Main research areas:

• Computational Neuroscience,
• Computational Intelligence, Neural Networks,

Recent projects including PhD projects

Computer vision, image processing and neural networks

• Image Segmentation: A Paradigm Shift to Image Processing [4,5]
  PhD research by SM Abdullah.
• Towards Augmented Reality: Fusion of Real-Time TRUS and MRI for Prostate Cancer Surgery. [3]
  PhD research project by Mohammad Ali Jan Ghasab.
• Towards Sustainable Cloud Computing [1].
  PhD project by MD Anit Khan.
• Semi-Supervised Classification for oil reservoir
  PhD research project by Yanan Li.
• Semi-supervised learning in convolutional neural networks applying "sleep-wake" cycles [2].
  PhD research project by Mikel Elkano.
• Mastering Atari Games with Deep Reinforcement Learning
  2017 Honours Project by Luoxi Pan
• An automated simple copy task test and scoring system: Current techniques, methods and technologies
  PhD research project by William Schmidt.
• Incremental Self-Organizing Maps: iSOM [15,6]
• Rotation invariant categorization of visual objects: [16,24]
• Iris Localization; [7,9,13,14,19]
  Completed PhD project
• Elastic nets and Generative Topographic Maps: [17,18]
• Automatic inspection of sewer pipes: [20]

Computational Neuroscience: Modelling integration of cross-modal sensory information in the brain

This project from the area of computational neuroscience is conducted in collaboration with Professor Lennart Gustafsson from Luleå University of Technology, Sweden.
Multimodal integration of sensory information has clear advantages for survival: events that can be sensed in more than one modality are detected more quickly and accurately, and if the sensory information is corrupted by noise the classification of the event is more robust in multimodal percepts than in the unisensory information. In our recent papers [19,20,21,28,29,35,44] we have introduced novel Artificial Cortical Networks aka Multimodal Self-Organizing Networks, consisting of several interconnected Self-Organizing modules that model integration of multimodal data. See also [43]. In particular, we investigate a possible ways in which an audiovisual representation of language components is mapped onto a human cortex.

Human Face Recognition and Tracking algorithms

In this doctoral project [114] Nathan Faggian investigated face detection and recognition algorithms [25,30,36,37,46].
The project was conducted in collaboration with the Industry Partner, Clarity Visual Intelligence  http://www.clarityvi.com/  and was funded by Australian Research Council Linkage grant.

Automatic fruit grading

In these doctoral projects [116] Sudanthi Wijewickrema
 http://www.csse.monash.edu.au/~snw/ 
and Abdul Malik Khan [113] investigated computer vision algorithms suitable for application in automatic fruit sorters.
The project was conducted in collaboration with the Industry Partner, Colour Vision Systems, Pty Ltd
 http://www.cvs.cx/  and was funded by Australian Research Council Linkage grant. Related publications: [24,31,32,38,39,40,41,48,49] and [26,27]

Neural Networks and Minimum Message Length (MML)

In these two doctoral projects Daniel Schmidt [115]
and Enes Makalic [117]
investigated application of Minimum Message Length concept to optimization of neural networks and their applications [45,52,55,60].

Modelling Autistic Learning

This project from the area of computational neuroscience is conducted in collaboration with A/Prof. Lennart Gustafsson from Luleå  University of Technology, Sweden.
Autism is a developmental disorder in which attention shifting is known to be restricted. Using an artificial neural network model we study how detailed learning in narrow fields develops when attention shifting between different sources of stimuli is restricted by familiarity preference. Our model is based on modified Self-Organizing Maps (SOM) supported by the attention shift mechanism. Our attention shift model based on growing familiarity of the artificial neural network to a specific source of stimuli is an original and novel achievement of this project.
The novelty seeking and the attention shifting restricted by familiarity preference learning modes are investigated for stimuli of low and high dimensionality which require different techniques to visualise feature maps. To make learning more biologically plausible we project the n-dimensional stimuli onto a unity (n+1)-dimensional hyper-sphere. The distance between a stimulus and a weight vector can now be simply measured by the post-synaptic activities. The modified "dot-product" learning law that keeps evolving weights on the surface of the hyper-sphere has been employed. The idea of projecting stimuli on the sphere surface is another original achievement of this project. One of the recently investigated problem are the theoretical foundations of an early intervention that will help to understand the principle of training to mould an autistic behaviour into a normal one.
The results have presented in the following publications [50,51,56,57,59,63,96,97].

