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Image Processing with MATLAB®
Image Processing and Computer Vision
Image Processing with MATLAB
Learn to analyze images in MATLAB and Image Processing Toolbox
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
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
Who Should
Attend
This course is intended for engineers, researchers, scientist who are working to enhance, segment and extract features from images
Course
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB. Basic knowledge of image processing concepts is strongly recommended.
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
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
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
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