Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks

Unsupervised Learning in Space and Time: A Modern Approach for Computer Vision using Graph-based Techniques and Deep Neural Networks By Leordeanu, Marius Published by Springer Publication Date: 2020-04-18 Subject: Computers Artificial Intelligence General, Computers Software Development & Engineering Computer Graphics, Computers Artificial Intelligence - General, Computers Software Development & Engineering - Computer Graphics, Mathematics Applied, Mathematics Applied, Computer Vision, Image Processing, Machine Learning, Mathematical Modelling, Image Processing, Computer Vision, Machine Learning, Mathematical Modelling, Computers Software Development & Engineering Computer Graphics, Computers, Software Development & Engineering, Computer Graphics, Computers Artificial Intelligence General, Artificial Intelligence, General, Mathematics Applied, Mathematics, Applied, Anf: Computers And It Subject Keywords: Computer Vision; Deep Learning; Unsupervised Learning; Applications of Convolutional Neural Networks; Graph Matching; Probabilistic Graphical Models; Efficient Computational and Statistical Methods; Fast Optimization Algorithms; Semantic Segmentation in Video; Object Discovery in Video; Video Understanding and Analysis Genre: Image Processing, Computer Vision, Machine Learning, Mathematical Modelling, Computers, Software Development & Engineering, Computer Graphics, Computers, Artificial Intelligence, General, Mathematics, Applied, Image Processing, Computer Vision, Machine Learning, Mathematical Modelling Target Audience: Professional and scholarly