📚 Explore the Comprehensive Computer Vision Course Roadmap Below! 🚀
| Section | Subsection | Topics |
|---|---|---|
| 1. Introduction to Computer Vision | 1.1. Basic Image Processing | Image Representation, Image Manipulation, Image Filtering, Histograms, Denoising Images |
| 1.2. OpenCV Basics | Installing OpenCV, Basic Operations, Image Thresholding, Contours, Basic Video Processing | |
| 2. Intermediate Computer Vision | 2.1. Feature Detection and Matching | Keypoints, Detectors, Descriptors, Feature Matching |
| 2.2. Image Segmentation | Thresholding, Clustering, Advanced Techniques, Histogram-based segmentation, Random walker Segmentation | |
| 2.3. Fourier Transform and Frequency Domain | Fourier Transform, Frequency Domain, DFT, FFT, Filtering, Inverse Transform, Aliasing | |
| 2.4. Object Detection | Traditional Methods (e.g., HOG + SVM), Modern Techniques (e.g., CNN-based) | |
| 3. Deep Learning for Computer Vision | 3.1. Fundamentals of Deep Learning | Neural Networks, Deep Learning Frameworks, Convolutional Neural Networks (CNNs) |
| 3.2. Advanced CNN Architectures | Popular Architectures (e.g., ResNet, VGG), Transfer Learning | |
| 3.3. Image Classification and Recognition | Data Preparation, Training and Evaluation | |
| 3.4. Object Detection with Deep Learning | Region Proposal Networks (RPN), Single-Shot Detectors (SSD, YOLO) | |
| 3.5. Image Segmentation with Deep Learning | Fully Convolutional Networks (FCNs), UNet, Mask R-CNN | |
| 4. Advanced Computer Vision Techniques | 4.1. Generative Models | Autoencoders, Generative Adversarial Networks (GANs) |
| 4.2. Advanced Topics in Object Detection and Segmentation | Instance Segmentation, Semantic Segmentation, Panoptic Segmentation | |
| 4.3. Geometric Deep Learning | Graph Neural Networks (GNNs), Point Cloud Processing, Mesh-based Learning, Graph Convolutional Networks (GCNs), Geometric Transformers | |
| 4.4. 3D Vision and Geometry | 3D Reconstruction, Stereo Vision | |
| 4.5. Video Analysis | Optical Flow, Action Recognition, Object Tracking, Background Subtraction | |
| 4.6. Reinforcement Learning in CV | Basic Concepts, Applications in CV | |
| 5. Specialized Areas | 5.1. Medical Imaging | DICOM Data, Medical Image Segmentation |
| 5.2. Autonomous Vehicles | Lidar and Radar, Sensor Fusion | |
| 5.3. Edge AI and Real-Time CV | Optimized Models, TensorFlow Lite, OpenVINO, Mobile Device Inference |