Computer Vision and Image Processing Course

Module 1: Visual Computing Fundamentals and Computer Vision Foundations

• Develop a comprehensive understanding of visual computing concepts, including image formation and camera models
• Analyze the fundamentals of computer vision, including image processing, feature extraction, and object recognition
• Configure a development environment for computer vision tasks using Python and OpenCV

Module 2: Image Processing, Augmentation, and Feature Extraction

• Implement image filtering techniques, including convolutional filters and Fourier transforms
• Design and apply image augmentation strategies to enhance dataset diversity and robustness
• Evaluate the performance of feature extraction algorithms, including SIFT and ORB

Module 3: CNN Architectures, Transfer Learning, and Computer Vision Models

• Design and implement convolutional neural network (CNN) architectures for image classification tasks
• Apply transfer learning techniques to leverage pre-trained models for computer vision tasks
• Optimize CNN models using hyperparameter tuning and regularization techniques

Module 4: Object Detection, Segmentation, and Localization

• Implement object detection algorithms, including YOLO and SSD
• Develop and evaluate image segmentation models using U-Net and Mask R-CNN
• Configure and optimize object localization pipelines using OpenCV and Python

Module 5: Video Analysis, Temporal Models, and Real-Time Processing

• Analyze and implement video processing techniques, including object tracking and motion estimation
• Design and evaluate temporal models for video analysis, including LSTM and GRU
• Develop real-time video processing pipelines using Python and OpenCV

Module 6: Model Optimization, Quantization, and Edge Deployment

• Optimize computer vision models for deployment on edge devices using model pruning and quantization
• Implement model quantization techniques, including post-training quantization and quantization-aware training
• Deploy optimized models on edge devices using TensorFlow Lite and OpenVINO

Module 7: Industry Applications and Computer Vision Use Cases

• Evaluate the applications of computer vision in various industries, including healthcare, finance, and retail
• Develop and present a computer vision project for a real-world use case
• Analyze the ethical and social implications of computer vision technology