MATLAB and Simulink Training

Image Processing with MATLAB

Course Details

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
  • Enhancing images
  • Detecting edges and shapes
  • Segmenting objects based on their color and texture
  • Modifying objects' shape using morphological operations
  • Measuring shape properties
  • Performing batch analysis over sets of images
  • Aligning images with image registration
  • Detecting, extracting, and matching image features

Day 1 of 2

Importing and Visualizing Images

Objective: Import and visualize different image types in MATLAB. Manipulate images for streamlining subsequent analysis steps.

  • Importing, inspecting, 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.

  • Extracting and labeling objects in a segmentation mask
  • Measuring shape properties
  • Separating adjacent and overlapping objects with watershed transform

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

Level: Intermediate


Duration: 2 days

Languages: English, 日本語, 한국어, 中文