Nil Goyette
Computer Vision and Pattern Recognition · 2012
A researcher in the Science corpus: 1 paper; trusts 1 researcher and is trusted by 1 researcher here, as inferred from the citation graph.
Subfields
- Computer Vision and Pattern Recognition — 13 citations into it; #19,462 of 21,575 by trust here, #12,172 by citations
Whom Nil Goyette trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Chris Stauffer
- Zoran Zivkovic
- Olivier Barnich
- Pakorn KaewTrakulPong
- Ahmed M. Elgammal
- Lucia Maddalena
- Jacinto Carlos Nascimento
- Martin Hofmann
- Thierry Bouwmans
- W. Grimson
Who trusts Nil Goyette overall
- Thierry Bouwmans
- Pierre-Luc St-Charles
- Matej Kristan
- Pablo Fernández Alcantarilla
- Yi Wang
- Sajid Javed
- Belmar García García
- Andrews Cordolino Sobral
- Guillaume-Alexandre Bilodeau
Papers from Nil Goyette's perspective
Ordered by how strongly the researchers Nil Goyette trusts endorse each paper. Same corpus, a different order for every researcher.
- Non-parametric Model for Background Subtraction (European Conference on Computer Vision)
- An Improved Adaptive Background Mixture Model for Real-time Tracking with Shadow Detection
- Learning Patterns of Activity Using Real-Time Tracking (IEEE Transactions on Pattern Analysis and Machine Intelligence)
- Background Subtraction (Encyclopedia of Biometrics)
- ViBe: A Universal Background Subtraction Algorithm for Video Sequences (IEEE Transactions on Image Processing)
- Adaptive background mixture models for real-time tracking
- A Self-Organizing Approach to Background Subtraction for Visual Surveillance Applications (IEEE Transactions on Image Processing)
- Performance evaluation of object detection algorithms for video surveillance (IEEE transactions on multimedia)
- Background Modeling using Mixture of Gaussians for Foreground Detection - A Survey
- Efficient adaptive density estimation per image pixel for the task of background subtraction (Pattern Recognition Letters)
Their papers
- Changedetection.net: A new change detection benchmark dataset (2012) — 858 citations