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Welcome to my webpage. I am passionate about applying my knowledge of computer vision (CV) and machine learning (ML) to difficult practical problems. 

Since 2019 I've been a full Professor at the University of Bonn (Germany) where my group focuses on applied computer vision/robotic vision approaches that enable robots and autonomous systems in Agriculture to interpret their environment, enabling them to act. Important areas of research are agricultural robots and autonomous systems as well as scalable classification and learning approaches. You can find out more about my group along with our current activities here.

Short CV

Chris McCool is a Full Professor at the University of Bonn, he heads the AgRobotics Lab and part of the Lamarr Institute for Machine Learning and Artificial Intelligence. He is the spokesperson (lead) of the DFG AI Research Unit AID4Crops (FOR5351) and a Principal Investigator of the DFG Funded Cluster of Excellence EXC 2070 "PhenoRob – Robotics and Phenotyping for Sustainable Crop Production". His research focuses on applied computer vision and machine learning probabilistic techniques for solutions to a range of challenges including agricultural robotics, visual perception, fine-grained scene understanding, and robot learning. He has coauthored more than 80 publications.

Prior to joining the University of Bonn, Chris was a Researcher and Senior Researcher (2014-2018) at the Queensland University of Technology (QUT) working on Computer Vision problems applied to Agricultural applications. From 2012-2014 he worked as a Researcher at NICTA on Environmental Computer Vision. From 2008-2012 he was a post-doctoral research at the Idiap Research Institute working in the Biometrics group led by Sebastien Marcel.

Extended CV available upon request.

Awards


Publications

2024

M. Halstead, P. Zimmer, and C. McCool, “A Cross‑Domain Challenge with Panoptic Segmentation in Agriculture,” International Journal of Robotics Research, 2024.

C. Smitt, M. Halstead, P. Zimmer, T. Läbe, E. Güclü, C. Stachniss, and C. McCool, “PAg‑NeRF: Towards fast and efficient end‑to‑end panoptic 3D representations for agricultural robotics,” IEEE Robotics and Automation Letters, vol. 9, pp. 907–914, 2024.

2023

P. Zimmer, M. Halstead, and C. McCool, “Panoptic One‑Click Segmentation: Applied to Agricultural Data,” IEEE Robotics and Automation Letters, 2023.

M. Li, M. Halstead, and C. McCool, “Knowledge distillation for efficient panoptic semantic segmentation applied to agriculture,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.

Y. Pan, F. Magistri, T. Läbe, E. Marks, C. Smitt, C. McCool, J. Behley, and C. Stachniss, “Panoptic mapping with fruit completion and pose estimation for horticultural robots,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2023.

2022

C. Smitt, M. Halstead, A. Ahmadi, and C. McCool, “Explicitly incorporating spatial information to recurrent networks for agriculture,” IEEE Robotics and Automation Letters, 2022.

F. Magistri, E. Marks, S. Nagulavancha, I. Vizzo, T. Laebe, J. Behley, M. Halstead, C. McCool, and C. Stachniss, “Constrastive 3D Shape Completion and Reconstruction for Agricultural Robots using RGB‑D Frames,” IEEE Robotics and Automation Letters, 2022.

A. Ahmadi, M. Halstead, and C. McCool, “BonnBot‑I: a precise weed management and crop monitoring platform,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.

A. Ahmadi, M. Halstead, and C. McCool, “Towards autonomous visual navigation in arable fields,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2022.

2021

M. Halstead, A. Ahmadi, C. Smitt, O. Schmittmann, and C.McCool, “Crop Agnostic Monitoring Driven by Deep Learning,” Frontiers in Plant Science, 2021.

A. Ahmadi, M. Halstead, and C. McCool, “Virtual temporal samples for recurrent neural networks: Applied to semantic segmentation in agriculture,” in German Conference on Pattern Recognition (GCPR/DAGM), 2021.

C. Smitt, M. Halstead, T. Zaenker, M. Bennewitz, and C. McCool, “PATHoBot: A Robot for Glasshouse Crop Phenotyping and Intervention,” in IEEE International Conference on Robotics and Automation (ICRA), 2021.

T. Zaenker, C. Smitt, C. McCool, and M. Bennewitz, “Viewpoint planning for fruit size and position estimation,” in International Conference on Intelligent Robots and Systems, 2021.

T. Zaenker, C. Lehnert, C. McCool, and M. Bennewitz, “Combining local and global viewpoint planning for fruit coverage,” in 2021 European Conference on Mobile Robots (ECMR), 2021.

2020

C. Lehnert, C. McCool, I. Sa, and T. Perez, “Performance improvements of a sweet pepper harvesting robot in protected cropping environments,” Journal of Field Robotics, vol. 37, no. 7, pp. 1197–1223, 2020.

