Computer Vision

Overview

What you'll learn

Key concepts and practical areas covered in this workshop.

• 01Learn Image Representation & Processing with OpenCV
• 02Apply Feature Extraction, Segmentation & Object Tracking
• 03Build & Train CNN Models for Image Classification
• 04Explore Advanced CV Techniques: Transfer Learning, Object Detection, GANs
• 05Deploy Computer Vision Models in Real-Time Applications
• 06Gain practical CV Skill.

Curriculum

Workshop curriculum

Review the topics covered in each module before sending an enquiry.

1. 01

   Foundations of Computer Vision

   2 weeks

   • Image Representation & Processing - Pixels, color channels, grayscale images, Loading, displaying & saving images with OpenCV
   • Image Transformations
   • Basic Operations
   • Hands-on
   • Exploratory Data Analysis (EDA)
   • Visualization using Matplotlib & Seaborn
2. 02

   Feature Extraction & Classical CV Techniques

   1 week

   • Feature Detection - SIFT, SURF, ORB basics
   • Segmentation & Tracking
   • Color & Histogram Analysis - Color spaces (RGB, HSV, LAB), Histogram equalization
   • Hands-on - Build small projects like face/shape detection
3. 03

   Deep Learning for Computer Vision

   3 weeks

   • Introduction to CNNs
   • Transfer Learning - Using pretrained models (ResNet, VGG)
   • Object Detection - YOLO, Faster R-CNN basics
   • Hands-on
   • Build a real-time image classification project
4. 04

   Capstone Project

   2 weeks

   • EDA
   • Predictive modeling or forecasting
   • Deployment on AWS (Lambda or SageMaker)
   • Dashboard/report generation

Before you begin

What you'll need

• Laptop with internet connection
• Basic logical thinking
• No prior AI experience needed