Learn comprehensively from basic image processing with OpenCV to object detection using deep learning, semantic segmentation, and image generation
Series Overview
This series is a practical educational content consisting of five chapters that allows you to learn the theory and implementation of Computer Vision progressively from the basics.
Computer Vision is a technology that enables computers to extract and understand meaningful information from images and videos. Computer vision techniques are diverse, ranging from classical image processing techniques such as image filtering and edge detection, to image classification using CNNs, object detection with YOLO and Faster R-CNN, semantic segmentation using U-Net and Mask R-CNN, and even image generation with GANs and Diffusion Models. They are utilized across all industries, including autonomous driving, medical image diagnosis, manufacturing quality inspection, facial recognition systems, and AR/VR applications. You will understand and be able to implement image recognition technologies being commercialized by companies like Google, Tesla, Amazon, and Meta. We provide practical knowledge using major libraries such as OpenCV, PyTorch, and TensorFlow.
Features:
- ✅ From Theory to Practice: Systematic learning from image processing fundamentals to the latest deep learning techniques
- ✅ Implementation-Focused: Over 50 executable Python/OpenCV/PyTorch code examples
- ✅ Industry-Oriented: Practical projects designed for real-world applications
- ✅ Latest Technology Standards: Implementation using YOLO, U-Net, Mask R-CNN, and Transformers
- ✅ Practical Applications: Hands-on practice in object detection, segmentation, pose estimation, and image generation
Total Learning Time: 6-7 hours (including code execution and exercises)
How to Study
Recommended Learning Sequence
For Beginners (completely new to computer vision):
- Chapter 1 → Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5 (all chapters recommended)
- Duration: 6-7 hours
For Intermediate Learners (with machine learning experience):
- Chapter 2 → Chapter 3 → Chapter 4 → Chapter 5
- Duration: 5-6 hours
For Specific Topic Reinforcement:
- Image Processing Basics & OpenCV: Chapter 1 (intensive study)
- CNN & Image Classification: Chapter 2 (intensive study)
- Object Detection: Chapter 3 (intensive study)
- Segmentation: Chapter 4 (intensive study)
- Advanced Applications: Chapter 5 (intensive study)
- Duration: 70-90 minutes/chapter
Chapter Details
Chapter 1: Image Processing Basics
Difficulty: Beginner
Reading Time: 70-80 minutes
Code Examples: 12
Learning Content
- Image Fundamentals - Pixels, color spaces (RGB, HSV, grayscale), image formats
- OpenCV Introduction - Image reading, saving, displaying, basic operations
- Filtering - Blurring, sharpening, noise reduction
- Edge Detection - Sobel, Canny, Laplacian
- Feature Extraction - SIFT, SURF, ORB, Harris Corner
Learning Objectives
- ✅ Understand basic image structure and color spaces
- ✅ Manipulate images with OpenCV
- ✅ Apply various filters
- ✅ Use edge detection algorithms
- ✅ Extract features from images
Chapter 2: Image Classification
Difficulty: Beginner to Intermediate
Reading Time: 80-90 minutes
Code Examples: 11
Learning Content
- CNN (Convolutional Neural Networks) - Convolutional layers, pooling layers, fully connected layers
- Representative CNN Architectures - LeNet, AlexNet, VGG, ResNet, EfficientNet
- Transfer Learning - Leveraging pre-trained models, Fine-tuning
- Data Augmentation - Rotation, flipping, cropping, color adjustment
- Practical Projects - Image classification on CIFAR-10 and ImageNet
Learning Objectives
- ✅ Understand CNN mechanisms
- ✅ Explain representative CNN architectures
- ✅ Implement Transfer Learning
- ✅ Apply Data Augmentation
- ✅ Build and evaluate image classification models
Chapter 3: Object Detection
Difficulty: Intermediate
Reading Time: 80-90 minutes
Code Examples: 10
Learning Content
- Object Detection Fundamentals - Bounding Box, IoU, NMS, mAP evaluation metrics
- Two-Stage Detectors - R-CNN, Fast R-CNN, Faster R-CNN
- One-Stage Detectors - YOLO (v3, v5, v8), SSD, RetinaNet
- Anchor-Free Detectors - FCOS, CenterNet, EfficientDet
- Practical Projects - Object detection on COCO and Pascal VOC
Learning Objectives
- ✅ Understand basic object detection concepts
- ✅ Explain differences between Two-Stage and One-Stage detectors
- ✅ Implement object detection with YOLO
- ✅ Evaluate detection results (mAP calculation)
- ✅ Train detectors on custom datasets
Chapter 4: Segmentation
Difficulty: Intermediate
Reading Time: 70-80 minutes
Code Examples: 9
Learning Content
- Types of Segmentation - Semantic, Instance, Panoptic Segmentation
- U-Net - Encoder-decoder structure, Skip Connections
- Mask R-CNN - Instance Segmentation implementation
- DeepLab - Atrous Convolution, ASPP, semantic segmentation
- Practical Projects - Medical image segmentation, autonomous driving scene understanding
Learning Objectives
- ✅ Understand types of segmentation
- ✅ Explain U-Net mechanisms
