MATLAB & Simulink
IMAGE PROCESSING AND COMPUTER VISION
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 functionality.
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
Techsource Systems is
Mathworks Sole and Authorised Distributor and Training Partner
MATLAB, Image Processing Toolbox and Computer Vision System Toolbox
Attended Comprehensive MATLAB or equivalent experience using MATLAB.
Basic knowledge of image processing and computer vision concepts
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
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
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
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
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
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
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