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Computer Vision with MATLAB®
Image Processing and Computer Vision
Computer Vision with MATLAB
Learn to perform computer vision tasks in MATLAB and Computer Vision Toolbox
Complimentary Services: Post training email support & 1-hr consultation session within 1 month after the course completion!
Course
Highlights
This one-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.
Topics include:
- Importing, displaying, and annotating videos
- Detecting objects in videos
- Estimating motion of objects
- Tracking a single object or multiple objects
- Removing lens distortion and measuring planar objects
Who Should
Attend
Engineers, researchers, scientist who are involved in development of computer vision applications.
Course
Prerequisites
MATLAB Fundamentals or equivalent experience using MATLAB. Image Processing with MATLAB and basic knowledge of image processing and computer vision concepts.
Course
Benefits
Upon completion of the course, the participants will be able to use Computer Vision System Toolbox to:
- detect specific objects
- estimate motion of objects
- track a single object or multiple objects
- remove distortion and measure planar object size
Partners
Upcoming Program
- Please keep me posted on the next schedule
- Please contact me to arrange customized/ in-house training
Course Outline
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
Detecting Objects
Objective: Utilize machine learning and deep learning algorithms for complex object detection.
- Marking objects of interest in training images
- Training and using a cascade object detector
- Using a deep learning object detector
Estimating Motion
Objective: Estimate direction and strength of motion in a video sequence.
- Understanding motion perception in images
- Estimating motion using optical flow methods
Tracking Objects
Objective: Track single and multiple objects and estimate their trajectory. Handle occlusion by predicting object position.
- Tracking single objects using a Kalman Filter
- Tracking multiple objects using a GNN tracker
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
