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.
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Interdisciplinary Bridge
— Artificial Intelligence and Computer Vision and Deep Learning and Machine Learning and Mathematics & Optimization
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Keyword Pioneer
— heterogeneous parametric model
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Hot Topic Early Bird
— model selection
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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
Authors
Topics
Machine Learning > Core Methods > Clustering
Mathematics & Optimization > Mathematics > Geometry
Mathematics & Optimization > Optimization > Combinatorial Optimization
Artificial Intelligence > Core AI > Reasoning
Computer Vision > Processing > Image Processing
Computer Vision > Core AI > Computer Vision
Deep Learning > Learning Types > Multi-Task Learning