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.​

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Mathworks Sole and Authorised Distributor and Training Partner

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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