Convolutional Neural Networks for Image Processing
Chapter Outline
1. Introduction to CNNs
- Historical context and development
- Comparison with traditional neural networks
- Key advantages for image processing tasks
- Overview of CNN applications in computer vision
2. Fundamental CNN Components
- Convolutional layers
- Filters and kernels
- Feature maps
- Stride and padding options
- Pooling layers
- Max pooling
- Average pooling
- Purpose in reducing spatial dimensions
- Activation functions
- ReLU and variants
- Importance of non-linearity
- Fully connected layers
3. CNN Architectures
- LeNet
- AlexNet
- VGG
- Inception/GoogLeNet
- ResNet
- DenseNet
- Vision Transformers (ViT)
4. Training CNNs
- Loss functions for image tasks
- Backpropagation through convolutional layers
- Regularization techniques
- Dropout
- Batch normalization
- Data augmentation
- Optimization strategies
- Learning rate scheduling
- Weight initialization methods
5. Transfer Learning with CNNs
- Pre-trained models on ImageNet
- Feature extraction approaches
- Fine-tuning strategies
- Domain adaptation challenges
6. Advanced CNN Applications
- Object detection
- R-CNN family
- YOLO
- SSD
- Semantic segmentation
- FCN
- U-Net
- Instance segmentation
- Mask R-CNN
- Image generation and style transfer
7. Explainable CNNs
- Visualization techniques
- Feature maps
- Activation maximization
- Grad-CAM
- Interpretability approaches
- Ethical considerations
- Model compression techniques
- Pruning
- Quantization
- Hardware considerations
- GPU acceleration
- Edge deployment
- Model architecture search
9. Case Studies
- Medical imaging analysis
- Autonomous driving perception
- Face recognition systems
- Industrial quality control
10. Future Directions
- Hybrid architectures (CNN + Transformer)
- Self-supervised learning for CNNs
- Multimodal learning
- Neuromorphic approaches
11. Practical Implementations
- CNN implementations in popular frameworks
- Deployment considerations
- Best practices for CNN development
12. Glossary and Resources
- Key terminology
- Recommended reading
- Online courses and tutorials
- Datasets for practice