MATLAB & Simulink

IMAGE PROCESSING AND COMPUTER VISION

Image Processing with MATLAB
Hands-on Course with Practical Exercises

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

This course is intended for engineers, researchers, scientist who are working to enhance, segment and extract features from images 

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Course Prerequisites

Attended 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 

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 for analysis by using common preprocessing techniques such as contrast adjustment and noise filtering..  

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• Adjusting contrast 

• Reducing noise with spatial filtering

• Equalizing inhomogeneous background 

• Processing images in distinct blocks

• Measuring image quality

Color and Texture Segmentation

Objective: Segment objects from an image based on color and texture. Use statistical measures to characterize texture features and measure texture similarity between images. 

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• Transforming between image color spaces

• Segmenting objects based on color attributes and color difference. 

• Segmenting objects based on texture using nonlinear filters.

• Analyzing image texture using statistical measures like contrast and correlation

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Improving Segmentation

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Objective:  Improve binary segmentation results by refining the segmentation mask. Use interactive and iterative techniques to segment image regions.

• Using morphological operations to refine segmentation masks

• Segmenting images and refining results interactively

• Using iterative techniques to evolve segmentation from a seed

Day 2 of 2

Finding and Analyzing Objects

Objective: Count and label objects detected in a segmentation. Measure object properties like area, perimeter, and centroids.

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• Detecting object edges 

• Segmenting objects by finding straight lines and circles 

• Performing batch analysis over sets of images 

Detecting Edges and Shapes

Objective: Detect edges of objects and extract boundary pixel locations. Detect objects by shapes such as lines and circles 

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• Detecting object edges

• Identifying objects by detecting lines and circles

• Performing batch analysis over sets of images

Spatial Transformation and Image Registration

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

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• Applying geometric transformations to images

• Aligning images using phase correlation

• Aligning images using point mapping

Automating Image Registration with Image Features

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

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• Detecting and extracting features

• Matching features to estimate geometric transformation between two images