2019 CVPR CVPR 2019

Fitting Multiple Heterogeneous Models by Multi-Class Cascaded T-Linkage

Abstract

This paper addresses the problem of multiple models fitting in the general context where the sought structures can be described by a mixture of heterogeneous parametric models drawn from different classes. To this end, we conceive a multi-model selection framework that extend T-linkage to cope with different nested class of models. Our method, called MCT, compares favourably with the state-of-the-art on publicly available data-sets for various fitting problems: lines and conics, homographies and fundamental matrices, planes and cylinders.

🌉 Interdisciplinary Bridge — Artificial Intelligence and Computer Vision and Deep Learning and Machine Learning and Mathematics & Optimization
🧭 Keyword Pioneer — heterogeneous parametric model
🐣 Hot Topic Early Bird — model selection
🐝 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, Security & Privacy, Speech & Audio