Module 1: Introduction to Computer Vision and OpenCV

• What is computer vision and real-world applications
• OpenCV library overview, installation, and setup
• Reading, writing, and displaying images and videos
• Image data types, colour spaces, and pixel manipulation

Module 2: Image Processing Fundamentals

• Image transformations: resize, rotate, flip, and crop
• Colour space conversions: BGR, RGB, HSV, and grayscale
• Histogram analysis and equalisation
• Noise reduction with blurring and filtering techniques

Module 3: Edge Detection and Feature Extraction

• Gradient-based edge detection: Sobel, Laplacian, and Canny
• Contour detection and shape analysis
• Feature detection: Harris corners and FAST keypoints
• Feature descriptors: SIFT, ORB, and BRISK

Module 4: Object Detection with Classical Methods

• Template matching for object localisation
• Haar cascade classifiers for face detection
• HOG features and SVM-based detection
• Background subtraction for motion detection

Module 5: Deep Learning for Computer Vision

• Introduction to CNNs for image classification
• Transfer learning with VGG, ResNet, and EfficientNet
• Object detection with YOLO: YOLOv8 setup and inference
• Semantic segmentation overview with DeepLab and U-Net

Module 6: Video Processing and Real-Time Applications

• Capturing and processing video streams with OpenCV
• Object tracking: CSRT, KCF, and DeepSORT trackers
• Optical flow for motion estimation
• Real-time face recognition with deep learning

Module 7: Advanced Applications

• OCR with Tesseract and OpenCV preprocessing
• Barcode and QR code detection and decoding
• 3D vision: stereo imaging and depth estimation basics
• Deploying computer vision models to edge devices

Module 8: Hands-on Projects and Assessment

• Building a real-time face detection and recognition system
• Object detection and tracking project with YOLOv8
• End-to-end computer vision application deployment
• Final assessment and course certification

Experience in the Industry Gain knowledge from computer vision engineers who have built image recognition, object detection, and video analytics systems for manufacturing, healthcare, and security industries.

Backgrounds at the Top Our Computer Vision trainers have delivered OpenCV and deep learning vision projects for automotive, surveillance, and industrial automation clients globally.

Clear & Effective Teaching Image processing, feature extraction, object detection, and deep learning vision models are taught progressively from OpenCV basics to YOLO and CNN deployments.

Hands-On Learning Focus Students implement real computer vision projects including face detection, object tracking, and image classification using OpenCV, TensorFlow, and PyTorch.

Up-to-Date Knowledge Trainers keep content current with the latest OpenCV releases, YOLO versions, vision transformer models, and edge deployment techniques.