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20162026
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cs.CV2023

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…

cs.CV2022

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…

cs.CV2021

Anomaly Detection of Defect using Energy of Point Pattern Features within Random Finite Set Framework

Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar +1

In this paper, we propose an efficient approach for industrial defect detection that is modeled based on anomaly detection using point pattern data. Most recent works use \textit{g…

cs.CV2021

Robust Pooling through the Data Mode

Ayman Mukhaimar, Ruwan Tennakoon, Chow Yin Lai +2

The task of learning from point cloud data is always challenging due to the often occurrence of noise and outliers in the data. Such data inaccuracies can significantly influence t…

cs.CV2021

Evaluation of Point Pattern Features for Anomaly Detection of Defect within Random Finite Set Framework

Ammar Mansoor Kamoona, Amirali Khodadadian Gostar, Alireza Bab-Hadiashar +1

Defect detection in the manufacturing industry is of utmost importance for product quality inspection. Recently, optical defect detection has been investigated as an anomaly detect…

cs.CV2020

Robust Object Classification Approach using Spherical Harmonics

Ayman Mukhaimar, Ruwan Tennakoon, Chow Yin Lai +2

In this paper, we present a robust spherical harmonics approach for the classification of point cloud-based objects. Spherical harmonics have been used for classification over the…