Exploring Image Processing with MATLAB, SIMULINK and Raspberry PI

Course Highlights

This three-day course shows how to perform various image processing techniques using the Image Acquisition Toolbox and Image Processing Toolbox in MATLAB environment and Computer Vision System Toolbox in SIMULINK environment. The course introduces image processing fundamentals as a startup. The course explores the process of acquiring image using webcam, different types of image representations, how to enhance image characteristics, image filtering, and how to reduce the effects of noise and blurring in an image. It also introduces different methods used to extract features and objects within an image and introduction to Computer Vision System Toolbox. The algorithm implementation on Raspberry Pi is covered at the end of the training.

Course Objectives

The aim of the course is to provide basic knowledge for participants to acquire image using the Image Acquisition Toolbox, to perform various image processing techniques using the Image Processing Toolbox and Video and Image Processing Blockset, to implement basic image processing algorithm on hardware.

Who Must Attend

 Engineer, researchers, scientists, and managers from the manufacturing, government and defense sectors who want to use or plan to use image processing, to learn the fundamental knowledge in image processing, to know how to use MATLAB and SIMULINK for image processing, or to be involved in the purchase of products that involve image processing.

Course Benefits

 Upon the completion of the course, the participants will gain a comprehensive understanding on utilizing the Image Acquisition Toolbox and Image Processing Toolbox in MATLAB environment and Computer Vision System Toolbox in SIMULINK for image processing by algorithm for their specific applications. The participants will be able to understand the design flow for implementing image processing algorithm from algorithm concept to hardware simulation.

Prerequisites

Attended "Comprehensive MATLAB ", "Comprehensive SIMULINK" and experience with basic computer operations. Basic knowledge of signal processing and image processing concepts is strongly recommended but not a must.

Course Outline


Day 1
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.

  • Importing and displaying images
  • Converting between image types
  • Exporting images

Interactive Exploration of Images
Objective: Interactively explore object details such as size and color to find start values for subsequent image processsing steps. To apply the steps repeatedly, you can create custom image exploration tools from the interactive modules.

  • Obtaining pixel intensity values
  • Extracting a region of interest (ROI)
  • Computing pixel statistics on a region of interest
  • Measuring object sizes
  • Creating a custom interactive tool 

Preprocessing Images
Objective: Preprocess images by filtering, and using contrast adjustment to simplify or allow for image analysis steps.

  • 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. Use the same techniques for creating panorama images.

  • Geometric transformations
  • Image registration using point mapping
  • Creating a panoramic scene

Day 2

Edge and Line Detection
Objective: Segment edges of objects and extract boundary pixel locations. Detect lines and circles in an image.

  • 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.

  • Color space transformation
  • Color segmentation
  • Texture segmentation
  • Texture-based image classification

Feature Extraction
Objective:Analyze and modify the objects' shape to improve segmentation results. Count the detected objects and calculate object features like area or centroids.

  • Counting objects
  • Measuring shape properties
  • Using morphological operations
  • Performing watershed segmentation

Day 3

Acquiring and Viewing an Image in MATLAB
Objective: Acquire an image using the Image Acquisition Toolbox and to view the image in MATLAB

  • Connecting the hardware
  • Retrieving hardware information
  • Creating a video input object
  • Configuring the video input object
  • Previewing the video stream
  • Acquiring the image data
  • Viewing the acquired image

Working with Images in Simulink
Objective: Explore some features of Computer Vision System Toolbox for image Processing

  • Importing and exporting Images with Computer Vision System Toolbox
  • Color conversion
  • Image Statistics
  • Image Enhancement
  • Object Counting 

Raspberry Pi Hardware Implementation using MATLAB and SIMULINK
Objective: Provide overview of Raspberry Pi hardware and how to get started with the hardware to implement Image Processing algorithm on MATLAB and SIMULINK environment 

  • Find and install support package for third-party hardware or software using supportPackageInstaller
  • Connect to Raspberry Pi hardware from MATALB
  • Connect to Raspberry Pi hardware from SIMULINK
  • Raspberry Pi library blocks
  • Configuration parameters dialog box overview
  • Run on target hardware pane
  • Tune and monitor models running on target hardware
  • Build simple model to run on Raspberry Pi hardware

 

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