Introduction to Computer Vision with Deep Learning

Image acquisition, sampling (pixels) and representation (histogram, color spaces, Fourier Transform)

Image transformations: local & global operators, convolution, filtering (smoothing, sharpening)

Low-level Vision: edges, corners, lines and circles detection, scalar product

Feature extraction and classification:

• Feature Extraction
• Deep Learning and Transfer Learning
• Image Pattern Classification

Computer Vision applications:

• Facial analysis: detection and recognition
• In-vehicle vision system: driver drowsiness
• Robot vision systems: human emotion and intention detection