Computer Vision with Applications in Home Security
Introduction to Deep Learning:
– From Machine Learning to Deep Learning
– Convolutional Neural Networks on Spatial and Temporal Domains
– Case Study on GitHub Samples
Caffe, PyTorch, and Tensorflow for Deep Learning:
– Introduction to Tools
– Implementation and Performance Study
Application of Deep Learning:
– Face Recognition
– Object Localization
– Review on CVPR Spotlights
Digital Surveillance Systems and Applications
Classification with Image Features
– Classification and Regression
– Adaboost Classification
– Principal Component Analysis and Discriminants
– Bayesian Estimation
– Deep Learning
Image Analysis Primitives and Transformations
– 2D and 3D Transformations
– Basics of Camera, Lens and Image Processing
Laboratory Exercises on Video Surveillance
Image Preprocess:
– Fundamentals on OpenCV
– Color, Image and Filters
– Frequency Methods
Introduction to Deep Learning Tools
– Pytorch introduction and implement
– Caffe introduction and implement
– Tensorflow introduction and implement
Programming Language
Fundamentals on C and Python programming.
Many sample programs will be introduced with highlights on the usage/familiarization of C
Students are expected to be able to handle several assigned problems.