Brendan McCane, Our people, Department of Computer Science

Owheo Building, Room G.31
Tel +64 3 479 8588
Email brendan.mccane@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

Publications

Liu, J., McCane, B., & Mills, S. (2024). Learning to explore by reinforcement over high-level options. Machine Vision & Applications, 35, 6. doi: 10.1007/s00138-023-01492-1

Xu, H., Szymanski, L., & McCane, B. (2023). VASE: Variational assorted surprise exploration for reinforcement learning. IEEE Transactions on Neural Networks & Learning Systems, 34(3), 1243-1252. doi: 10.1109/TNNLS.2021.3105140

Liu, J., Mills, S., & McCane, B. (2022). RocNet: Recursive octree network for efficient 3D processing. Computer Vision & Image Understanding, 224, 103555. doi: 10.1016/j.cviu.2022.103555

Bennani, H., McCane, B., & Cornwall, J. (2022). Three-dimensional reconstruction of in vivo human lumbar spine from biplanar radiographs. Computerized Medical Imaging & Graphics, 96, 102011. doi: 10.1016/j.compmedimag.2021.102011

Szymanski, L., McCane, B., & Atkinson, C. (2022). Conceptual complexity of neural networks. Neurocomputing, 469, 52-64. doi: 10.1016/j.neucom.2021.10.063

Ott, C., McCane, B., & Meek, N. (2021). Mastery learning in CS1: An invitation to procrastinate?: Reflecting on six years of mastery learning. Proceedings of the 26th ACM Conference on Innovation and Technology in Computer Science Education (ITiCSE) (Vol. 1). (pp. 18-24). New York, NY: ACM. doi: 10.1145/3430665.3456321

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2021). Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. Neurocomputing, 428, 291-307. doi: 10.1016/j.neucom.2020.11.050

Liu, J., Mills, S., & McCane, B. (2020). RocNet: Recursive octree network for efficient 3D deep representation. Proceedings of the International Conference on 3D Vision (3DV). 1, (pp. 414-422). doi: 10.1109/3DV50981.2020.00051

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2020). CoCoNet: A collaborative convolutional network applied to fine-grained bird species classification. Proceedings of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ51579.2020.9290677

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2020). PProCRC: Probabilistic collaboration of image patches for fine-grained classification. Proceedings of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ51579.2020.9290537

Liu, J., Mills, S., & McCane, B. (2020). Variational autoencoder for 3D voxel compression. Proceedings of the 35th International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ51579.2020.9290656

Zhang, S., McCane, B., Neo, P. S.-H., Shadli, S. M., & McNaughton, N. (2020). Trait depressivity prediction with EEG signals via LSBoost. Proceedings of the International Joint Conference on Neural Networks (IJCNN). (pp. 1-8). IEEE. doi: 10.1109/IJCNN48605.2020.9207020

Liu, J., Mills, S., & McCane, B. (2020). RocNet: Recursive octree network for efficient 3D deep representation. arXiv. Retrieved from https://arxiv.org/abs/2008.03875

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2020). Distance Metric Learned Collaborative Representation Classifier (DML-CRC). IEEE Letters of the Computer Society, 3(2), 34-37. doi: 10.1109/LOCS.2020.2997647

Lowe, G., McCane, B., Sutherland, M., Waas, J., Schaefer, A., Cox, N., & Stewart, M. (2020). Automated collection and analysis of infrared thermograms for measuring eye and cheek temperatures in calves. Animals, 10(2), 292. doi: 10.3390/ani10020292

Bennani, H., & McCane, B. (2020). Three-dimensional (3D) reconstruction of dried vertebrae from bi-planar radiographs. Proceedings of the 2019 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ48456.2019.8961014

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2019). PProCRC: Probabilistic collaboration of image patches. arXiv. Retrieved from https://arxiv.org/abs/1903.09123

McCane, B. (2019, October). The rise of the machines. University of Otago, Dunedin, New Zealand. [Inaugural Professorial Lecture].

