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

Who Should Attend

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

Course Prerequisites

Attended MATLAB Fundamentals

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 

Partners 

09_MW_logo_RGB.jpg

Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner

Upcoming Program

xilinx ATP 黑.png

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. 

  • 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..  

  • 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. 

  • 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

Improving Segmentation

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.

  • 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 

  • 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.

  • 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.

  • Detecting and extracting features

  • Matching features to estimate geometric transformation between two images