M. Halstead, S. Denman, C. Fookes, and C. McCool, “Fruit Detection in the Wild: the impact of varying conditions and cultivar,” in Digital Image Computing: Theory and Applications, 2020.

A. Milioto, J. Behley, C. McCool, and C. Stachniss, “Lidar panoptic segmentation for autonomous driving,” in IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.

2019

B. Arain, C. McCool, P. Rigby, D. Cagara, and M. Dunbabin, “Improving underwater obstacle detection using semantic image segmentation,” in 2019 International Conference on Robotics and Automation (ICRA), 2019, pp. 9271–9277.

C. Lehnert, D. Tsai, A. Eriksson, and C. McCool, “3d move to see: Multi‑perspective visual servoing towards the next best view within unstructured and occluded environments,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2019, pp. 3890–3897.

2018

D. Hall, F. Dayoub, T. Perez, and C. McCool, “A rapidly deployable classification system using visual data for the application of precision weed management,” Computers and Electronics in Agriculture, vol. 148, pp. 107–120, 2018.

C. McCool, J. Beattie, J. Firn, C. Lehnert, J. Kulk, O. Bawden, R. Russell, and T. Perez, “Efficacy of mechanical weeding tools: A study into alternative weed management strategies enabled by robotics,” IEEE Robotics and Automation Letters, vol. 3, no. 2, pp. 1184–1190, 2018.

M. Halstead, C. McCool, S. Denman, T. Perez, and C. Fookes, “Fruit quantity and ripeness estimation using a robotic vision system,” IEEE Robotics and Automation Letters, 2018.

C. McCool, J. Beattie, M. M. Milford, J. D. Bakker, J. L. Moore, and J. Firn, “Automating analysis of vegetation with computer vision: Cover estimates and classification,” Ecology and Evolution, vol. 8, no. 12, pp. 6005–6015, 2018.

2017

C. Lehnert, A. English, C. McCool, A. Tow, and T. Perez, “Autonomous sweet pepper harvesting for protected cropping systems,” IEEE Robotics and Automation Letters, 2017.

C. McCool, T. Perez, and B. Upcroft, “Mixtures of lightweight deep convolutional neural networks: Applied to agricultural robotics,” IEEE Robotics and Automation Letters, vol. 2, pp. 1344–1351, 2017. 

O. Bawden, J. Kulk, R. Russell, C. McCool, A. English, F. Dayoub, C. Lehnert, and T. Perez, “Robot for weed species plant‑specific management,” Journal of Field Robotics, vol. 34, no. 6, pp. 1179–1199, 2017.

I. Sa, C. Lehnert, A. English, C. McCool, F. Dayoub, B. Upcroft, and T. Perez, “Peduncle detection of sweet pepper for autonomous crop harvesting ‑ combined color and 3‑d information,” IEEE Robotics and Automation Letters, vol. 2, pp. 765–772, 2017.

D. Hall, F. Dayoub, J. Kulk, and C. McCool, “Towards unsupervised weed scouting for agricultural robotics,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 5223–5230.

T. Perez, C. McCool, O. Bawden, and J. Kulk, “Robotic weeding–from concept to trials,” in Asian‑Australasian Conference on Precision Agriculture, 2017.

D. Hall, F. Dayoub, T. Perez, and C. McCool, “A transplantable system for weed classification by agricultural robotics,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017, pp. 5174–5179.

C. McCool and T. Perez, “Improved vision‑based weed classification for robotic weeding–a method for increasing speed while retaining accuracy,” in Asian‑Australasian Conference on Precision Agriculture, 2017.

J. Leitner, A. Tow, N. Sünderhauf, J. Dean, J. Durham, M. Cooper, M. Eich, C. Lehnert, R. Mangels, et al., “The ACRV picking benchmark: A robotic shelf picking benchmark to foster reproducible research,” in 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017, pp. 4705–4712.

2016

I. Sa, Z. Ge, F. Dayoub, B. Upcroft, T. Perez, and C. McCool, “Deepfruits: A fruit detection system using deep neural networks,” Sensors, 2016.

J. Carvajal, A. Wiliem, C. McCool, B. Lovell, and C. Sanderson, “Comparative evaluation of action recognition methods via riemannian manifolds, fisher vectors and gmms: Ideal and challenging conditions,” in Trends and Applications in Knowledge Discovery and Data Mining, 2016, pp. 88–100.

J. Carvajal, C. McCool, B. Lovell, and C. Sanderson, “Joint recognition and segmentation of actions via probabilistic integration of spatio‑temporal fisher vectors,” in Trends and Applications in Knowledge Discovery and Data Mining, H. Cao, J. Li, and R. Wang, Eds., 2016, pp. 115–127.

