Professor Brendan McCane

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

Chapter in Book - Research

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

Chapter in Book - Research

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.

Chapter in Book - Research

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.

Chapter in Book - Research

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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.

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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

Journal - Research Article

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.

Journal - Research Article

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).

Journal - Research Article

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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. 19344. IEEE. doi: 10.1109/IJCNN.2019.8851782

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Published proceedings: Full paper