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Vision Lab

Neil Bruce Neil D. B. Bruce
Associate Professor
School of Computer Science
University of Guelph
Canada
brucen@uoguelph.ca		 Interested in research? I'm currently seeking graduate students and undergraduate project students. Click here to find out more.
 

Dr. Neil Bruce graduated from the University of Guelph with a BSc Double major in CS and Pure Math. Dr. Bruce then attended the University of Waterloo for a MASc in System Design Engineering and York University for a PhD in Computer Science. Prior to joining Guelph he worked in the Department of Computer Science at Ryerson University. Prior to this Dr. Bruce worked at the University of Manitoba as Assistant then Associate Professor. Dr. Bruce has postdoctoral experience working at INRIA (France) and Epson Canada. His research has explored solutions to issues in computer vision, deep-learning, human perception, neuroscience and visual computing.

For a more complete list of publications see my Google Scholar page

  
 
Selected Publications
  
                                          

Revisiting Saliency Metrics: Farthest-Neighbor Area Under Curve S Jia, NDB Bruce, In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020

EML-net: An expandable multi-layer network for saliency prediction S Jia, NDB Bruce, Image and Vision Computing, 103887, 2020

How much Position Information Do Convolutional Neural Networks Encode? MA Islam, S Jia, NDB Bruce International Conference on Learning Representations (ICLR), 2020

Distributed Iterative Gating Networks for Semantic Segmentation R Karim, MA Islam, NDB Bruce The IEEE Winter Conference on Applications of Computer Vision (WACV) , 2844-2853, 2020

In-depth Evaluation and Experimental Analysis of a Weight Pruning Genetic Algorithm S Janjic, P Thulasiraman, N Bruce 2019 IEEE Congress on Evolutionary Computation (CEC), 1814-1821, 2019

Recurrent Iterative Gating Networks for Semantic Segmentation R Karim, MA Islam, NDB Bruce 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), 1070-1079, 2019

Relative Saliency and Ranking: Models, Metrics, Data, and Benchmarks M Kalash, MA Islam, NDB Bruce In IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2018

Redundancy in convolutional neural networks: Insights on model compression and structure S Janjic, P Thulasiraman, N Bruce 2018 International Joint Conference on Neural Networks (IJCNN), 1-8, 2018

Capturing real-world gaze behaviour: live and unplugged K Singh, M Kalash, N Bruce Proceedings of the 2018 ACM Symposium on Eye Tracking Research and Applications (ETRA), 2018

Revisiting salient object detection: Simultaneous detection, ranking, and subitizing of multiple salient objects M Amirul Islam, M Kalash, NDB Bruce Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 29, 2018

Salient Object Detection using a Context-Aware Refinement Network MA Islam, M Kalash, M Rochan, N Bruce, Y Wang 2017 British Machine Vision Conference (BMVC 2017), 2017

Gated feedback refinement network for dense image labeling M Amirul Islam, M Rochan, NDB Bruce, Y Wang Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017

Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features JFG Boisvert, NDB Bruce Neurocomputing 207, 653-668, 2016

NDB Bruce, C Catton, S Janjic A Deeper Look at Saliency: Feature Contrast, Semantics, and Beyond, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 516-524

SK Ryman, NDB Bruce, MS Freund, Temporal responses of chemically diverse sensor arrays for machine olfaction using artificial intelligence, Sensors and Actuators B: Chemical 231, 666-674

JFG Boisvert, NDB Bruce, Predicting task from eye movements: On the importance of spatial distribution, dynamics, and image features, Neurocomputing

S Rahman, NDB Bruce. Factors underlying inter-observer agreement in gaze patterns: predictive modelling and analysis. In Proceedings of the Ninth Biennial ACM Symposium on Eye Tracking Research & Applications (ETRA '16). ACM, New York, NY, USA, 155-162.

S Rahman, N Bruce, Saliency, Scale and Information: Towards a Unifying Theory, Advances in Neural Information Processing Systems (NIPS 2015), 2179-2187

NDB Bruce, S Rahman, D Carrier, Sparse coding in early visual representation: From specific properties to general principles, Neurocomputing 171, 1085-1098

NDB Bruce, C Wloka, N Frosst, S Rahman, JK Tsotsos, On computational modeling of visual saliency: Examining what’s right, and what’s left, Vision research 116, 95-112

HR Nasrinpour, NDB Bruce, Saliency weighted quality assessment of tone-mapped images, 2015 IEEE International Conference on Image Processing (ICIP), 4947-4951

S Rahman, N Bruce, Visual Saliency Prediction and Evaluation across Different Perceptual Tasks, PLoS ONE 10 (9), e0138053