Wang, Y., McCane, B., McNaughton, N., Huang, Z., Shadli, S., & Neo, P. (2019). AnxietyDecoder: An EEG-based anxiety predictor using a 3-D convolutional neural network. Proceedings of the International Joint Conference on Neural Networks (IJCNN). 19344. IEEE. doi: 10.1109/IJCNN.2019.8851782

Szymanski, L., McCane, B., & Atkinson, C. (2019). Switched linear projections and inactive state sensitivity for deep neural network interpretability. arXiv. Retrieved from https://arxiv.org/abs/1909.11275

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2019). Distance metric learned collaborative representation classifier. arXiv. Retrieved from https://arxiv.org/abs/1905.01168

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2019). Fine-grained collaborative K-means clustering. Proceedings of the 2018 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ.2018.8634796

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2019). CoCoNet: A Collaborative Convolutional Network. arXiv. Retrieved from https://arxiv.org/abs/1901.09886

Chakraborty, T., & McCane, B. (2018). Collaborative learning of small fine-grained datasets. Proceedings of the 11th Annual Dodd-Walls Centre Symposium. (pp. 46). Retrieved from http://www.doddwalls.ac.nz/

Mesbah, R., & McCane, B. (2018). AutoMarkov dilated conditional adversarial networks for medical image semantic segmentation. Proceedings of the 11th Annual Dodd-Walls Centre Symposium. (pp. 43). Retrieved from http://www.doddwalls.ac.nz/

McCane, B. (2018). Simultaneously learning environment and motor-control. Proceedings of the Australasian Conference on Robotics and Automation (ACRA). Retrieved from https://ssl.linklings.net/conferences/acra/acra2018_proceedings/views/at_a_glance.html

Xu, H., McCane, B., & Szymanski, L. (2018). Twin bounded large margin distribution machine. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 718-729). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_64

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2018). Pseudo-rehearsal: Achieving deep reinforcement learning without catastrophic forgetting. arXiv. 8p. Retrieved from https://arxiv.org/abs/1812.02464

Ott, C., McCane, B., & Meek, N. (2018). Five years of Mastery Learning: What did we learn? Proceedings of the 18th Koli Calling International Conference on Computing Education Research. 33. doi: 10.1145/3279720.3279752

Wang, Y., Huang, Z., McCane, B., & Neo, P. (2018). EmotioNet: A 3-D convolutional neural network for EEG-based emotion recognition. Proceedings of the International Joint Conference on Neural Networks (IJCNN). IEEE. doi: 10.1109/IJCNN.2018.8489715

Atkinson, C., McCane, B., & Szymanski, L. (2018). Increasing the accuracy of convolutional neural networks with progressive reinitialisation. Proceedings of the 2017 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/IVCNZ.2017.8402457

McCane, B., & Szymanski, L. (2018). Efficiency of deep networks for radially symmetric functions. Neurocomputing, 313, 119-124. 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

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2018). LOOP Descriptor: Local Optimal Oriented Pattern. IEEE Signal Processing Letters, 25(5), 635-639. doi: 10.1109/LSP.2018.2817176

Atkinson, C., McCane, B., Szymanski, L., & Robins, A. (2018). Pseudo-recursal: Solving the catastrophic forgetting problem in deep neural networks (v2). arXiv. Retrieved from https://arxiv.org/abs/1802.03875

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2017). LOOP descriptor: Local optimal oriented pattern. arXiv. Retrieved from https://arxiv.org/abs/1710.09317

Chakraborti, T., McCane, B., Mills, S., & Pal, U. (2017). A generalised formulation for collaborative representation of image patches (GP-CRC). In T. K. Kim, S. Zafeiriou, G. Brostow & K. Mikolajczyk (Eds.), Proceedings of the British Machine Vision Conference. (pp. 1-11). BMVA Press. doi: 10.5244/C.31.65

Atkinson, C., McCane, B., & Szymanski, L. (2017, December). Increasing the accuracy of convolutional neural networks with progressive reinitialisation. Verbal presentation at the Electronics New Zealand Conference (ENZCon), Christchurch, New Zealand.

Mesbah, R., McCane, B., Mills, S., & Robins, A. (2017, November). Improving spatial context in CNNs for semantic medical image segmentation. Poster session presented at the 4th Asian Conference on Pattern Recognition, Nanjing, China.