Hybrid analog-digital biologically-motivated neural networks

This is a completed PhD project of Dr Murray Mount from the area of computational neuroscience.
The objective was to investigate, design, simulate, and possibly build in a VLSI technology a hybrid analog-digital biologically-motivated three-dimensional neural processor. Such a neural processor can be used as a computational tool in problems involving real-time perception, control and adaptation, and in simulation of biological mechanisms found in neural systems such as those of the cortex.
This work resulted in a number of conference presentations and publications [119,75,87,92,93]

Advanced Algorithms for Ultrasonic Imaging

Participants: Dr N. Bhattacharjee completed PhD thesis [118], Charles Greif, Robert Prain - doctoral candidate, Suhardi Tjoa - completed Masters thesis and Dr Grant Hampson - completed PhD thesis [121].
This project spans areas of signal and image processing with embedded hardware implementation of resulting computational algorithms. The work started in 1994 as a doctoral project [121] and resulted in prototype an ultrasonic imaging system built around 1998. Original results related to implementation of the phase shift through the digital rotation of ultrasonic signal converted into a complex-number domain have been presented in a number of publications [54,65,98,99,82,85,86].

Interpretation of a class of ophthalmological images

(with Dr J. F. Boyce from the Image Processing Group, King's College London and the Department of Ophthalmology, St. Thomas' Hospital London). In this project we investigate theoretical aspects and develop relevant image segmentation algorithms to be used as a part of a practical information system of interpretation Posterior Capsular Opacification (PCO) images. PCO images are taken to monitor the state of a patient's vision after the implantation of the intra-ocular lenses during the cataract operation. The image interpretation system provides a clinician with a quantitative measure of opacification that can occur after the surgery. This opacification gradually impairs the patient's vision annulling the benefits of implantation of artificial lenses. Recent research effort is directed towards application of partial differential equations in image enhancement and segmentation.
Results of investigations have been presented in [62,66,67,68,79,84,73,72,76] and [80,81,95,100,101,102,103,104,105,106]. The project was funded by ARC small grants (1997, 1998) and by the British Engineering and Physical Sciences Research Council (EPSRC) Visiting Fellowship Grant (1997).

Wavelet transform Lattice Quantisation

with application in image coding (with Dr M. Shnaider). In this project we investigate theoretical aspects and applications of two-dimensional wavelet transform and lattice quantisation in image coding. The combination of biorthogonal wavelet transform and lattice vector quantisation of the resulting wavelet coefficients has proven to be a powerful technique for image compression. Lattice quantisation based on the D-lattices offers superior speed of compression with near optimal compression ratio if an appropriate method of bit allocation is used. Some results of the investigations have been presented in [61,70,77,120,88,89,90,94,108,109].

Publications

[18a]

M. A. Khan, A. Paplinski, A. M. Khan, M. Murshed, and R. Buyya, "Dynamic virtual machine consolidation algorithms for energy-efficient cloud resource management: A review," in Sustainable Cloud and Energy Services: Principles and Practice (W. Rivera, ed.), pp. 135-165, Springer, 2018. Springer.

[17a]

M.  Elkano, H.  Bustince, A. P. Paplinski, "A preliminary approach to semi-supervised learning applying sleep-wake cycles," in ICONIP, LNCS, vol. 10637, pp. 466-474, Springer, 2017. pdf.

[17b]

M. A. J. Ghasab, A. P. Paplinski, J. M. Betts, H. M. Reynolds, and A. Haworth, "Automatic 3D modelling for prostate cancer brachytherapy," in ICIP, pp. 1-6, IEEE, 2017. pdf.

[17c]

S. Abdullah, P. Tischer, S. Wijewickrema, and A. P. Papliński, "Parameter-free hierarchical natural image segmentation," in VCIP, pp. 1-6, IEEE, 2017. pdf.

[3]

S. Abdullah, P. Tischer, S. Wijewickrema, and A. P. Papliński, "Hierarchical mutual nearest neighbour image segmentation," in DICTA, pp. 1-6, IEEE, 2016. pdf.

[4]

A. P. Papliński, "Self-organization on a sphere with application to topological ordering of Chinese characters," in ICONIP, LNCS, vol. 9950, pp. 452-459, Springer, 2016. pdf.

[5]

N. Mahadeo, G. Haffari, and A. P. Papliński, "Predicting segmentation errors in an iris recognition system," in Proc. ICB, pp. 23-30, IEEE, 2015. pdf.

[6]

A. P. Paplinski and W. M. Mount, "Transferring knowledge between learning systems," in Proc. ISDA, pp. 119-123, IEEE, 2014. pdf.

[7]

N. Mahadeo, A. P. Papliński, and S. Ray, "Automated selection of optimal frames in nir iris video," in Proc. CVPR, pp. 48-55, IEEE, 2014. pdf.

[8]

T. Jantvik, L. Gustafsson, and A. P. Papliński, "A self-organized artificial neural network architecture that generates the McGurk effect," in Proc. IJCNN, pp. 3974-3980, 2014. pdf.

