448 citations · 686 across the 24 of their papers we have counts for
5 papers · 1 filter
Maximum Consensus by Weighted Influences of Monotone Boolean Functions
Erchuan Zhang, David Suter, Ruwan Tennakoon +4
Robust model fitting is a fundamental problem in computer vision: used to pre-process raw data in the presence of outliers. Maximisation of Consensus (MaxCon) is one of the most po…
Event Data Association via Robust Model Fitting for Event-based Object Tracking
Haosheng Chen, Yue Wu, Yidong Peng
Event-based approaches, which are based on bio-inspired asynchronous event cameras, have achieved promising performance on various computer vision tasks. However, the study of the…
Achieving Domain Robustness in Stereo Matching Networks by Removing Shortcut Learning
WeiQin Chuah, Ruwan Tennakoon, Alireza Bab-Hadiashar +1
Learning-based stereo matching and depth estimation networks currently excel on public benchmarks with impressive results. However, state-of-the-art networks often fail to generali…
Consensus Maximisation Using Influences of Monotone Boolean Functions
Ruwan Tennakoon, David Suter, Erchuan Zhang +2
Consensus maximisation (MaxCon), which is widely used for robust fitting in computer vision, aims to find the largest subset of data that fits the model within some tolerance level…
Unsupervised Learning for Robust Fitting:A Reinforcement Learning Approach
Giang Truong, Huu Le, David Suter +2
Robust model fitting is a core algorithm in a large number of computer vision applications. Solving this problem efficiently for datasets highly contaminated with outliers is, howe…