Computer Vision Using AI full syllabus

Ideal Duration: 3 to 5 Months

Prerequisites:

• Basic Python knowledge
• Basic understanding of Deep Learning (Neural Networks)
• Math basics: Matrices, Image representation

Module 1: Introduction to Computer Vision (CV)

Understand the world of computer vision—how machines process visual information.

Topics:

• What is Computer Vision?
• Real-World Applications: Face Unlock, Barcode Scanning, Self-Driving Cars
• Image vs Video Processing
• Understanding Pixels, RGB, Grayscale
• Basic Image Operations using OpenCV (reading, resizing, flipping)

Mini Project: Build a basic photo filter (like Instagram)

Module 2: Image Processing Fundamentals

Learn the essential image processing techniques used before AI kicks in.

Topics:

• Color Spaces: RGB, HSV, Grayscale
• Image Thresholding
• Edge Detection (Canny, Sobel)
• Blurring & Smoothing
• Morphological Operations (Erosion, Dilation)

Mini Project: Create a document scanner from a webcam

Module 3: Working with OpenCV (Python)

OpenCV is your toolkit for everything CV-related.

Topics:

• OpenCV Basics
• Drawing on Images (shapes, text)
• Image Contours
• ROI (Region of Interest) and Masking
• Face Detection using Haar Cascades

Project: Real-time Face Detection App

Module 4: Deep Learning for Computer Vision

Here AI starts to make your system smarter with powerful learning.

Topics:

• Introduction to CNNs (Convolutional Neural Networks)
• Layers: Conv, Pooling, Flatten, Fully Connected
• How CNNs “see” features (edges, shapes, objects)
• Using TensorFlow or PyTorch for image classification

Project: Train a CNN to classify animals (cats, dogs, horses, etc.)

Module 5: Transfer Learning for Vision Tasks

Why train from scratch when pre-trained models can save your time?

Topics:

• What is Transfer Learning?
• Pre-trained Models (VGG16, ResNet, MobileNet, EfficientNet)
• Fine-Tuning vs Feature Extraction
• Custom Classification on New Dataset

Project: Build a flower species recognizer using MobileNet

Module 6: Object Detection

Identify where the object is in an image, not just what it is.

Topics:

• Object Detection vs Classification
• Bounding Boxes & Intersection over Union (IoU)
• Traditional Methods (HOG + SVM)
• Modern Methods:
  • SSD (Single Shot Detector)
  • YOLO (You Only Look Once)
  • Faster R-CNN

Project: Detect people in a CCTV video using YOLOv5

Module 7: Image Segmentation

Going deeper than object detection — understand each pixel’s identity.

Topics:

• What is Segmentation?
• Semantic vs Instance Segmentation
• U-Net Architecture
• Use Cases: Medical Imaging, Satellite Images

Project: Segment roads and buildings from satellite images

Module 8: Facial Recognition & Biometrics

Learn how AI can recognize faces just like humans (or better).

Topics:

• Face Detection vs Face Recognition
• Landmark Detection (eyes, nose, mouth)
• FaceNet & DeepFace
• Building a Face Recognition Attendance System

Project: Build your own Face Recognition Door Lock System (using webcam)

Module 9: Working with Videos & Real-time Applications

Bring your models into real-world live scenarios.

Topics:

• Reading and Writing Videos in OpenCV
• Frame-by-Frame Processing
• Real-time Object Detection with Webcam
• FPS Optimization Tips

Project: Real-time object tracker using YOLO + OpenCV

Module 10: Computer Vision with Generative AI

Make machines create visual art, faces, or even cartoons!

Topics:

• GANs (Generative Adversarial Networks) Basics
• Image-to-Image Translation (e.g., Sketch to Color)
• Style Transfer
• DeepFakes & Ethics of Generative CV

Project: Create AI-based art using neural style transfer

Module 11: Model Deployment

Take your CV model to the web or a mobile device.

Topics:

• Exporting Models (ONNX, SavedModel)
• Serving Models via Flask or FastAPI
• Deploying on Streamlit or Gradio
• Intro to Edge Deployment (Raspberry Pi, Android)

Project: Deploy an AI-powered product scanner web app

Tools You’ll Learn:

• Programming: Python
• Libraries: OpenCV, NumPy, Matplotlib, Seaborn
• Deep Learning: TensorFlow, Keras, PyTorch
• Utilities: Google Colab, Jupyter, Hugging Face, Gradio

By the end, you’ll be able to:

• Build real-world AI tools that use visual inputs
• Solve real problems (security, healthcare, retail, etc.)
• Deploy smart apps that see and understand images
• Launch your own AI-powered computer vision product