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., & 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

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

Chakraborty, T., McCane, B., Mills, S., & Pal, U. (2017). Collaborative representation based fine-grained species recognition. In D. Bailey, G. Sen Gupta & S. Marsland (Eds.), Proceedings of the 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). (pp. 42-47). IEEE. doi: 10.1109/ivcnz.2016.7804421

Mesbah, R., McCane, B., & Mills, S. (2017). Deep convolutional encoder-decoder for myelin and axon segmentation. Proceedings of the 2016 International Conference on Image and Vision Computing New Zealand (IVCNZ). IEEE. doi: 10.1109/ivcnz.2016.7804455

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

Fu, X., McCane, B., Mills, S., Albert, M., & Szymanski, L. (2016). Auto-JacoBin: Auto-encoder Jacobian binary hashing. arXiv. 17p. Retrieved from https://arxiv.org/abs/1602.08127

Mesbah, R., McCane, B., & Mills, S. (2016). Myelin and axon segmentation with convolutional neural networks. In M. Andersen & A. Deb (Eds.), Proceedings of the 9th Annual Dodd-Walls Symposium. (pp. 7). Retrieved from http://www.doddwalls.ac.nz/wawcs0138456/Symposium.html

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

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

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

Ruckstuhl, K., Wright, J., & McCane, B. (2015, May). Kia whakatika ā mua: Building an online Māori language calculator. Verbal presentation at the Hui Poutama: Māori Research Symposium, Dunedin, New Zealand.

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: 12th Asian Conference on Computer Vision 2014, revised selected papers, part 1: Lecture notes in computer science (Vol. 9003). (pp. 162-177). Cham, Switzerland: Springer. doi: 10.1007/978-3-319-16865-4_11

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

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

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

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

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

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

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

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

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

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

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

Bennani, H., Cornwall, J., & McCane, B. (2013). Computer modelling of vertebral body shape. Clinical Anatomy, 26, (pp. 642). doi: 10.1002/ca.22235

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

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

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 27^th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 492-496). New York: ACM. [Full Paper]

McCane, B. (2012). The linear space of all images smaller than 32x32. In B. McCane, S. Mills & J. D. Deng (Eds.), Proceedings of the 27^th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 156-161). 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 27^th Image and Vision Computing New Zealand Conference (IVCNZ). (pp. 67-72). New York: ACM. doi: 10.1145/2425836.2425852

McCane, B., Mills, S., & Deng, J. D. (Eds.). (2012). Proceedings of the 27^th Image and Vision Computing New Zealand Conference (IVCNZ). New York: ACM. 547p.

Khan, N., McCane, B., & Mills, S. (2012). Vision based indoor scene localization via smart phone [Demonstration]. Proceedings of the 13th International Conference of the NZ Chapter of the ACM's Special Interest Group on Human-Computer Interaction (CHINZ). (pp. 88). New York: ACM. doi: 10.1145/2379256.2379272

Khan, N., & McCane, B. (2012). Smartphone application for indoor scene localization. Proceedings of the 14th Association for Computing Machinery (ACM) Special Interest Group on Accessible Computing (SIGACCESS) International Conference on Computers and Accessibility (ASSETS). (pp. 201-202). New York: ACM. doi: 10.1145/2384916.2384953

Khan, N., McCane, B., & Mills, S. (2012). Vision based indoor scene localization via smart phone. Proceedings of the 13th Annual Conference of the NZ ACM Special Interest Group on Human-Computer Interaction (CHINZ): @ The Interface Between Disciplines. Retrieved from http://www.chinz2012.otago.ac.nz/programme.html

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

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

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

Szymanski, L., & McCane, B. (2011). Visualising kernel spaces. In P. Delmas, B. Wuensche & J. James (Eds.), Proceedings of the Image and Vision Computing New Zealand (IVCNZ) Conference. (pp. 449-452). Auckland, New Zealand: IVCNZ. [Full Paper]

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). Homography based visual bag of world model for scene matching in indoor environments. In P. Delmas, B. Wuensche & J. James (Eds.), Proceedings of the Image and Vision Computing New Zealand (IVCNZ) Conference. (pp. 70-75). Auckland, New Zealand: IVCNZ. [Full Paper]

Wang, R., McCane, B., & Fang, B. (2011). RBC image segmentation based on shape reconstruction and multi-scale surface fitting. Proceedings of the 3rd International Symposium on Information Science and Engineering (ISISE). (pp. 586-589). IEEE. doi: 10.1109/ISISE.2010.159

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

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

Navarro Newball, A. A., Wyvill, G., & McCane, B. (2009). Object interaction using tabulated spheres subsets. In W. Tang & J. Collomosse (Eds.), Proceedings of the Theory and Practice of Computer Graphics (TPCG) Conference. (pp. 57-60). doi: 10.2312/LocalChapterEvents/TPCG/TPCG09/057-060

Vromen, J., & McCane, B. (2009). Red blood cell segmentation from SEM images. In D. Bailey (Ed.), Proceedings of the 24th International Conference Image and Vision Computing New Zealand. (pp. 44-49). IEEE. doi: 10.1109/IVCNZ.2009.5378364

Kukenys, I., McCane, B., & Neumegen, T. (2009, September). Training support vector machines on large sets of image data. Poster session presented at the Ninth Asian Conference on Computer Vision, Xi' an, China.

