Computer Vision with MATLAB

This two-day course provides hands-on experience with performing computer vision tasks. Examples and exercises demonstrate the use of appropriate MATLAB® and Computer Vision System Toolbox TM functionality.

Topics include:

  • Importing, displaying and annotating images and videos
  • Detecting, extracting and matching object features
  • Automatically aligning images using geometric transformations
  • Detecting objects in images and videos
  • Tracking objects and estimating their motion in a video
  • Removing lens distortion from images
  • Measuring planar objects 
  • Working with point clouds
  • Reconstructing a 3D scene from two or multiple images

Products

  • MATLAB, Image Processing Toolbox and Computer Vision System Toolbox


Prerequisites

  • Attended Comprehensive MATLAB or equivalent experience using MATLAB.
  • Basic knowledge of image processing and computer vision concepts


Course Outline

Day 1 of 2

Importing, Visualizing and Annotating Videos

Objective: Import videos into MATLAB, as well as annotate and visualize them. The focus is on using System Objects for performing iterative computations on video frames.

-Importing and displaying video files

-Highlighting objects by drawing markers and shapes like rectangles

-Combining and overlaying two images

-Performing iterative computations on video frames 

Detecting, Extracting and Matching Image Features

Objective: Use corner and blob detectors to detect local features in images. Extract and match features from two images. Use matched features to automatically align and stitch images.

-Detecting and extracting features in an image

-Matching features between two input feature sets

-Estimating geometric transformation between images

-Aligning and stitching images 

Object Detection

Objective: Train a detector for flexible object detection. Detect moving objects by using a foreground detector.

- Marking objects of interest in training images

- Training and using the cascaded object detector

- Using foreground detection to detect objects 

Motion Estimation

Objective: Estimate direction and strength of motion in a video sequence.

- Understanding motion perception in images

- Estimating motion using block matcher

- Estimating motion using optical flow methods

 
Day 2 of 2
Tracking

Objective: Track single and multiple objects and estimate their trajectory. Handle occlusion by predicting object position.
- Tracking objects using histogram of pixel values
 

- Tracking points using a point tracker

- Predicting object position using the Kalman filter

- Tracking multiple objects

Camera Calibration

Objective: Remove lens distortion from images. Measure size of planar objects.
- Estimating intrinsic, extrinsic, and lens distortion parameters of a camera

- Visualizing the calibration error

- Removing lens distortion

- Measuring planar objects in real-world units 
Point Clouds

Objective: Work with data points stored as point clouds. Import, visualize, and process point clouds.
 
- Importing and visualizing point clouds

- Removing outliers from point clouds
- Registering multiple point clouds

- Fitting a geometric shape into a point cloud 
3D Reconstruction

Objective: Create a 3D reconstruction of a scene using stereo cameras or a series of images taken by a moving camera.
 
- Reconstructing a scene using two or multiple images

- Reconstructing a scene using calibrated stereo cameras

- Extracting depth information from stereo images

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