[9]

A. P. Papliński, "The angular integral of the Radon transform (aniRT) as a feature vector in categorization of visual objects," in ISNN, LNCS, vol. 7951, pp. 523-531, Springer, 2013. pdf.

[10]

A. P. Papliński and W. M. Mount, "Bimodal incremental self-organizing network (BiSON) with application to learning Chinese characters," in LNCS, vol. 8226, pp. 121-128, Springer, 2013. pdf.

[11]

N. Mahadeo, A. P. Papliński, and S. Ray, "Robust video based iris segmentation in less constrained environments," in Proc. DICTA, pp. 372-379, IEEE, 2013. pdf.

[12]

N. Mahadeo, A. P. Papliński, and S. Ray, "Automated selection of optimal frames in nir iris video," in Proc. DICTA, pp. 380-387, IEEE, 2013. pdf.

[13]

A. P. Papliński, "Incremental self-organizing map (iSOM) in categorization of visual objects," in ICONIP, LNCS, vol. 7664, pp. 125-132, Springer, 2012. pdf.

[14]

A. P. Papliński, "Rotation invariant categorization of colour images using Radon transform," in Proc. WCCI-IJCNN, pp. 1408-1413, IEEE, 2012. pdf.

[15]

D. Cohen and A. P. Papliński, "The elastic net as visual category representation: Visualisation and classification," in ICONIP, LNCS, vol. 7664, pp. 133-140, Springer, 2012. pdf.

[16]

D. Cohen and A. P. Papliński, "A comparative evaluation of the Generative Topographic Mapping and the Elastic Net for the formation of Ocular Dominance stripes," in Proc. WCCI-IJCNN, pp. 3237-3244, IEEE, 2012. pdf.

[17]

N. Mahadeo, A. P. Papliński, and S. Ray, "Model-based pupil and iris localization," in Proc. WCCI-IJCNN, pp. 1427-1433, IEEE, 2012. pdf.

[18]

H. Ganegedara, D. Alahakoon, J. Mashford, A. Paplinski, K. Muller, and T. Deserno, "Self organising map based region of interest labelling for automated defect identification in large sewer pipe image collections," in Proc. WCCI-IJCNN, pp. 858-865, IEEE, 2012. pdf.

[19]

A. P. Papliński, L. Gustafsson, and W. M. Mount, "A recurrent multimodal network for binding written words and sensory-based semantics into concepts," in LNCS (B.-L. Lu, L. Zhang, and J. Kwok, eds.), vol. 7062, pp. 413-422, Springer, 2011.

[20]

T. Jantvik, L. Gustafsson, and A. P. Papliński, "A self-organized artificial neural network architecture for sensory integration with applications to letter-phoneme integration," Neural Computation, vol. 23, pp. 2101-2139, 2011.

[21]

A. P. Papliński, L. Gustafsson, and W. M. Mount, "A model of binding concepts to spoken names," Aust. Journal of Intelligent Information Processing Systems, vol. 11, no. 2, pp. 1-5, 2010.

[22]

A. P. Papliński, "Rotation invariant categorization of visual objects using Radon transform and self-organizing modules," in LNCS, vol. 6444, pp. 360-366, Springer, 2010.

[23]

A. P. Papliński, "Multivariable ARMA systems - making a polynomial matrix proper," Tech. Rep. 2009/240, Clayton School of IT, Monash University, Australia, May 2009. pdf.

[24]

S. N. R. Wijewickrema, A. Papliński, and C. E. Esson, "A novel approach to orthogonal distance least squares fitting of general conics," Proc. Int. Conf. Comp. Vision Theory and Appl., pp. 138-145, Feb. 2009. pdf.

[25]

N. Faggian, A. Paplinski, and J. Sherrah, "3D morphable model fitting from multiple views," in Proc. 8th IEEE Int. Conf. Automatic Face and Gesture Recognition, (Amsterdam), pp. 1-6, Sept. 2008. pdf.

[26]

A. M. Khan and A. P. Papliński, "Blemish detection in citrus fruits,," in Proc. SPIT-IEEE Colloquium Intern. Conf., pp. 203-211, Feb. 2008.

[27]

A. M. Khan and A. P. Papliński, "Blemish detection in citrus fruits,," in Proc. 7th In. Conf. Appl. Comp. Sci., (Wisconsin, USA), pp. 262-271, Apr. 2008.

[28]

L. Gustafsson, T. Jantvik, and A. P. Papliński, "A multimodal self-organizing network for sensory integration of letters and phonemes," in Proc. IASTED Int. Conf. Artif. Intell. Soft Comp., (Palma De Mallorca, Spain), pp. 25-31, Aug. 2007. pdf.