Z. Ge, A. Bewley, C. McCool, P. Corke, B. Upcroft, and C. Sanderson, “Fine‑grained classification via mixture of deep convolutional neural networks,” in 2016 IEEE Winter Conference on Applications of Computer Vision (WACV), 2016.

C. McCool, Z. Ge, and P. Corke, “Feature learning via mixtures of dcnns for fine‑grained plant classification,” in CLEF (Working Notes), 2016.

Z. Ge, C. McCool, C. Sanderson, P. Wang, L. Liu, I. Reid, and P. Corke, “Exploiting temporal information for dcnn‑based fine‑grained object classification,” in 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2016.

C. Lehnert, I. Sa, C. McCool, B. Upcroft, and T. Perez, “Sweet pepper pose detection and grasping for automated crop harvesting,” in IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 2428–2434.

C. McCool, I. Sa, F. Dayoub, C. Lehnert, T. Perez, and B. Upcroft, “Visual detection of occluded crop: For automated harvesting,” in IEEE International Conference on Robotics and Automation (ICRA), 2016, pp. 2506–2512.

2015

I. Sa, C. McCool, C. Lehnert, and T. Perez, “On visual detection of highly‑occluded objects for harvesting automation in horticulture,” in Proceedings of the 2015 Workshop on Robotics in Agriculture at ICRA, 2015.

 S. Shirazi, C. Sanderson, C. McCool, and M. Harandi, “Bags of affine subspaces for robust object tracking,” in 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 2015.

Z. Ge, C. McCool, C. Sanderson, A. Bewley, Z. Chen, and P. Corke, “Fine‑grained bird species recognition via hierarchical subset learning,” in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 561–565.

Z. Ge, C. McCool, C. Sanderson, and P. Corke, “Modelling local deep convolutional neural network features to improve fine‑grained image classification,” in 2015 IEEE International Conference on Image Processing (ICIP), 2015, pp. 4112–4116.

D. Hall, C. McCool, F. Dayoub, N. Sunderhauf, and B. Upcroft, “Evaluation of features for leaf classification in challenging conditions,” in 2015 IEEE Winter Conference on Applications of Computer Vision, 2015, pp. 797–804.

C. Lehnert, T. Perez, and C. McCool, “Optimisation‑based design of a manipulator for harvesting capsicum,” in Workshop on Robotics in Agriculture at ICRA, 2015.

Z. Ge, C. McCool, C. Sanderson, and P. Corke, “Content specific feature learning for fine‑grained plant classification,” in CLEF Working Notes, 2015.

Z. Ge, C. McCool, C. Sanderson, and P. Corke, “Subset feature learning for fine‑grained category classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2015.

2014

J. Carvajal, C. Sanderson, C. McCool, and B. Lovell, “Multi‑action recognition via stochastic modelling of optical flow and gradients,” in 2nd Workshop on Machine Learning for Sensory Data Analysis, 2014.

N. Suenderhauf, C. McCool, B. Upcroft, and T. Perez, “Fine‑grained plant classification using convolutional neural networks for feature extraction.,” in CLEF Working Notes, 2014.

K. Anantharajah, Z. Ge, C. McCool, S. Denman, C. Fookes, P. Corke, D. Tjondronegoro, and S. Sridharan, “Local inter‑session variability modelling for object classification,” in IEEE Winter Conference on Applications of Computer Vision, 2014, pp. 309–316.

J. Carvajal, C. McCool, and C. Sanderson, “Summarisation of short‑term and long‑term videos using texture and colour,” in IEEE Winter Conference on Applications of Computer Vision, 2014, pp. 769–775.

2013

C. McCool, R. Wallace, M. McLaren, L. El Shafey, and S. Marcel, “Session variability modelling for face authentication,” IET Biometrics, 2013.

L. El Shafey, C. McCool, R. Wallace, and S. Marcel, “A scalable formulation of probabilistic linear discriminant analysis: Applied to face recognition,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 7, pp. 1788–1794, 2013.

2012

P. Tresadern, C. McCool, N. Poh, P. Matejka, A. Hadid, C. Levy, T. Cootes, and S. Marcel, “Mobile biometrics (mobio): Joint face and voice verification for a mobile platform,” IEEE Pervasive Computing, 2012.

R. Wallace, M. McLaren, C. McCool, and S. Marcel, “Cross‑pollination of normalization techniques from speaker to face authentication using gaussian mixture models,” IEEE Transactions on Information Forensics and Security, vol. 7, no. 2, pp. 553–562, 2012.

P. Motlicek, L. El Shafey, R. Wallace, C. McCool, and S. Marcel, “Bi‑modal authentication in mobile environments using session variability modelling,” in Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), 2012, pp. 1100–1103.

A. Anjos, L. El Shafey, R. Wallace, M. Günther, C. McCool, and S. Marcel, “Bob: A free signal processing and machine learning toolbox for researchers,” in Proceedings of the 20th ACM International Conference on Multimedia, 2012, pp. 1449–1452.