- ✅ Implement Mask R-CNN
- ✅ Evaluate segmentation results (IoU, Dice coefficient)
- ✅ Train segmentation models on custom datasets
Chapter 5: Advanced Applications
Difficulty: Intermediate to Advanced
Reading Time: 80-90 minutes
Code Examples: 10
Learning Content
- Pose Estimation - OpenPose, MediaPipe, keypoint detection
- Face Recognition - Face detection, facial landmarks, face authentication (FaceNet, ArcFace)
- Image Generation - GAN, VAE, Diffusion Models, StyleGAN
- OCR (Optical Character Recognition) - CRNN, Tesseract, EasyOCR, TrOCR
- Vision Transformer - ViT, DINO, CLIP, multimodal learning
Learning Objectives
- ✅ Implement pose estimation
- ✅ Build face recognition systems
- ✅ Use image generation models
- ✅ Implement OCR systems
- ✅ Understand Vision Transformer mechanisms
Overall Learning Outcomes
Upon completing this series, you will acquire the following skills and knowledge:
Knowledge Level (Understanding)
- ✅ Explain image processing fundamentals and OpenCV usage
- ✅ Understand mechanisms of CNN, object detection, and segmentation
- ✅ Explain roles and use cases of YOLO, U-Net, and Mask R-CNN
- ✅ Understand technologies for pose estimation, face recognition, and image generation
- ✅ Explain current trends in computer vision technologies
Practical Skills (Doing)
- ✅ Implement image processing with OpenCV
- ✅ Build image classification models with CNN
- ✅ Create object detection systems with YOLO
- ✅ Implement segmentation with U-Net and Mask R-CNN
- ✅ Develop pose estimation, face recognition, and OCR systems
Application Ability (Applying)
- ✅ Select appropriate computer vision techniques for projects
- ✅ Train custom models on custom datasets
- ✅ Properly evaluate model performance
- ✅ Design and implement computer vision systems
- ✅ Apply computer vision to real-world problems
Prerequisites
To effectively learn this series, it is desirable to have the following knowledge:
Required (Must Have)
- ✅ Python Basics: Variables, functions, classes, modules
- ✅ NumPy Basics: Array manipulation, vector and matrix operations
- ✅ Machine Learning Fundamentals: Concepts of training, validation, and testing
- ✅ Linear Algebra Basics: Vectors, matrices, matrix multiplication
- ✅ PyTorch/TensorFlow Basics: Tensor operations, model building (recommended)
Recommended (Nice to Have)
- 💡 Deep Learning Fundamentals: Neural networks, gradient descent
- 💡 Image Processing Experience: Experience using PIL and OpenCV
- 💡 Calculus Basics: Partial derivatives, gradients (for deep learning)
- 💡 Statistics Basics: Probability distributions, expected values (for evaluation metrics)
- 💡 GPU Environment: CUDA, experience with GPU training
Recommended Prior Learning:
- 📚 - ML fundamentals
- 📚 - Neural networks, PyTorch
- 📚 - NumPy, pandas, matplotlib
- 📚 - Vector and matrix operations
Technologies and Tools Used
Main Libraries
- OpenCV 4.8+ - Image processing, computer vision
- PyTorch 2.0+ - Deep learning framework
- torchvision 0.15+ - Image datasets, models, transformations
- NumPy 1.24+ - Numerical computation
- Matplotlib 3.7+ - Visualization
- Pillow 10.0+ - Image processing
- albumentations 1.3+ - Data Augmentation
Specialized Libraries
- Ultralytics YOLOv8 - Object detection
- MMDetection - Object detection framework
- Detectron2 - Facebook AI's detection and segmentation library
- MediaPipe - Pose estimation, face recognition
- EasyOCR - Optical character recognition
- timm - PyTorch Image Models
Development Environment
- Python 3.8+ - Programming language
- Jupyter Notebook - Interactive development environment
- Google Colab - Cloud GPU environment (recommended)
- CUDA 11.8+ - GPU acceleration (recommended)
- cuDNN 8.6+ - Deep learning GPU optimization
Datasets
- ImageNet - Large-scale image classification dataset
- COCO - Object detection and segmentation dataset
- CIFAR-10/100 - Small-scale image classification dataset
- Pascal VOC - Object detection dataset
- Cityscapes - Autonomous driving segmentation dataset
Let's Get Started!
Are you ready? Start with Chapter 1 and master computer vision technologies!
Chapter 1: Image Processing Basics →
Next Steps
After completing this series, we recommend proceeding to the following topics:
Advanced Study
- 📚 3D Computer Vision: Stereo vision, 3D reconstruction, SLAM
- 📚 Video Analysis: Action recognition, object tracking, temporal analysis
- 📚 Multimodal Learning: CLIP, ALIGN, integration of images and text
- 📚 Edge Device Deployment: TensorRT, ONNX, mobile optimization
Related Series
- 🎯 - Transformer, Attention, latest architectures
- 🎯 - Sensor fusion, scene understanding
- 🎯 - CT, MRI, lesion detection
Practical Projects
- 🚀 Real-time Object Detection System - Detection application using webcam
- 🚀 Face Recognition System Development - Implementation from detection to authentication
- 🚀 Medical Image Segmentation - Lung and tumor segmentation
- 🚀 Autonomous Driving Simulator - Lane detection, vehicle detection, scene understanding
Version History
- 2025-10-21: v1.0 First edition released
Your computer vision journey begins here!