[29]

S. Chou, A. P. Paplinski, and L. Gustafsson, "Speaker-dependent bimodal integration of Chinese phonemes and letters using multimodal self-organizing networks," in Proc. Int. Joint Conf. Neural Networks, (Orlando, Florida), pp. 248-253, Aug. 2007. pdf.

[30]

N. Faggian, A. Paplinski, and J. Sherrah, "3D morphable model parameter estimation," in Lect. Notes in Artif. Intell., vol. 4304, pp. 519-528, Springer, 2006.

[31]

S. N. R. Wijewickrema, A. P. Papliński, and C. E. Esson, "Extraction and mapping of texture for spherical objects on conveyors," WSEAS Trans. Signal Proc., vol. 2, pp. 1108-1115, Aug. 2006.

[32]

S. N. R. Wijewickrema, A. P. Papliński, and C. E. Esson, "Determination of tangency for quadric reconstruction," WSEAS Trans. Signal Proc., vol. 2, pp. 1100-1107, Aug. 2006.

[33]

N. Bhattacharjee and A. P. Papliński, "Ultrasonic imaging based on synthetic ellipsoidal wavefronts," WSEAS Trans. Signal Proc., vol. 2, pp. 1492-1499, Nov. 2006.

[34]

A. P. Papliński and L. Gustafsson, "Feedback in multimodal self-organizing networks enhances perception of corrupted stimuli," in Lect. Notes in Artif. Intell., vol. 4304, pp. 19-28, Springer, 2006. pdf.

[35]

L. Gustafsson and A. P. Papliński, "Bimodal integration of phonemes and letters: an application of multimodal self-organizing networks," in Proc. Int. Joint Conf. Neural Networks, (Vancouver, Canada), pp. 704-710, July 2006.

[36]

N. Faggian, A. Paplinski, and J. Sherrah, "Active appearance models for automatic fitting of 3D morphable models," in Proc. IEEE Int. Conf. Advanced Video and Signal based Surveillance, (Sydney, Australia), pp. 90-95, IEEE Computer Society, Nov. 2006.

[37]

N. Faggian, A. P. Papliński, and T.-J. Chin, "Face recognition from video using active appearance model segmentation," in Proc. 18th Int. Conf. Pattern Recognition, (Hong Kong), pp. 287-290, Aug. 2006.

[38]

S. N. R. Wijewickrema, A. P. Papliński, and C. E. Esson, "Tangency of conics and quadrics," in 6th WSEAS Int. Conf. on Signal Processing, Computational Geometry and Artificial Vision (ISCGAV'06), (Crete Island, Greece), pp. 21-29, Aug. 2006.

[39]

S. N. R. Wijewickrema, A. P. Papliński, and C. E. Esson, "Texture unwrapping for spherical objects on conveyors," in 6th WSEAS Int. Conf. on Signal Processing, Computational Geometry and Artificial Vision (ISCGAV'06), (Crete Island, Greece), pp. 141-146, Aug. 2006.

[40]

S. N. R. Wijewickrema, A. P. Papliński, and C. E. Esson, "Reconstruction of spheres using occluding contours from stereo images," in Proc. 18th Int. Conf. Pattern Recognition, (Hong Kong), pp. 151-154, Aug. 2006.

[41]

S. N. R. Wijewickrema, A. P. Papliński, and C. E. Esson, "Reconstruction of ellipsoids on rollers from stereo images using occluding contours," in Proc. Int. Conf. Computer Vision Theory and Applications, (Setubal, Portugal), Feb. 2006.

[42]

N. Bhattacharjee and A. P. Papliński, "Synthetic ellipsoidal wavefront imaging," in Proc. 5th WSEAS Int. Conf. Circuits, Systems, Electronics, Control and Signal Processing, (Dallas, USA), pp. 300-305, Nov. 2006.

[43]

P. Xu, C.-H. Chang, and A. Papliński, "Self-organizing topological tree for on-line vector quantization and data clustering," IEEE Tran. System, Man and Cybernetics, Part B: Cybernetics, vol. 35, pp. 515-526, June 2005.

[44]

A. P. Papliński and L. Gustafsson, "Multimodal feedforward self-organizing maps," in Lect. Notes in Comp. Sci., vol. 3801, pp. 81-88, Springer, 2005. pdf.

[45]

D. Schmidt, A. P. Papliński, and G. Lowe, "Adaptive control of hydraulic systems with MML inferred RBF networks," in Proc. Int. Conf. Robotics and Automation, (Barcelona, Spain), pp. 2368-2374, April 2005.

[46]

N. Faggian, S. Romdhani, J. Sherrah, and A. Paplinski, "Color active appearance model analysis using a 3D morphable model," in Digital Image Computing: Techniques and Applications, (Cairns, Australia), pp. 59-66, IEEE Computer Society, Dec. 2005.