C. McCool, S. Marcel, A. Hadid, M. Pietikäinen, P. Matejka, J. Cernocký, N. Poh, J. Kittler, A. Larcher, et al., “Bi-modal person recognition on a mobile phone: Using mobile phone data,” in 2012 IEEE International Conference on Multimedia and Expo Workshops, 2012, pp. 635–640.

2011

R. Wallace, M. McLaren, C. McCool, and S. Marcel, “Inter‑session variability modelling and joint factor analysis for face authentication,” in 2011 International Joint Conference on Biometrics (IJCB), 2011, pp. 1–8.

2010

N. Poh, C. Chan, J. Kittler, S. Marcel, C. McCool, E. Rua, J. Alba Castro, M. Villegas, R. Paredes, et al., “An evaluation of video‑to‑video face verification,” IEEE Transactions on Information Forensics and Security, vol. 5, no. 4, pp. 781–801, 2010.

C. McCool, J. Sanchez‑Riera, and S. Marcel, “Feature distribution modelling techniques for 3d face verification,” Pattern Recognition Letters, vol. 31, no. 11, pp. 1324–1330, 2010.

J. Galbally, C. McCool, J. Fierrez, S. Marcel, and J. Ortega‑Garcia, “On the vulnerability of face verification systems to hill‑climbing attacks,” Pattern Recognition, vol. 43, no. 3, pp. 1027–1038, 2010.

S. Marcel, C. McCool, P. Matějka, T. A. J. Černocký, S. Chakraborty, V. Balasubramanian, S. Panchanathan, C. Chan, J. Kittler, et al., “On the results of the first mobile biometry (mobio) face and speaker verification evaluation,” in Recognizing Patterns in Signals, Speech, Images and Videos, 2010, pp. 210–225.

C. Atanasoaei, C. McCool, and S. Marcel, “A principled approach to remove false alarms by modelling the context of a face detector,” in British Machine Vision Conference (BMVC), 2010.

2009

J. Galbally, J. Fierrez, J. Ortega‑Garcia, C. McCool, and S. Marcel, “Hill‑climbing attack to an eigenface‑based face verification system,” in 2009 First IEEE International Conference on Biometrics, Identity and Security (BIdS), 2009, pp. 1–6.

N. Poh, C. Chan, J. Kittler, S. Marcel, C. McCool, E. Rúa, J. Castro, M. Villegas, R. Paredes, et al., “Face video competition,” in Advances in Biometrics, 2009, pp. 715–724.

2008

C. McCool, V. Chandran, S. Sridharan, and C. Fookes, “3d face verification using a free‑parts approach,” Pattern Recognition Letters, vol. 29, no. 9, pp. 1190–1196, 2008.

C. Fookes, G. Mamic, C. McCool, and S. Sridharan, “Normalisation and recognition of 3d face data using robust hausdorff metric,” in 2008 Digital Image Computing: Techniques and Applications, 2008, pp. 124–129.

2007

C. McCool, G. Mamic, C. Fookes, and S. Sridharan, “Normalisation of 3d face data,” in 1st International Conference on Signal Processing and Communication Systems, 2007.

2006

C. McCool, J. Cook, V. Chandran, and S. Sridharan, “Feature modelling of pca difference vectors for 2d and 3d face recognition,” in 2006 IEEE International Conference on Video and Signal Based Surveillance, 2006, pp. 57–57.

C. McCool, V. Chandran, and S. Sridharan, “2d‑3d hybrid face recognition based on pca and feature modelling,” in 2nd Workshop on Multimodal User Authentication, 2006.

J. Cook, C. McCool, V. Chandran, and S. Sridharan, “Combined 2d/3d face recognition using log‑gabor templates,” in 2006 IEEE International Conference on Video and Signal Based Surveillance, 2006.

2005

C. McCool, V. Chandran, A. Nguyen, and S. Sridharan, “Object recognition using stereo vision and higher order spectra,” in Digital Image Computing: Techniques and Applications (DICTA’05), 2005.

2004

S. Lowther, C. McCool, V. Chandran, and S. Sridharan, “Improving face localisation using claimed identity for face verification,” in 3rd Workshop on the Internet, Telecommunications and Signal Processing (WITSP 2004), 2004.

K. Messer, J. Kittler, M. Sadeghi, M. Hamouz, A. Kostin, F. Cardinaux, S. Marcel, S. Bengio, C. Sanderson, et al., “Face authentication test on the banca database,” in Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004., 2004, 523–532 Vol.4.

2003

D. Butler, C. McCool, M. McKay, S. Lowther, V. Chandran, and S. Sridharan, “Robust face localisation using motion, colour and fusion,” in DICTA, 2003.