Computer Vision: From Fundamentals to Real-World AI Applications
web-development.
Course Modules:
Module 1: Introduction to Computer Vision
What is computer vision and how does it work?
Common applications in industry
Tools and setup: OpenCV, Python, and libraries
Module 2: Image Processing Essentials
Color models and pixel operations
Blurring, filtering, and edge detection
Thresholding and segmentation techniques
Module 3: Feature Extraction & Shape Detection
Corners, contours, and histogram analysis
Detecting lines, shapes, and motion
Keypoint matching with ORB and SIFT (OpenCV)
Module 4: Face and Emotion Recognition
Using Haar Cascades and DNNs for face detection
Real-time face tracking and landmark detection
Emotion recognition using pre-trained models
Module 5: Video Analysis and Object Tracking
Reading and writing video files
Real-time frame analysis
Multi-object tracking and background subtraction
Module 6: Deep Learning for Vision Tasks
Convolutional Neural Networks (CNNs)
Training image classifiers with TensorFlow and Keras
Using transfer learning (VGG, ResNet)
Module 7: Object Detection and Localization
YOLO, SSD, and MobileNet-based models
Drawing bounding boxes and labels
Object tracking in real-time systems
Module 8: OCR and Scene Understanding
Optical Character Recognition with Tesseract
Reading license plates, text, and signs
Image captioning and scene classification
Module 9: Capstone Project
Choose from:
Real-time surveillance vision system
Medical image classifier
Smart retail shelf-monitoring tool
Submit working demo, codebase, and final documentation
Tools & Technologies Used:
OpenCV (Python)
TensorFlow / Keras
YOLO / SSD models
Google Colab or Jupyter Notebooks
Target Audience:
Developers and engineers entering AI
Students of data science and ML
Innovators building vision-based applications
Robotics and automation enthusiasts
Global Learning Benefits:
Learn to build AI systems that can interpret images and videos
Apply both classic and modern techniques in real-world projects
Get hands-on experience with OpenCV and deep learning vision tools
Prepare for roles in AI engineering, surveillance tech, and smart automation
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