[47]

L. Gustafsson and A. P. Papliński, "Self-organizing neural network modelling of learning when attention shifting is impaired as in autism and the effects of early intervention," in Proc. XIIth European Conference on Developmental Psychology, (Tenerife, Spain), pp. 201-208, Aug. 2005.

[48]

S. N. R. Wijewickrema and A. P. Papliński, "Generalized Hebbian learning for ellipse fitting," in Proc. 13th Int. Conf. in Central Europe on Computer Graphics, Visualization and Computer Vision, (Plzen, Czech Republic), pp. 11-16, February 2005.

[49]

S. N. R. Wijewickrema and A. P. Papliński, "Principal component analysis for the approximation of a fruit as an ellipse," in Proc. 13th Int. Conf. Central Europe on Computer Graphics, Visualization and Computer Vision, (Plzen, Czech Republic), pp. 1-6, February 2005.

[50]

L. Gustafsson and A. P. Papliński, "Neural network modelling of autism," in Recent developments in autism research (M. F. Casanova, ed.), pp. 100-134, Hauppauge, New York: Nova Science Publishers, Inc., November 2005. pdf.

[51]

L. Gustafsson and A. P. Papliński, "Self-organization of an artificial neural network subjected to attention shift impairments and novelty avoidance: Implications for the development of autism," J. Autism and Developmental Disorder, vol. 34, pp. 189-198, April 2004. pdf.

[52]

E. Makalic, L. Allison, and A. P. Papliński, "MML inference of RBF neural networks for regression," in Proc. Brazilian Symp. Artificial Neural Networks (SBRN), (São Luis do Maranhão, Brazil), pp. 101-108, Sept. 2004.

[53]

A. P. Papliński and L. Gustafsson, "An attempt in modelling early intervention in autism using neural networks," in Proc. Int. Joint Conf. Neural Networks, (Budapest, Hungary), pp. 101-108, July 2004. pdf.

[54]

R. Prain and A. P. Papliński, "A distributed arithmetic online rotator for signal processing applications," in EUROMICRO Symp. Digital System Design: Architectures, Methods and Tools, (Rennes, France), pp. 301-306, September 2004.

[55]

D. Schmidt, A. P. Papliński, and G. Lowe, "On the design of a hydraulically actuated finger for dextrous manipulation," in Proc. Int. Conf. Robotics and Automation, (New Orleans, LA, USA), pp. 3001-3006, May 2004.

[56]

L. Gustafsson and A. P. Papliński, "Preoccupation with a restricted pattern of interest in modelling autistic learning," in Lect. Notes in Artif. Intell. (V. Pallade, R. J. Howlett, and L. Jain, eds.), vol. 2774, Part II, pp. 1122-1129, Springer, 2003. pdf.

[57]

A. P. Papliński and L. Gustafsson, "Detailed learning in narrow fields - towards a neural network model of autism," in Lect. Notes in Comp. Sci. (O. Kaynak, E. Alpaydin, and L. Xu, eds.), vol. 2714, pp. 830-838, Springer, 2003. pdf.

[58]

L. Gustafsson and A. P. Papliński, "Autistic-like detailed learning in a narrow range of stimuli: results from simulations with artificial neural networks restricted by familiarity preference," in Inaugural World Autism Congress 2002, (Melbourne), November 2002.

[59]

A. P. Papliński and L. Gustafsson, "An attempt in modelling autism using self-organizing maps," in Proc. 9th Intern. Conf. Neural Information Processing, (Singapore), pp. 301-304, November 2002. pdf.

[60]

D. Schmidt and A. P. Papliński, "An experiment in neuro-computed torque control of a geared, DC motor driven industrial robot," in Proc. 2nd WSEAS Int. Conf. on Instrumentation, Measurement, Control, Circuits and Systems, (Cancun, Mexico), pp. 429-433, May 2002.

[61]

M. Shnaider and A. P. Papliński, "Still image compression with lattices in the wavelet domain," in Advances in Imaging and Electron Physics. Aspects of Image Processing and Compression, vol. 119, pp. 56-123, Academic Press, 2001.

[62]

A. P. Papliński, "Current improvements in interpretation of posterior capsular opacification images," in Proc. Sixth International Symposium on Signal Processing and its Application, (Kuala Lumpur, Malaysia), pp. 218-221, August 2001. pdf.

[63]

L. Gustafsson and A. P. Papliński, "Attention shift impairments and novelty avoidance - effects of characteristics of autism on the self-organization of an artificial neural network," in Xth European Conference on Developmental Psychology, (Uppsala, Sweden), August 2001.

[64]

A. P. Papliński, N. Bhattacharjee, and C. Greif, "Rotating ultrasonic signal vectors with a word-parallel CORDIC processor," in Proc. EUROMICRO Symposium on Digital System Design: Architectures, Methods and Tools, (Warsaw, Poland), pp. 254-261, September 2001.

