2014
CVPR
CVPR 2014
T-Linkage: A Continuous Relaxation of J-Linkage for Multi-Model Fitting
Abstract
This paper presents an improvement of the J-linkage algorithm for fitting multiple instances of a model to noisy data corrupted by outliers. The binary preference analysis implemented by J-linkage is replaced by a continuous (soft, or fuzzy) generalization that proves to perform better than J-linkage on simulated data, and compares favorably with state of the art methods on public domain real datasets.
🌉
Interdisciplinary Bridge
— Computer Vision and Machine Learning and Mathematics & Optimization
📈
Trend Setter
— Robustness
🐣
Hot Topic Early Bird
— outlier detection
🐝
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, Security & Privacy