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

Szymanski, L., McCane, B., & Rountree, N. (2008). Maximum margin perceptron: Towards optimal and deterministic neural network architectures. Proceedings of the New Zealand Computer Science Research Student Conference. (pp. 266-269). [Full Paper]

Rezajooi, K., Moumene, M., McCane, B., & Casey, A. T. H. (2008). Potential adverse effects of incorrect centre of rotation placement in cervical disc arthroplasty devices. British Journal of Neurosurgery. 22(4), (pp. 470-471). doi: 10.1080/02688690802349590

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

Kukenys, I., & McCane, B. (2008). Classifier cascades for support vector machines. Proceedings of the Image and Vision Computing New Zealand 23rd International Conference. IEEE. doi: 10.1109/IVCNZ.2008.4762088

Birchall, S., Abbott, J. H., & McCane, B. (2008). Finite centre of rotation of lumbar spine segmental motion in the sagittal plane. Proceedings of the New Zealand Society of Physiotherapists Conference. (pp. 26). Wellington, New Zealand: NZSP. [Abstract]

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

Abbott, J., & McCane, B. (2007). Development and validation of the ClaritySMART spinal motion analysis research technology. Physiotherapy. 93(Suppl. 1), (pp. S47). [Abstract]

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

McCane, B. (Ed.). (2005). Proceedings of the Image and Vision Computing New Zealand 2005. Dunedin, New Zealand: Department of Computer Science, University of Otago. 512p. Retrieved from http://www.cs.otago.ac.nz/ivcnz05/programme.html

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.

Mackie, J., & McCane, B. (2004). Finger detection with decision trees. In D. Pairman, H. North & S. McNeill (Eds.), Proceedings of the Image and Vision Computing New Zealand. (pp. 399-403). Lincoln, New Zealand: Landcare Research Ltd. [Full Paper]

Wood, A., McCane, B., & King, S. A. (2004). Ray tracing arbitrary objects on the GPU. In D. Pairman, H. North & S. McNeill (Eds.), Proceedings of the Image and Vision Computing New Zealand. (pp. 327-332). Lincoln, New Zealand: Landcare Research Ltd. [Full Paper]

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

Abbott, J. H., McCane, B., & King, T. (2003). ClaritySMART - Spinal Motion Analysis Research Technology (Version http://claritysmart.otago.ac.nz/) [Computer Software]. Dunedin, New Zealand: University of Otago. Retrieved from http://claritysmart.otago.ac.nz/

McCane, B., & Novins, K. L. (2003). On training cascade face detectors. In D. G. Bailey (Ed.), Proceedings of Image and Vision Computing New Zealand 2003. (pp. 239-244). Palmerston North: Institute of Information Sciences and Technology, Massey University. [Full Paper]

King, S., Knott, A., & McCane, B. (2003). Language-driven nonverbal communication in a bilingual conversational agent. Proceedings of the 16th International Conference on Computer Animation and Social Agents (CASA'03). (pp. 17-22). California: IEEE Computer Society. [Full Paper]

Caelli, T., & McCane, B. (2003). Components analysis of hidden Markov models in computer vision. Proceedings of the 12th International Conference on Image Analysis and Processing. (pp. 510-515). California: IEEE. [Full Paper]

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

Vittrup, M., Sorensen, M., & McCane, B. (2002). Pose estimation by applied numerical techniques. In D. Kenwright (Ed.), Proceedings of Image and Vision Computing New Zealand 2002 (IVCNZ2002). Auckland, New Zealand: IVCNZ. [Full Paper]

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

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. (Eds.). (2001). Proceedings of Image and Vision Computing New Zealand 2001. Dunedin: Wickliffe Ltd. 393p.

