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Pradip Mainali
Researcher at Katholieke Universiteit Leuven
Publications - 22
Citations - 233
Pradip Mainali is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Filter (signal processing) & Pixel. The author has an hindex of 6, co-authored 22 publications receiving 187 citations. Previous affiliations of Pradip Mainali include IMEC.
Papers
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Journal ArticleDOI
SIFER: Scale-Invariant Feature Detector with Error Resilience
TL;DR: The proposed feature detection algorithm leads to an outstanding scale-invariant feature detection quality, albeit at reduced planar rotational invariance, and is scalable with the filter order, providing many quality-complexity trade-off working points.
Journal ArticleDOI
Robust Low Complexity Corner Detector
TL;DR: This paper redesigned Harris and KLT algorithms to reduce their complexity in each stage of the algorithm: Gaussian derivative, cornerness response, and non-maximum suppression (NMS), achieving a complexity reduction by a factor of 9.8.
Journal ArticleDOI
Derivative-Based Scale Invariant Image Feature Detector With Error Resilience
TL;DR: A novel scale-invariant image feature detection algorithm (D-SIFER) using a newly proposed scale-space optimal 10th-order Gaussian derivative (GDO-10) filter, which reaches the jointly optimal Heisenberg's uncertainty of its impulse response in scale and space simultaneously.
Patent
Feature Detection in Numeric Data
Pradip Mainali,Gauthier Lafruit +1 more
TL;DR: In this paper, a method for detecting features in digital numeric data comprises obtaining digital data comprising values corresponding to a plurality of sampling points over a domain space having at least one dimension.
Journal ArticleDOI
Explainable Machine Learning for Fraud Detection
Ismini Psychoula,Andreas Gutmann,Pradip Mainali,Sharon H. Lee,Paul Dunphy,Fabien A. P. Petitcolas +5 more
TL;DR: In this article, explainability methods in the domain of real-time fraud detection were explored by investigating the selection of appropriate background data sets and runtime tradeoffs on supervised and unsupervised models.