[65]

N. Bhattacharjee, A. P. Papliński, and C. Greif, "FPGA implementation of a pipelined word-parallel CORDIC processor for an ultrasonic imaging system," in Proc. 2001 IEEE International Symposium on Intelligent Signal Processing and Communication Systems, (Nashville, Tennessee, USA), pp. 429-433, November 2001.

[66]

A. P. Papliński, "Curvature-driven min/max flow and anisotropic diffusion in image enhancement," in Proc. 2nd Int. Symp. Advanced Concepts for Intelligent Vision Systems (ACIVS'2000), (Baden-Baden, Germany), pp. 41-48, Int. Inst. for Advanced Studies in Systems Research and Cybernetics, August 2000. pdf.

[67]

A. P. Papliński, J. F. Boyce, and S. A. Barman, "Improvements in interpretation of posterior capsular opacification (PCO) images," in Proc. SPIE: Medical Imaging 2000, vol. 3979, (San Diego, California, USA), pp. 951-958, February 2000.

[68]

S. A. Barman, J. F. Boyce, and A. P. Papliński, "Automatic quantification of posterior capsular opacification," in Proc. SPIE: Medical Imaging 2000, vol. 3979, (San Diego, California, USA), pp. 119-128, February 2000.

[69]

B. M. Garner and A. P. Papliński, "An interpretation of the function of the striate cortex," in Proc. SPIE: Medical Imaging 2000, vol. 3981, (San Diego, California, USA), pp. 248-255, February 2000.

[70]

M. Shnaider and A. P. Papliński, "Lattice vector quantization for wavelet based image coding," in Advances in Imaging and Electron Physics, vol. 109, pp. 199-263, Academic Press, 1999.

[71]

M. Shnaider and A. P. Papliński, "Selecting lattices for quantization of wavelet coefficients of images," Optical Engineering, vol. 39, pp. 1327-1337, May 2000.

[72]

A. P. Papliński and J. F. Boyce, "Processing a class of ophthalmological images using an anisotropic diffusion equation," in Proc. 2nd Annual IASTED International Conference on Computer Graphics and Imaging (CGIM'99), (Palm Springs, California, USA), pp. 134-138, October 1999.

[73]

A. P. Papliński and J. F. Boyce, "Application of an anisotropic diffusion equation in processing a class of ophthalmological images," in Proc. Int. Symp. Adv. Concepts for Intell. Vision Systems (ACIVS'99), (Baden-Baden, Germany), pp. 33-39, The International Institute for Advanced Studies in Systems Research and Cybernetics, August 1999.

[74]

A. P. Papliński, "Directional filtering in edge detection," IEEE Trans. Image Proc., vol. 7, pp. 611-615, April 1998. pdf.

[75]

A. P. Papliński, "Convergence monitoring in generalised Hebbian learning," in Proc. 1988 IEEE International Joint Conference on Neural Networks, IJCNN'98, (Anchorage, Alaska), pp. 1372-1376, May 1998.

[76]

A. P. Papliński and J. F. Boyce, "A first-order wave equation in modelling the behaviour of epithelial cells in an eye posterior capsule," in Proc. 6th IEEE Int. Workshop Intell. Sig. Proc. Comm. Systems (ISPACS'98), (Melbourne, Australia), November 1998.

[77]

M. Shnaider and A. P. Papliński, "Image coding through D-lattice quantization of wavelet coefficients," Graphical Models and Image Processing, vol. 59, pp. 193-204, July 1997.

[78]

N. Rode and A. P. Papliński, "Dynamic gait changing for hexapod walking robots," in Proc. IASTED International Conference on Robotics and Manufacturing, (Cancun, Mexico), pp. 37-40, May 1997.

[79]

A. P. Papliński and J. F. Boyce, "Segmentation of a class of ophthalmological images using a directional variance operator and co-occurrence arrays," Optical Engineering, vol. 36, pp. 3140-3147, November 1997.

[80]

A. P. Papliński and J. F. Boyce, "Tri-directional filtering in processing a class of ophthalmological images," in Proc. IEEE Region 10 Annual Conference, TENCON'97, (Brisbane), pp. 687-690, December 1997.

[81]

A. P. Papliński and J. F. Boyce, "Co-occurrence arrays and edge density in segmentation of a class of ophthalmological images," in Proc. 4rd Conference on Digital Image Computing: Techniques and Applications, DICTA97, (Auckland,), pp. 521-528, December 1997.

[82]

G. Hampson and A. P. Papliński, "Hardware implementation of an ultrasonic beamformer," in Proc. IEEE Region 10 Annual Conference, TENCON'97, (Brisbane), pp. 227-230, December 1997.