B Ens, E Ofek, N Bruce, P Irani, Spatial Constancy of Surface-Embedded Layouts across Multiple Environments, Proceedings of the 3rd ACM Symposium on Spatial User Interaction, 65-68

D Lundqvist, N Bruce, A Öhman, Finding an emotional face in a crowd: Emotional and perceptual stimulus factors influence visual search efficiency, Cognition and Emotion 29 (4), 621-633

R Hettiarachchi, J Peters, N Bruce, Fence-like Quasi-periodic Texture Detection in Images, Theory and Applications of Mathematics & Computer Science 4 (2), 123-139

M Nakane, JE Young, and NDB Bruce. 2014. More human than human?: a visual processing approach to exploring believability of android faces. In Proceedings of the second international conference on Human-agent interaction (HAI '14). ACM, New York, NY, USA, 377-381.

S Rahman, M Rochan, Y Wang, NDB Bruce, Examining visual saliency prediction in naturalistic scenes 2014 IEEE International Conference on Image Processing (ICIP), 4082-4086

M Rochan, S Rahman, NDB Bruce, Y Wang, Segmenting objects in weakly labeled videos, 2014 Canadian Conference onComputer and Robot Vision (CRV) 119-126

Bruce, N.D.B., Towards Fine Grained Fixation Analysis: Distilling Out Contect Dependence, ACM Symposium on Eye Tracking Research and Applications (ETRA 2014), 2014.

Bruce, N.D.B., ExpoBlend: Information preserving exposure blending based on normalized log-domain entropy, Computers & Graphics, 39:12-23, 2014.

Yang, X-D., Hasan, K., Bruce, N.D.B., Irani, P., Surround-See: Enabling Peripheral Vision on Smartphones during Active Use . In Proceedings of the 26th Symposium on User Interface Software and Technology (UIST'13). St. Andrews, UK. ACM.

Bruce, N.D.B., Non-linear normalized entropy based exposure blending. Proceedings of Graphics Interface 2013, 37-44, 2013.
Feedforward Feedback Spatial and Spatiotemporal Saliency

Bruce, N.D.B*, Shi, X.*, Tsotsos, J.K., Recurrent Refinement for Visual Saliency Estimation in Surveillance Scenarios,  Computer and Robot Vision (CRV), 2012, 117-124, 2012.
Schematic of recurrent computation

Shi, X., Bruce, N.D.B., Tsotsos, J.K., Biologically motivated local contextual modulation improves low-level visual feature representations, Image Analysis and Recognition, 79-88, 2012.
Fixating in one place, attending elsewhere

Bruce, N.D.B., Tsotsos, J.K., Attention in Stereo Vision: Implications for Computational Models of Attention. In Pomplun, M., Suzuki, J., Developing and Applying Biologically-Inspired Vision Systems: Interdisciplinary Concepts. IGI Global, 2012.
Asymmetric Search Tasks

Bruce, N.D.B. Tsotsos, J.K., Visual representation determines search difficulty: explaining visual search asymmetries. Frontiers in Computational Neuroscience. 5:33, 2011.
Fast recurrent computation

Shi, X.*, Bruce, N.D.B.*, Tsotsos, J.K., Fast, Recurrent, Attentional Modulation Improves Saliency Representation and Scene Recognition, CVPR Workshop on Biologically-Consistent Vision, 2011. 
How representation influences salience

Bruce, N.D.B., Shi, X., Simine, E., Tsotsos, J.K., Visual Representation in the Determination of Saliency, in Proceedings of CRV 2011, 25-27, pp. 242-249, St. Johns, NL. 
A schematic of the AIM model of visual saliency

Bruce, N.D.B., Tsotsos, J.K., Saliency, Attention, and Visual Search: An Information Theoretic Approach, Journal of Vision 9(3):5, 1-24, 2009, http://journalofvision.org/9/3/5/, doi:10.1167/9.3.5.
Contextual effects on unexpected events in a stadium

Bruce, N.D.B., and Kornprobst, P., On the role of context in probabilistic models of visual saliency, In proceedings of the IEEE International Conference on Image processing, 2009.
Comparison of criteria for selecting Harris Corners

Bruce, N.D.B., and Kornprobst, P., Harris corners in the Real World: A Principled Selection Criterion for Interest Points Based on Ecological Statistics, In Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, 2009.
The many interpretations of the classic Dalmatian image

Tsotsos, J.K., Bruce, N.D.B., Computational foundations for attentive processes, Scholarpedia 3(12):6545, 2008.
Left and Right handed handles

Loach, D., Frischen, A., Bruce, N.D.B., Tsotsos, J.K., An attentional mechanism for selecting appropriate actions afforded by graspable objects, Psychological Science 19(12), p 1253-1257, 2008.
Saliency based on Spatiotemporal Patterns

Bruce, N.D.B., Tsotsos, J.K., Spatiotemporal Saliency: Towards a Hierarchical Representation of Visual Saliency, 5th Int. Workshop on Attention in Cognitive Systems, Santorini Greece, May 12, 2008. Selected for oral presentation
Local frequency content in windowed regions

Bruce, N.D.B., Loach, D., Tsotsos, J.K., Visual Correlates Of Fixation Selection: A Look At The Spatial Frequency Domain, IEEE Int. Conf. on Image Processing, San Antonio, Sept. 16-19, 2007.
Predicting Saliency: The classic conjunction search

Bruce, N.D.B., Tsotsos, J.K., An Information Theoretic Model of Saliency and Visual Search, L. Paletta and E. Rome (Eds.): WAPCV 2007, LNAI 4840, pp. 171-183, 2007. Selected for oral presentation
Circuitry implementing local saliency driven analysis

Bruce, N.D.B., Tsotsos, J.K., Saliency Based on Information Maximization.Advances in Neural Information Processing Systems, 18, pp. 155-162, June 2006. Selected for oral presentation
Statistical bias in an image due to perspective

Bruce, N.D.B., Tsotsos, J.K., A Statistical Basis for Visual Field Anisotropies.Neurocomputing, v. 69:10-12, pp. 1301-1304, June 2006.
A stereo match via attention

Bruce, N.D.B., Tsotsos, J.K., An Attentional Framework for Stereo Vision, InProceedings of the 2nd Canadian Conference on Computer and Robot Vision, Victoria, BC, 2005. Selected for oral presentation

Bruce, N.D.B., Features that draw visual attention: An information theoretic perspective. Neurocomputing, v. 65-66, pp. 125-133, May 2005.

Bruce, N.D.B., Image analysis through local information measures. In Proceedings of the 17th International Conference on Pattern Recognition, Cambridge, UK, 2004.

Bruce, N.D.B., Jernigan, M.E. Evolutionary Design of Context-Free Attentional Operators. In Proceedings of the IEEE International Conference on Image Processing, Barcelona, Spain, 2003.
Optical Flow associated with movement of pigs

Zelek, J.S., Bruce, N.D.B., Kanwar, R. Automated Monitoring of Pigs from Afar. In Proceedings Vision Interface 2000, pp. 166-173, Montreal, QC, 2000.
Theses, Book Chapters and Technical Reports
    
 

Bruce, N.D.B., Saliency, Attention and Visual Search: An Information Theoretic Approach, Ph.D. Thesis, York University, 2008.
 

Bruce, N.D.B., Tsotsos, J.K., A Connectionist Perspective on Laterality in Visual Attention, York University, Department of Computer Science, Technical Report, March 2005.
 

Tombu, M., Bruce, N.D.B., Rothenstein, A., Tsotsos, J.K. A Brief Tour of This Volume, Neurobiology of Attention, Ed. by L. Itti, G. Rees, J.K. Tsotsos, Elsevier/Academic Press, 2004.
 

Bruce, N.D.B. Evolutionary Design for Computational Visual Attention. M.A.Sc. Thesis, University of Waterloo, 2003.
Abstracts, Posters and Invited Talks
    
 

Endres D., Hoffken M., Vintila F., Bruce N., Bouecke, J.D., Kornprobst P., Neumann H. & Giese M. (2010). Hooligan Detection: the Effects of Saliency and Expert Knowledge. ECVP 2010 and Perception, 39, page 193.

  
 

Bruce, N.D.B., On the use of Shannon information and Bayesian surprise in modeling attention, Models and Mechanisms of Visual Attention: A Critical Appraisal, Workshop at the Neural Information Processing Systems (NIPS), 2007. Invited Talk

  
 

Bruce, N.D.B., Tsotsos, J.K., Attention based on information maximization.Journal of Vision, 7(9):950a, 2007. http://journalofvision.org/7/9/950/,doi:10.1167/7.9.950.

  
 

Bruce, N.D.B., Tsotsos., J.K., Attention based on Information Maximization, ICVS 2007 Workshop: From Computational Cognitive Neuroscience to Computer Vision, Bielefeld,  2007. Invited talk

  
 

Bruce, N.D.B., Tsotsos, J.K., A statistical basis for visual field anisotropies. CNS 2005, Annual Computational Neuroscience Meeting, July 17-21 2005, Madison, WI, USA. Poster

  
 

Bruce, N.D.B., Features That Draw Visual Attention An Information Theoretic Perspective. CNS 2004, Annual Computational Neuroscience Meeting, July 18-20 2004, Baltimore, MD, USA. Poster