and Line Detection

 

Objective: Segment edges of objects and extract boundary pixel locations. Detect lines and circles in an image.

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  • Segmenting object edges

  • Detecting straight lines

  • Performing batch analysis over sets of images

  • Detecting circular objects

 

Color and Texture Segmentation

 

Objective: Segment objects based on color or texture. Use texture features for image classification.

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  • Color space transformation

  • Color segmentation

  • Texture segmentation

  • Texture-based image classification

 

Feature Extraction

 

Objective: Analyze and modify the object's shape to improve segmentation results. Count the detected objects and calculate object features like area or centroids.

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  • Counting objects

  • Measuring shape properties

  • Using morphological operations

  • Performing watershed segmentation