[83]

A. Cocchiglia and A. P. Papliński, "Implementation of an autoassociative recurrent neural network for speech recognition," in Proc. IEEE Region 10 Annual Conference, TENCON'97, (Brisbane), pp. 245-248, December 1997.

[84]

A. P. Papliński and N. Bhattacharjee, "Hardware implementation of the Lehmer random number generator," IEE Proc.-Comput. Digit. Tech., vol. 143, pp. 93-95, January 1996. pdf.

[85]

G. Hampson and A. P. Papliński, "Fast implementation of the phase-shift beamformer," in Proc. Fourth International Symposium on Signal Processing and its Applications, ISSPA96, (Gold Coast, Australia), pp. 684-687, August 1996.

[86]

G. Hampson and A. P. Papliński, "The phase-shift beamformer using CORDIC," in Proc. IEEE International Symposium on Phased Array Systems and Technology, (Boston, Massachusetts, USA), pp. 27-30, October 1996.

[87]

M. W. Mount and A. P. Papliński, "Modelling realistic neural systems with a biologically-motivated neural processor chip," in Proc. Seventh Annual International Conference on Signal Processing Applications and Technology, ICSPAT96, (Boston, Massachusets), pp. 1323-1327, October 1996.

[88]

M. Shnaider and A. P. Papliński, "Compression of fingerprint images using wavelet transform and vector quantization," in Proc. Fourth International Symposium on Signal Processing and its Applications, ISSPA96, (Gold Coast, Australia), pp. 437-440, August 1996.

[89]

M. Shnaider and A. P. Papliński, "An interactive wavelet image processor for XWindows," in Proc. Seventh Annual International Conference on Signal Processing Applications and Technology, ICSPAT96, (Boston, Massachusets), pp. 1944-1948, October 1996.

[90]

M. Shnaider and A. P. Papliński, "Wavelet software package for image expansion," in Proc. IEEE Region 10 Conference on Digital Signal Processing Applications, TENCON96, (Perth, Australia), pp. 602-607, November 1996.

[91]

N. J. Rode and A. P. Papliński, "A simple biologically inspired walking robot," in Proc. 27th International Symposium on Industrial Robots, ISIR96, (Milan, Italy), pp. 901-906, October 1996.

[92]

M. W. Mount and A. P. Papliński, "Signal transfer and basic learning in a biologically-motivated neural processor model," in Proc. 8th Int. Conf. on Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, IEA/AIE-95, (Melbourne, Australia), pp. 35-44, June 1995.

[93]

M. W. Mount and A. P. Papliński, "2D simulation of cortical networks in a neural processor array model," in Proc. 1995 Int. Conf. on Neural Networks, ICNN95, vol. 2, (Perth, Australia), pp. 947-952, November 1995.

[94]

M. Shnaider and A. P. Papliński, "A novel wavelet toolbox with optimal vector quantizer," in Proc. 3rd Conference on Digital Image Computing: Techniques and Applications, DICTA95, (Brisbane, Australia), pp. 74-79, December 1995.

[95]

A. P. Papliński and J. F. Boyce, "Segmentation of opacification of posterior capsule images," in Proc. 3rd Conference on Digital Image Computing: Techniques and Applications, DICTA95, (Brisbane, Australia), pp. 503-508, December 1995.

[96]

L. Gustafsson and A. P. Papliński, "An experiment in modelling learning in autism using self-organizing artificial neural networks," Tech. Rep. 2001/93, School of Comp. Sci. and Soft. Eng., Monash University, Australia, June 2002.

[97]

L. Gustafsson and A. P. Papliński, "Simulation of the autistic characteristics of attention shift impairment and novelty avoidance using self-organizing artificial neural networks," Tech. Rep. 2001/93, School of Computer Science and Software Engineering, Monash University, Australia, June 2001.

[98]

A. P. Papliński, N. Bhattacharjee, and C. Greif, "A new word-parallel CORDIC processor for ultrasonic imaging applications," Tech. Rep. 2001/90, School of Computer Science and Software Engineering, Monash University, Australia, March 2001.

[99]

N. Bhattacharjee, A. P. Papliński, and G. Hampson, "Phase-shift beamforming," Tech. Rep. 2000/53, School of Computer Science and Software Engineering, Australia, January 2000.

[100]

A. P. Papliński, "Generalized Hebbian learning and its application in dimensionality reduction," Tech. Rep. 97-2, Digital Systems, Monash University, Australia, June 1997.

[101]

A. P. Papliński, "Improving edge detection by directional filtering," Tech. Rep. 96-1, Digital Systems, Monash University, Australia, February 1996.

[102]

A. P. Papliński, "Image segmentation using tri-directional filtering, conjugate images, and co-occurrence arrays," Tech. Rep. 96-3, Digital Systems, Monash University, Australia, April 1996.

