Introduction to Computer Vision: Teaching Machines to See and Understand
web-development.
Course Modules:
Module 1: What is Computer Vision?
Defining computer vision and its real-world applications
Evolution from traditional image processing to AI-based systems
Overview of the CV pipeline: input, processing, and understanding
Module 2: Image Representation and Processing
Pixels, channels, and color spaces (RGB, grayscale, HSV)
Image resizing, cropping, rotation, and filtering
Histograms, edge detection, and thresholding techniques
Module 3: Core Computer Vision Techniques
Image classification vs. object detection
Contour detection and shape analysis
Feature detection (SIFT, ORB, HOG)
Module 4: Introduction to OpenCV and Python
Setting up OpenCV in a Python environment
Reading, writing, and displaying images and videos
Drawing shapes, text, and basic processing tasks
Module 5: Applying Computer Vision to Real Problems
Use cases: face detection, barcode scanning, traffic monitoring
Practical demo: building a simple face or object detector
Project-based exercises using webcams or datasets
Module 6: Capstone Project – Build a Mini CV System
Choose a project (e.g., motion detector, license plate scanner, image filter app)
Implement using OpenCV and Python
Submit source code, screenshots, and a brief report
Tools & Technologies Used:
Python
OpenCV
NumPy, Matplotlib
Optional: pre-trained models (Haar Cascades, DNN modules)
Target Audience:
Beginners in AI or machine learning
Software developers and engineers
Students exploring computer vision careers
Anyone interested in building real-world vision-based applications
Global Learning Benefits:
Understand how machines “see” the world
Start applying vision tools in your projects and products
Build strong foundations for advanced CV topics like deep learning, CNNs, and YOLO
Learn with hands-on Python and OpenCV practice
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