Pengelly, J., McCane, B., & Nicoll, J. (2001). Computerized evaluation of mammography phantom image quality. In K. Novins & B. McCane (Eds.), Proceedings of Image and Vision Computing New Zealand. (pp. 345-350). Dunedin: Wickliffe Ltd. [Full Paper]

Da, L., McCane, B., & Wei, P. (2001). A topographic segmentation method for merged character images. In K. Novins & B. McCane (Eds.), Image and Vision Computing New Zealand. (pp. 207-212). Dunedin: Wickliffe Ltd. [Full Paper]

McCane, B. (2001). Real Time Deformable Template Matching. In A. Bobick, D. Terzopoulos & J. Davis (Eds.), 'Technical Sketches', International Conference on Computer Vision and Pattern Recognition. CD ROM. Hawaii. [Full Paper]

Da, L., McCane, B., & Wei, P. (2001). Character preclassification based on fuzzy typographical analysis. Proceedings of 6th International Conference on Document Analysis and Recognition. Seattle, USA. [Full Paper]

Novins, K. L., & McCane, B. (2000). Teaching computer vision using primary source material. In K. Bowyer (Ed.), Proceedings of the IEEE Workshop on Undergraduate Education and Image Computation. Los Alamitos: IEEE Computer Society. [Full Paper]

McCane, B. (2000). Snakes and spiders. In A. Sanfeliu, J. J. Villanueva, M. Vanrell, R. Alquezar, J. O. Eklundh & Y. Aloimonous (Eds.), Proceedings of the International Conference on Pattern Recognition. 1, (pp. 652-655). Los Alamitos, California: IEEE Computer Society. [Full Paper]

Galvin, B., McCane, B., & Novins, K. L. (1999). OSCAR: Object Segmentation using Correspondence and Relaxation. Proceedings of the Second International Conference on 3-D Digital Imaging and Modeling (3DIM) '99. Ottawa. [Full Paper]

Erickson, A., McCane, B., & Novins, K. L. (1999). Face and Eye Tracking. Proceedings of Image and Vision Computing New Zealand 1999, IVCNZ. (pp. 139-144). Christchurch: Landcare Research New Zealand. [Full Paper]

Galvin, B., McCane, B., & Novins, K. L. (1999). Virtual Snakes for Occlusion Analysis. Proceedings of the 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR'99. 2, (pp. 294-299). IEEE Computer Society Press. [Full Paper]

Galvin, B., McCane, B., & Novins, K. L. (1999). Robust Feature Tracking. Proceedings of Fifth International/National Biennial Conference on Digital Image Computing, Techniques and Applications. (pp. 232-236). Perth. [Full Paper]

Mason, D. R. R., McCane, B., & Novins, K. L. (1999). Generating Motion Fields of Complex Scenes. Proceedings of Computer Graphics International 1999, CGI '99. (pp. 65-69). Canmore: IEEE Computer Society Press. [Full Paper]

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., Novins, K. L., & Galvin, B. (1998). On the Evaluation of Optical Flow Algorithms. Proceedings of Fifth International Conference on Control, Automation, Robotics and Vision. (pp. 1563-1567). Singapore. [Full Paper]

Galvin, B. B., Novins, K. L., & McCane, B. (1998). Motion-Based Segmentation Using Active Contours. Proceedings of Image and Vision Computing New Zealand (IVCNZ)'98. (pp. 346-351). Auckland, New Zealand: University of Auckland CITR. [Full Paper]

Galvin, B., McCane, B., Novins, K. L., Mason, D. R., & Mills, S. J. (1998). Recovering Motion Fields: An Analysis of Eight Optical Flow Algorithms. Proceedings of the British Machine Vision Conference (BMVC)'98. (pp. 195-204). Southhampton. [Full Paper]

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.

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).

Galvin, B., & McCane, B. (1997). On the creation of dense depth fields from long image sequences. Digital Image Computing: Techniques and Applications. (pp. 373-378). Auckland: Massey University. [Full Paper]

McCane, B. (1997). On the evaluation of image segmentation algorithms. Digital Image Computing: Techniques and Applications. (pp. 455-460). Auckland: Massey University. [Full Paper]

McCane, B., & Caelli, T. (1997). Multi-scale adaptive segmentation using edge and region based attributes. In L. C. Jain (Ed.), First International Conference on Knowledge-based Intelligent Electronic Systems. (pp. 72-80). Adelaide. [Full Paper]

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