Computer Vision with OpenCV & Deep Learning

About Course

Master the art of teaching computers to see and understand visual information. This course walks you through the essentials of image processing, object detection, and deep learning applications in vision. You’ll use OpenCV, TensorFlow, and advanced CNN architectures for real-world projects like face recognition and image segmentation.

Course Content

Introduction to Computer Vision

Lesson 1: What is Computer Vision?
Lesson 2: Applications of Computer Vision
Lesson 3: Setting up OpenCV
Lesson 4: Reading and Displaying Images
Lesson 5: Image Color Spaces (RGB, HSV, Grayscale)
Lesson 6: Resizing and Cropping Images
Lesson 7: Drawing Shapes and Text on Images
Lesson 8: Capturing Video Streams
Lesson 9: Image Transformations
Lesson 10: Understanding Image Histograms

Image Processing with OpenCV

Lesson 1: Image Filtering and Smoothing
Lesson 2: Edge Detection with Canny
Lesson 3: Thresholding Techniques
Lesson 4: Morphological Transformations
Lesson 5: Contour Detection
Lesson 6: Object Counting
Lesson 7: Perspective and Affine Transformations
Lesson 8: Image Blending and Masking
Lesson 9: Image Gradients
Lesson 10: Practical Image Processing Projects

Deep Learning for Vision

Lesson 1: Introduction to CNNs
Lesson 2: Building a CNN from Scratch
Lesson 3: Image Classification with TensorFlow
Lesson 4: Transfer Learning with Pretrained Models
Lesson 5: Data Augmentation Techniques
Lesson 6: Fine-Tuning CNNs
Lesson 7: Object Detection Algorithms (YOLO, SSD)
Lesson 8: Image Segmentation with UNet
Lesson 9: Facial Recognition Systems
Lesson 10: Real-Time Object Detection Project

Advanced Vision Applications

Lesson 1: Optical Character Recognition (OCR)
Lesson 2: Gesture Recognition
Lesson 3: Scene Understanding
Lesson 4: Feature Matching with SIFT & ORB
Lesson 5: Image Stitching for Panorama
Lesson 6: Tracking Moving Objects
Lesson 7: Depth Estimation from Stereo Vision
Lesson 8: Image Captioning
Lesson 9: Vision Transformers (ViT)
Lesson 10: End-to-End Vision Project

Deployment and Real-World Integration

Lesson 1: Saving and Loading Models
Lesson 2: Exporting Models for Mobile
Lesson 3: Integrating CV Models with Flask
Lesson 4: Creating a Web App for Object Detection
Lesson 5: Edge AI and Real-Time Inference
Lesson 6: Optimizing Model Performance
Lesson 7: Deploying on Cloud Platforms
Lesson 8: Using TensorRT for Faster Inference
Lesson 9: Testing and Debugging CV Systems
Lesson 10: Future of Computer Vision

About Course

Course Content

Introduction to Computer Vision

Lesson 1: What is Computer Vision?

Lesson 2: Applications of Computer Vision

Lesson 3: Setting up OpenCV

Lesson 4: Reading and Displaying Images

Lesson 5: Image Color Spaces (RGB, HSV, Grayscale)

Lesson 6: Resizing and Cropping Images

Lesson 7: Drawing Shapes and Text on Images

Lesson 8: Capturing Video Streams

Lesson 9: Image Transformations

Lesson 10: Understanding Image Histograms

Image Processing with OpenCV

Lesson 1: Image Filtering and Smoothing

Lesson 2: Edge Detection with Canny

Lesson 3: Thresholding Techniques

Lesson 4: Morphological Transformations

Lesson 5: Contour Detection

Lesson 6: Object Counting

Lesson 7: Perspective and Affine Transformations

Lesson 8: Image Blending and Masking

Lesson 9: Image Gradients

Lesson 10: Practical Image Processing Projects

Deep Learning for Vision

Lesson 1: Introduction to CNNs

Lesson 2: Building a CNN from Scratch

Lesson 3: Image Classification with TensorFlow

Lesson 4: Transfer Learning with Pretrained Models

Lesson 5: Data Augmentation Techniques

Lesson 6: Fine-Tuning CNNs

Lesson 7: Object Detection Algorithms (YOLO, SSD)

Lesson 8: Image Segmentation with UNet

Lesson 9: Facial Recognition Systems

Lesson 10: Real-Time Object Detection Project

Advanced Vision Applications

Lesson 1: Optical Character Recognition (OCR)

Lesson 2: Gesture Recognition

Lesson 3: Scene Understanding

Lesson 4: Feature Matching with SIFT & ORB

Lesson 5: Image Stitching for Panorama

Lesson 6: Tracking Moving Objects

Lesson 7: Depth Estimation from Stereo Vision

Lesson 8: Image Captioning

Lesson 9: Vision Transformers (ViT)

Lesson 10: End-to-End Vision Project

Deployment and Real-World Integration

Lesson 1: Saving and Loading Models

Lesson 2: Exporting Models for Mobile

Lesson 3: Integrating CV Models with Flask

Lesson 4: Creating a Web App for Object Detection

Lesson 5: Edge AI and Real-Time Inference

Lesson 6: Optimizing Model Performance

Lesson 7: Deploying on Cloud Platforms

Lesson 8: Using TensorRT for Faster Inference

Lesson 9: Testing and Debugging CV Systems

Lesson 10: Future of Computer Vision