448 citations · 686 across the 24 of their papers we have counts for
26 papers
Bicoloured-interval and interval-sandwich graphs: two new classes in the tolerance hierarchy
Abdul Basit, David Suter, Erchuan Zhang
We introduce two new graph classes, the bicoloured-interval graphs and the interval-sandwich graphs, arising from the study of the robust line fitting problem in computer vision. I…
Single Domain Generalization via Normalised Cross-correlation Based Convolutions
WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad +2
Deep learning techniques often perform poorly in the presence of domain shift, where the test data follows a different distribution than the training data. The most practically des…
ITSA: An Information-Theoretic Approach to Automatic Shortcut Avoidance and Domain Generalization in Stereo Matching Networks
WeiQin Chuah, Ruwan Tennakoon, Reza Hoseinnezhad +2
State-of-the-art stereo matching networks trained only on synthetic data often fail to generalize to more challenging real data domains. In this paper, we attempt to unfold an impo…
Fast Semantic-Assisted Outlier Removal for Large-scale Point Cloud Registration
Giang Truong, Huu Le, Alvaro Parra +3
With current trends in sensors (cheaper, more volume of data) and applications (increasing affordability for new tasks, new ideas in what 3D data could be useful for); there is cor…
A Hybrid Quantum-Classical Algorithm for Robust Fitting
Anh-Dzung Doan, Michele Sasdelli, David Suter +1
Fitting geometric models onto outlier contaminated data is provably intractable. Many computer vision systems rely on random sampling heuristics to solve robust fitting, which do n…
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…
Quantum Robust Fitting
Tat-Jun Chin, David Suter, Shin-Fang Chng +1
Many computer vision applications need to recover structure from imperfect measurements of the real world. The task is often solved by robustly fitting a geometric model onto noisy…
Monotone Boolean Functions, Feasibility/Infeasibility, LP-type problems and MaxCon
David Suter, Ruwan Tennakoon, Erchuan Zhang +2
This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem. The latter refers to a particular type of robust fitting cha…
End-to-end Learning of Object Motion Estimation from Retinal Events for Event-based Object Tracking
Haosheng Chen, David Suter, Qiangqiang Wu +1
Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event came…
Hypergraph Optimization for Multi-structural Geometric Model Fitting
Shuyuan Lin, Guobao Xiao, Yan Yan +2
Recently, some hypergraph-based methods have been proposed to deal with the problem of model fitting in computer vision, mainly due to the superior capability of hypergraph to repr…
Deterministic consensus maximization with biconvex programming
Zhipeng Cai, Tat-Jun Chin, Huu Le +1
Consensus maximization is one of the most widely used robust fitting paradigms in computer vision, and the development of algorithms for consensus maximization is an active researc…
Superpixel-guided Two-view Deterministic Geometric Model Fitting
Guobao Xiao, Hanzi Wang, Yan Yan +1
Geometric model fitting is a fundamental research topic in computer vision and it aims to fit and segment multiple-structure data. In this paper, we propose a novel superpixel-guid…
Searching for Representative Modes on Hypergraphs for Robust Geometric Model Fitting
Hanzi Wang, Guobao Xiao, Yan Yan +1
In this paper, we propose a simple and effective {geometric} model fitting method to fit and segment multi-structure data even in the presence of severe outliers. We cast the task…
Deterministic Approximate Methods for Maximum Consensus Robust Fitting
Huu Le, Tat-Jun Chin, Anders Eriksson +2
Maximum consensus estimation plays a critically important role in robust fitting problems in computer vision. Currently, the most prevalent algorithms for consensus maximization dr…
Superpixel-based Two-view Deterministic Fitting for Multiple-structure Data
Guobao Xiao, Hanzi Wang, Yan Yan +1
This paper proposes a two-view deterministic geometric model fitting method, termed Superpixel-based Deterministic Fitting (SDF), for multiple-structure data. SDF starts from super…
Hypergraph Modelling for Geometric Model Fitting
Guobao Xiao, Hanzi Wang, Taotao Lai +1
In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a…
Mode-Seeking on Hypergraphs for Robust Geometric Model Fitting
Hanzi Wang, Guobao Xiao, Yan Yan +1
In this paper, we propose a novel geometric model fitting method, called Mode-Seeking on Hypergraphs (MSH),to deal with multi-structure data even in the presence of severe outliers…
Multi-Subregion Based Correlation Filter Bank for Robust Face Recognition
Yan Yan, Hanzi Wang, David Suter
In this paper, we propose an effective feature extraction algorithm, called Multi-Subregion based Correlation Filter Bank (MS-CFB), for robust face recognition. MS-CFB combines the…
Fast Supervised Hashing with Decision Trees for High-Dimensional Data
Guosheng Lin, Chunhua Shen, Qinfeng Shi +2
Supervised hashing aims to map the original features to compact binary codes that are able to preserve label based similarity in the Hamming space. Non-linear hash functions have d…
Fast Training of Effective Multi-class Boosting Using Coordinate Descent Optimization
Guosheng Lin, Chunhua Shen, Anton van den Hengel +1
Wepresentanovelcolumngenerationbasedboostingmethod for multi-class classification. Our multi-class boosting is formulated in a single optimization problem as in Shen and Hao (2011)…