[103]

A. P. Papliński, "A note on parallel calculation of the 2-D discrete convolution," Tech. Rep. 95-5, Robotics and Digital Technology, Monash University, Australia, April 1995. pdf.

[104]

A. P. Papliński and J. F. Boyce, "An implementation of the active contour method for noisy images using a local minimisation algorithm," Tech. Rep. 95-1, Robotics and Digital Technology, Monash University, Australia, January 1995.

[105]

A. P. Papliński and J. F. Boyce, "Circular region extraction using a filtered radial gradient method," Tech. Rep. 95-2, Robotics and Digital Technology, Monash University, Australia, January 1995.

[106]

A. P. Papliński and J. F. Boyce, "Computational aspects of segmentation of a class of medical images using the concept of conjugate images," Tech. Rep. 95-6, Robotics and Digital Technology, Monash University, Australia, April 1995.

[107]

G. Hampson and A. P. Papliński, "Simulation of beamforming techniques for the linear array of transducers," Tech. Rep. 95-3, Robotics and Digital Technology, Monash University, Australia, March 1995.

[108]

M. Shnaider and A. P. Papliński, "Wavelet transform for image coding," Tech. Rep. 94-11, Robotics and Digital Technology, Monash University, Australia, December 1994.

[109]

M. Shnaider and A. P. Papliński, "Frequency-sensitive competitive neural networks with application to image compression," Tech. Rep. 94-2, Robotics and Digital Technology, Monash University, Australia, May 1994.

[110]

G. Hampson and A. P. Papliński, "A VHDL implementation of a CORDIC arithmetic processor," Tech. Rep. 94-09, Robotics and Digital Technology, Monash University, Australia, October 1994.

[111]

G. Hampson and A. P. Papliński, "Beamforming by interpolation," Tech. Rep. 93-12, Robotics and Digital Technology, Monash University, Australia, September 1993.

[112]

P. T. Jantvik, Phenomenological modelling of sensory integration phenomena using self-organized feature maps. PhD thesis, Clayton School of IT, Monash University and Dept. Comp. Sci., Elect. Space Eng., Lulea University of Technology, Melbourne, Australia and Lulea, Sweden, 2012.

[113]

A. M. Khan, Automatic fruit inspection and classification in real time. PhD thesis, Clayton School of Information Technology. Monash University, Melbourne, Australia, 2009. Main supervisor: Andrew P. Papliński.

[114]

N. Faggian, Morphable Human Face Modelling. PhD thesis, Clayton School of Information Technology. Monash University, Melbourne, Australia, 2008. Main supervisor: Andrew P. Papliński.

[115]

D. Schmidt, Minimum Message Length Inference of Autoregressive Moving Average Models. PhD thesis, Clayton School of Information Technology. Monash University, Melbourne, Australia, 2008. Main supervisor: Andrew P. Papliński.

[116]

S. N. R. Wijewickrema, Reconstruction of Quadrics from Silhouettes of Stereo Views with an Application to Automated Fruit Grading. PhD thesis, Clayton School of Information Technology. Monash University, Melbourne, Australia, 2007. Main supervisor: Andrew P. Papliński.

[117]

E. Makalic, Reconstruction of Quadrics from Silhouettes of Stereo Views with an Application to Automated Fruit Grading. PhD thesis, School of Computer Science and Software Engineering. Monash University, Melbourne, Australia, 2006. Supervisors: Lloyd Allison and Andrew P. Papliński.

[118]

N. Bhattacharjee, Performance Enhancement of Synthetic Aperture Ultrasonic Imaging Systems. PhD thesis, School of Computer Science and Software Engineering. Monash University, Melbourne, Australia, 2006. Main supervisor: Andrew P. Papliński.

[119]

M. W. Mount, Development of Hybrid Processor Arrays for Modelling Realistic Neural Systems. PhD thesis, Faculty of Information Technology. Monash University, Melbourne, Australia, 1999. Supervisor: Andrew P. Papliński.

[120]

M. Shnaider, A Study of an Image Coding System Based on the Wavelet Transform and Lattice Vector Quantisation. PhD thesis, Faculty of Computing and Information Technology. Monash University, Melbourne, Australia, 1997. Supervisor: Andrew P. Papliński.

[121]

G. Hampson, Implementing Multi-Dimensional Digital Hardware Beamformers. PhD thesis, Faculty of Computing and Information Technology. Monash University, Melbourne, Australia, 1998. Supervisor: Andrew P. Papliński.

[122]

N. J. Rode, On the Design of a Biologically Inspired Hexapod Robot. PhD thesis, Faculty of Information Technology. Monash University, Melbourne, Australia, 1998. Supervisor: Andrew P. Papliński.





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