Computer Vision with OpenCV Training in Thimphu, Bhutan

The Computer Vision with OpenCV training provides participants with a comprehensive understanding of computer vision concepts and their implementation using the OpenCV library. The course covers image processing, feature detection, object recognition, and video analysis techniques. Through hands-on exercises, participants will gain practical experience in building computer vision applications for tasks such as face detection, object tracking, and real-time image processing, enabling them to create innovative solutions in domains like robotics, healthcare, and automation.

Introduction to Computer Vision and Basic Image Processing

1. Introduction to Computer Vision and OpenCV
• Overview of computer vision and its applications
• Introduction to OpenCV and installation
• Understanding OpenCV’s ecosystem and key libraries
• Practical exercise: Setting up OpenCV and running the first program
2. Basic Image Processing Techniques
• Reading, displaying, and saving images
• Color spaces and image transformations
• Image filtering, resizing, and cropping
• Practical exercise: Applying transformations on sample images
3. Thresholding and Edge Detection
• Thresholding techniques: Binary, adaptive, and Otsu’s method
• Edge detection using Sobel and Canny methods
• Practical exercise

Feature Detection, Image Matching, and Video Processing

4. Feature Detection and Description
• Keypoint detection with Harris, SIFT, and ORB
• Feature matching using Brute Force and FLANN-based matchers
• Practical exercise
5. Geometric Transformations and Warping
• Image transformations: Rotation, scaling, and perspective warping
• Affine and homography transformations
• Practical exercise
6. Video Analysis with OpenCV
• Reading and processing video streams
• Background subtraction and motion detection
• Object tracking with centroid tracking and bounding boxes
• Practical exercise

Advanced Computer Vision Applications

7. Object Detection and Recognition
• Introduction to Haar cascades and deep learning-based detection
• YOLO (You Only Look Once) and SSD (Single Shot Detector) concepts
• Practical exercise
8. Image Segmentation
• Segmentation techniques: Watershed and GrabCut
• Contour detection and analysis
• Practical exercise
9. Integrating OpenCV with AI/ML Models
• Using pre-trained models for classification and recognition tasks
• Deploying OpenCV applications with TensorFlow or PyTorch models
• Practical exercise