2015
CVPR
CVPR 2015
Hierarchically-Constrained Optical Flow
Abstract
This paper presents a novel approach to solving optical flow problems using a discrete, tree-structured MRF derived from a hierarchical segmentation of the image. Our method can be used to find globally optimal matching solutions even for problems involving very large motions. Experiments demonstrate that our approach is competitive on the MPI-Sintel dataset and that it can significantly outperform existing methods on problems involving large motions.
🌉
Interdisciplinary Bridge
— Computer Vision and Machine Learning
🧭
Keyword Pioneer
— large motion
🐣
Hot Topic Early Bird
— graph matching
🐝
Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics