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Associate Professor Brendan McCane

Brendan

Owheo Building, Room 2.52
Phone: +64 3 479 8588
Email: mccane@cs.otago.ac.nz

My research interests include computer vision, pattern recognition, machine learning, biomedical imaging, and robotics. My current research focuses on theoretical understanding of the effectiveness of deep networks, and self-learning for robots.

I also have an interest in computer graphics and participate in the computer graphics group here at the University of Otago.

My background is in Computer Science and I completed my undergraduate studies and PhD at James Cook University of North Queensland in Australia. My PhD was entitled "Learning to Recognise 3D Objects from 2D Intensity Images", which I completed in February 1996. I then held a temporary position as a lecturer at James Cook University, before taking up a position in February 1997 as a lecturer with the Computer Science Department here at Otago University.

For more information, see my research pages.

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Publications

McCane, B., & Szymanski, L. (2018). Efficiency of deep networks for radially symmetric functions. Neurocomputing. Advance online publication. doi: 10.1016/j.neucom.2018.06.003

Mesbah, R., McCane, B., & Mills, S. (2018). Conditional random fields incorporate convolutional neural networks for human eye sclera semantic segmentation. Proceedings of the IEEE International Joint Conference on Biometrics (IJCB). (pp. 768-773). IEEE. doi: 10.1109/BTAS.2017.8272768

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2018). Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks. arXiv. Retrieved from https://arxiv.org/abs/1802.03875

McCane, B., & Szymanski, L. (2017). Deep networks are efficient for circular manifolds. Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). (pp. 3464-3469). IEEE. doi: 10.1109/ICPR.2016.7900170

Slack, D., McCane, B., & Knott, A. (2017). Self-organising temporal pooling. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 4316-4323). IEEE. doi: 10.1109/IJCNN.2017.7966402

Chapter in Book - Research

Fu, X., McCane, B., Mills, S., & Albert, M. (2015). NOKMeans: Non-Orthogonal K-means hashing. In D. Cremers, I. Reid, H. Saito & M.-H. Yang (Eds.), Computer Vision ACCV 2014: Lecture notes in computer science (Vol. 9003). (pp. 162-177). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-16865-4_11

Kukenys, I., McCane, B., & Neumegen, T. (2010). Training support vector machines on large sets of image data. In H. Zha, R.-I. Taniguchi & S. Maybank (Eds.), Computer vision ACCV 2009: Lecture notes in computer science (Vol. 5996). (pp. 331-340). Berlin, Germany: Springer. doi: 10.1007/978-3-642-12297-2_32

McCane, B., Caelli, T., & de Vel, O. (1998). Inducing complex spatial descriptions in two dimensional scenes. In Artificial intelligence: Learning and reasoning with complex representations. Volume 1359. (pp. 123-132). Springer.

McCane, B. (1997). Fuzzy conditional rule generation for the learning and recognition of 3D objects. In T. Caelli & W. Bischof (Eds.), Machine Learning and Image Interpretation. (pp. 17-65). New York: Plenum.

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Journal - Research Article

McCane, B., & Szymanski, L. (2018). Efficiency of deep networks for radially symmetric functions. Neurocomputing. Advance online publication. doi: 10.1016/j.neucom.2018.06.003

Bennani, H., McCane, B., & Cornwall, J. (2016). Three dimensional (3D) lumbar vertebrae data set. Data Science Journal, 15, 9. doi: 10.5334/dsj-2016-009

Scott, G. H., Ingle, Jr, J. C., McCane, B., Powell, II, C. L., & Thunell, R. C. (2015). Truncorotalia crassaformis from its type locality: Comparison with Caribbean plankton and Pliocene relatives. Marine Micropaleontology, 117, 1-12. doi: 10.1016/j.marmicro.2015.02.001

Khan, N., McCane, B., & Mills, S. (2015). Better than SIFT? Machine Vision & Applications, 26(6), 819-836. doi: 10.1007/s00138-015-0689-7

Szymanski, L., & McCane, B. (2014). Deep networks are effective encoders of periodicity. IEEE Transactions on Neural Networks & Learning Systems, 25(10), 1816-1827. doi: 10.1109/TNNLS.2013.2296046

McCane, B. (2013). Shape variation in outline shapes. Systematic Biology, 62(1), 134-146. doi: 10.1093/sysbio/sys080

Kukenys, I., & McCane, B. (2013). Touch tracking with a particle filter. Machine Vision & Applications, 24(7), 1501-1512. doi: 10.1007/s00138-013-0486-0

McCane, B., & Kean, M. R. (2011). Integration of parts in the facial skeleton and cervical vertebrae. American Journal of Orthodontics & Dentofacial Orthopedics, 139(1), e13-e30. doi: 10.1016/j.ajodo.2010.06.016

Lam, S. C. B., McCane, B., & Allen, R. (2009). Automated tracking in digitized videofluoroscopy sequences for spine kinematic analysis. Image & Vision Computing, 27(10), 1555-1571. doi: 10.1016/j.imavis.2009.02.010

Angelidis, A., & McCane, B. (2009). Fur simulation with spring continuum. Visual Computer, 25(3), 255-265. doi: 10.1007/s00371-008-0218-z

McCane, B., & Albert, M. (2008). Distance functions for categorical and mixed variables. Pattern Recognition Letters, 29(7), 986-993. doi: 10.1016/j.patrec.2008.01.021

Navarro Newball, A. A., Wyvill, G., & McCane, B. (2008). Efficient mesh generation using subdivision surfaces. Sistemas & Telemática, 6(12), 111-126.

Abbott, J. H., Fritz, J. M., McCane, B., Shultz, B., Herbison, P., Lyons, B., … Walsh, R. M. (2006). Lumbar segmental mobility disorders: Comparison of two methods of defining abnormal displacement kinematics in a cohort of patients with non-specific mechanical low back pain. BMC Musculoskeletal Disorders, 7, 45. doi: 10.1186/1471-2474-7-45

McCane, B., King, T. I., & Abbott, J. H. (2006). Calculating the 2D motion of lumbar vertebrae using splines. Journal of Biomechanics, 39, 2703-2708. doi: 10.1016/j.jbiomech.2005.09.015

Abbott, J. H., McCane, B., Herbison, P., Moginie, G., Chapple, C., & Hogarty, T. (2005). Lumbar segmental instability: A criterion-related validity study of manual therapy assessment. BMC Musculoskeletal Disorders, 6, 56. doi: 10.1186/1471-2474-6-56

McCane, B., Abbott, J. H., & King, T. (2005). On calculating the finite centre of rotation for rigid planar motion. Medical Engineering & Physics, 27, 75-79.

McCane, B., & Caelli, T. (2004). Diagnostic tools for evaluating and updating hidden Markov models. Pattern Recognition, 37, 1325-1337.

McCane, B., Galvin, B., & Novins, K. L. (2002). Algorithmic Fusion for More Robust Feature Tracking. International Journal of Computer Vision, 49(1), 79-89.

Novins, K. L., & McCane, B. (2001). Incorporating primary source material into the undergraduate computer vision curriculum. International Journal of Pattern Recognition & Artificial Intelligence, 15, 775-787.

McCane, B., Novins, K. L., Crannitch, D., & Galvin, B. (2001). On benchmarking optical flow. Computer Vision & Image Understanding, 84(1), 126-143.

McCane, B., Caelli, T., & de Vel, O. (1997). Learning to recognise 3D objects using sparse depth and intensity information. International Journal of Pattern Recognition & Artificial Intelligence, 11(6).

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Conference Contribution - Published proceedings: Full paper

Mesbah, R., McCane, B., & Mills, S. (2018). Conditional random fields incorporate convolutional neural networks for human eye sclera semantic segmentation. Proceedings of the IEEE International Joint Conference on Biometrics (IJCB). (pp. 768-773). IEEE. doi: 10.1109/BTAS.2017.8272768

McCane, B., & Szymanski, L. (2017). Deep networks are efficient for circular manifolds. Proceedings of the 23rd International Conference on Pattern Recognition (ICPR). (pp. 3464-3469). IEEE. doi: 10.1109/ICPR.2016.7900170

Slack, D., McCane, B., & Knott, A. (2017). Self-organising temporal pooling. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 4316-4323). IEEE. doi: 10.1109/IJCNN.2017.7966402

McCane, B., Ott, C., Meek, N., & Robins, A. (2017). Mastery learning in introductory programming. Proceedings of the Nineteenth Australasian Computing Education (ACE) Conference. New York, NY: ACM. doi: 10.1145/3013499.3013501

Mesbah, R., McCane, B., & Mills, S. (2016). Deep convolutional encoder-decoder for myelin and axon segmentation. Proceedings of the Image and Vision Computing New Zealand (IVCNZ) International Conference. IEEE. doi: 10.1109/ivcnz.2016.7804455

Chakraborty, T., McCane, B., Mills, S., & Pal, U. (2016). Collaborative representation based fine-grained species recognition. In D. Bailey, G. Sen Gupta & S. Marsland (Eds.), Proceedings of the Image and Vision Computing New Zealand (IVCNZ) International Conference. (pp. 42-47). IEEE. doi: 10.1109/ivcnz.2016.7804421

Fu, X., McCane, B., Mills, S., & Albert, M. (2015). How to select hashing bits? A direct measurement approach. Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ). 20. IEEE. doi: 10.1109/IVCNZ.2015.7761538

Julé, A., McCane, B., Knott, A., & Mills, S. (2015). Discriminative touch from pressure sensors. In D. Bailey, G. Sen Gupta & S. Demidenko (Eds.), Proceedings of the 6th International Conference on Automation, Robotics and Applications (ICARA). (pp. 279-282). IEEE. doi: 10.1109/icara.2015.7081160

Mikhisor, M., Wyvill, G., McCane, B., & Mills, S. (2015). Adapting generic trackers for tracking faces. Proceedings of the International Conference on Image and Vision Computing New Zealand (IVCNZ). 90. IEEE. doi: 10.1109/IVCNZ.2015.7761570

Mikhisor, M., Wyvill, G., McCane, B., & Mills, S. (2014). 3D face tracking in fisheye stereo video using particle filters. Proceedings of the 29th International Conference on Image and Vision Computing New Zealand (IVCNZ). (pp. 259-264). New York: ACM. doi: 10.1145/2683405.2683452

Khan, U. M., Mills, S., McCane, B., & Trotman, A. (2014). Emergent properties from feature co-occurrence in image collections. Proceedings of the 22nd International Conference on Pattern Recognition (ICPR). (pp. 2347-2352). IEEE. doi: 10.1109/ICPR.2014.407

McCane, B. (2014). Case deletion for fundamental matrix computation. Proceedings of the 29th International Conference on Image and Vision Computing New Zealand (IVCNZ). (pp. 25-30). New York: ACM. doi: 10.1145/2683405.2683416

Khan, N., McCane, B., & Mills, S. (2013). 3D versus 2D based indoor image matching analysis on images from low cost mobile devices. Proceedings of the 28th International Conference of Image and Vision Computing New Zealand (IVCNZ). (pp. 253-258). IEEE. doi: 10.1109/IVCNZ.2013.6727025

Mikhisor, M., Wyvill, G., McCane, B., & Mills, S. (2013). The integral image method for fisheye images. Proceedings of the 28th International Conference of Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ.2013.6726983

Szymanski, L., & McCane, B. (2013). Learning in deep architectures with folding transformations. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2013.6706945

Fu, X., Martin, S., Mills, S., & McCane, B. (2013). Improved spectral clustering using adaptive Mahalanobis distance. Proceedings of the 2nd IAPR Asian Conference on Pattern Recognition (ACPR). (pp. 171-175). IEEE. doi: 10.1109/ACPR.2013.100

Fu, X., McCane, B., Albert, M., & Mills, S. (2013). Action recognition based on principal geodesic analysis. Proceedings of the 28th International Conference of Image and Vision Computing New Zealand (IVCNZ). (pp. 259-264). IEEE. doi: 10.1109/IVCNZ.2013.6727026

McCane, B. (2012). The linear space of all images smaller than 32x32. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 156-161). New York: ACM. [Full Paper]

Szymanski, L., & McCane, B. (2012). Deep, super-narrow neural network is a universal classifier. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2012.6252513

Khan, U. M., McCane, B., & Trotman, A. (2012). A feature compression scheme for large scale image retrieval systems. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 492-496). New York: ACM. [Full Paper]

Khan, N., McCane, B., & Mills, S. (2012). Feature set reduction for image matching in large scale environments. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 67-72). New York: ACM. doi: 10.1145/2425836.2425852

Khan, U. M., McCane, B., & Trotman, A. (2012). Emergent semantic patterns in large scale image dataset: A datamining approach. Proceedings of the International Conference on Digital Image Computing Techniques and Applications (DICTA). IEEE. doi: 10.1109/DICTA.2012.6411739

Szymanski, L., & McCane, B. (2012). Push-pull separability objective for supervised layer-wise training of neural networks. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2012.6252366

McCane, B. (2011). Model-based estimation of vertebrae from uncalibrated bi-planar XRays. In P. Delmas, B. Wuensche & J. James (Eds.), Proceedings of the Image and Vision Computing New Zealand (IVCNZ) Conference. (pp. 305-310). Auckland, New Zealand: IVCNZ. [Full Paper]

Khan, N. Y., McCane, B., & Wyvill, G. (2011). SIFT and SURF performance evaluation against various image deformations on benchmark dataset. Proceedings of the International Conference on Digital Image Computing Techniques and Applications (DICTA). (pp. 501-506). IEEE. doi: 10.1109/DICTA.2011.90

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