C
Chris McCool
Researcher at University of Bonn
Publications - 118
Citations - 3911
Chris McCool is an academic researcher from University of Bonn. The author has contributed to research in topics: Facial recognition system & Convolutional neural network. The author has an hindex of 31, co-authored 112 publications receiving 3029 citations. Previous affiliations of Chris McCool include NICTA & University of Queensland.
Papers
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Journal ArticleDOI
DeepFruits: A Fruit Detection System Using Deep Neural Networks.
TL;DR: A novel multi-modal Faster R-CNN model, which achieves state-of-the-art results compared to prior work with the F1 score, which takes into account both precision and recall performances improving from 0.807 to 0.838 for the detection of sweet pepper.
Proceedings ArticleDOI
Bi-Modal Person Recognition on a Mobile Phone: Using Mobile Phone Data
Chris McCool,Sébastien Marcel,Abdenour Hadid,Matti Pietikäinen,Pavel Matejka,Jan Cernock #x Fd,Norman Poh,Josef Kittler,Anthony Larcher,Christophe Lévy,Driss Matrouf,Jean-François Bonastre,Phil Tresadern,Timothy F. Cootes +13 more
TL;DR: It is shown, on this mobile phone database, that face and speaker recognition can be performed in a mobile environment and using score fusion can improve the performance by more than 25% in terms of error rates.
Proceedings ArticleDOI
Bob: a free signal processing and machine learning toolbox for researchers
TL;DR: Bob is a free signal processing and machine learning toolbox originally developed by the Biometrics group at Idiap Research Institute, Switzerland that provides a researcher-friendly Python environment for rapid development and supports reproducible research through its integrated experimental protocols for several databases.
Journal ArticleDOI
Autonomous Sweet Pepper Harvesting for Protected Cropping Systems
TL;DR: In this paper, a new robotic harvester (Harvey) that can autonomously harvest sweet peppers in protected cropping environments is presented, which combines effective vision algorithms with a novel end-effector design to enable successful harvesting of sweet peppers.
Journal ArticleDOI
Mixtures of Lightweight Deep Convolutional Neural Networks: Applied to Agricultural Robotics
TL;DR: This work proposes a novel approach for training deep convolutional neural networks (DCNNs) that allows for tradeoff complexity and accuracy to learn lightweight models suitable for robotic platforms such as AgBot II (which performs automated weed management).