Research Projects
Main research areas:
 Computational Neuroscience,
 Computational Intelligence, Neural Networks,
 Computer Vision
Computer vision, image processing and neural networks
 Incremental SelfOrganizing Maps: iSOM [1]

Rotation invariant categorization of visual objects: [2,10]

Iris Localization; [5]

Elastic nets and Generative Topographic Maps: [3,4]

Automatic inspection of sewer pipes: [6]
Computational Neuroscience: Modelling integration of crossmodal sensory information in the brain
This project from the area of computational neuroscience is conducted in collaboration with A/Prof. 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 [7,8,9,16,17,23,32] we have introduced novel Artificial Cortical Networks aka Multimodal SelfOrganizing Networks, consisting of several interconnected SelfOrganizing modules that model integration of multimodal data. See also [31]. 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 [101] Nathan Faggian investigated face detection
and recognition algorithms [13,18,24,25,34].
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 [103] Sudanthi Wijewickrema
http://www.csse.monash.edu.au/~snw/
and Abdul Malik Khan [100] 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: [12,19,20,26,27,28,29,36,37] and [14,15]
Neural Networks and Minimum Message Length (MML)
In these two doctoral projects Daniel Schmidt [102]
and Enes Makalic [104]
investigated application of Minimum Message Length concept to
optimization of neural networks and their applications
[33,40,43,48].
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 SelfOrganizing 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 ndimensional stimuli onto a unity (n+1)dimensional hypersphere. The distance between a stimulus and a weight vector can now be simply measured by the postsynaptic activities. The modified "dotproduct" learning law that keeps evolving weights on the surface of the hypersphere 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
[38,39,44,45,47,51,83,84].
Hybrid analogdigital biologicallymotivated 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 analogdigital biologicallymotivated threedimensional neural processor. Such a neural processor can be used as a computational tool in problems involving realtime 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 [106,62,74,79,80]
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 intraocular 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
[50,54,55,56,66,71,61,60,63]
and
[67,68,82,87,88,89,90,91,92,93].
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 twodimensional 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 Dlattices 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
[49,58,64,107,75,76,77,81,95,96].
Advanced Algorithms for Ultrasonic Imaging
Participants: Dr N. Bhattacharjee completed PhD thesis [105], Charles Greif,
Robert Prain  doctoral candidate, Suhardi Tjoa  completed
Masters thesis and Dr Grant Hampson  completed PhD thesis [108].
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
[108] 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 complexnumber domain have been
presented in a number of publications
[42,53,85,86,69,72,73].
Recent publications
 [1]

A. P. Paplinski, "Incremental selforganizing map (iSOM) in
categorization of visual objects," in ICONIP, LNCS , vol. 7664, Springer, 2012, pp. 125132.

[2]

A. P. Paplinski, "Rotation
invariant categorization of colour images using Radon
transform," in Proc. WCCIIJCNN. IEEE, 2012, pp. 14081413.
 [3]

D. Cohen and A. P. Paplinski, "The
elastic net as visual category
representation: Visualisation and classification," in ICONIP, LNCS,
vol. 7664, Springer, 2012, pp. 133140.
 [4]

D. Cohen and A. P. Paplinski, "A comparative evaluation of the Generative Topographic Mapping
and the Elastic Net for the formation of Ocular Dominance stripes,"
in Proc. WCCIIJCNN. IEEE,
2012, pp. 32373244.
 [5]

N. Mahadeo, A. P. Paplinski, and S. Ray,
"Modelbased pupil and iris
localization," in Proc. WCCIIJCNN. IEEE, 2012, pp. 14271433.
 [6]

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. WCCIIJCNN. IEEE, 2012,
pp. 858865.
 [7]

A. P. Paplinski, L. Gustafsson, and W. M. Mount,
"A recurrent multimodal
network for binding written words and sensorybased semantics into
concepts," in LNCS, B.L. Lu, L. Zhang, and J. Kwok, Eds., vol.
7062. Springer, 2011, pp. 413422.
 [8]

T. Jantvik, L. Gustafsson, and A. P. Paplinski, "A selforganized
artificial neural network architecture for sensory integration with
applications to letterphoneme integration," Neural Computation,
vol. 23, pp. 21012139, 2011.
 [9]

A. P. Paplinski, 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. 15, 2010.
 [10]

A. P. Paplinski, "Rotation
invariant categorization of visual objects
using Radon transform and selforganizing modules," in LNCS, vol.
6444. Springer, 2010, pp. 360366.
 [11]

A. P. Paplinski, "Multivariable ARMA systems  making a polynomial matrix propers,"
Clayton School of IT, Monash University, Australia, Tech. Rep. 2009/240, May
2009. [Online]. Available:
http://www.csse.monash.edu.au/~app/TR2009_240.pdf
 [12]

S. N. R. Wijewickrema, A. Paplinski, and C. E. Esson,
"A novel approach to
orthogonal distance least squares fitting of general conics," Proc.
Int. Conf. Comp. Vision Theory and Appl., Feb. 2009.
 [13]

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, The Netherlands, Sept. 2008.
 [14]

A. M. Khan and A. P. Paplinski, "Blemish detection in citrus fruits," in
Proc. SPITIEEE Colloquium Intern. Conf., Feb. 2008, pp. 203211.
 [15]

A. M. Khan and A. P. Paplinski, "Blemish detection in citrus fruits,,"
in Proc. 7th In. Conf. Appl. Comp. Sci., Wisconsin, USA, Apr. 2008, pp. 262271.
 [16]

L. Gustafsson, T. Jantvik, and A. P. Paplinski,
"A multimodal
selforganizing network for sensory integration of letters and phonemes," in
Proc. IASTED Int. Conf. Artif. Intell. Soft Comp., Palma De
Mallorca, Spain, Aug. 2007.
 [17]

S. Chou, A. P. Paplinski, and L. Gustafsson,
"Speakerdependent bimodal
integration of Chinese phonemes and letters using multimodal
selforganizing networks," in Proc. Int. Joint Conf. Neural Networks,
Orlando, Florida, Aug. 2007, pp. 248253
 [18]

N. Faggian, A. Paplinski, and J. Sherrah, "3D morphable model parameter
estimation," in Lect. Notes in Artif. Intell.,
vol. 4304. Springer, 2006, pp. 519528.
 [19]

S. N. R. Wijewickrema, A. P. Paplinski, and C. E. Esson, "Extraction and
mapping of texture for spherical objects on conveyors," WSEAS Trans.
Signal Proc., vol. 2, pp. 11081115, Aug. 2006.
 [20]

, "Determination of tangency for quadric reconstruction," WSEAS
Trans. Signal Proc., vol. 2, pp. 11001107, Aug. 2006.
 [21]

N. Bhattacharjee and A. P. Paplinski, "Ultrasonic imaging based on
synthetic ellipsoidal wavefronts," WSEAS Trans. Signal Proc., vol. 2,
pp. 14921499, Nov. 2006.
 [22]

A. P. Paplinski and L. Gustafsson, "Feedback in multimodal selforganizing
networks enhances perception of corrupted stimuli," in Lect. Notes in
Artif. Intell., vol. 4304. Springer,
2006, pp. 1928.
 [23]

L. Gustafsson and A. P. Paplinski, "Bimodal integration of phonemes and
letters: an application of multimodal selforganizing networks," in
Proc. Int. Joint Conf. Neural Networks, Vancouver, Canada, July 2006,
pp. 704710.
 [24]

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: IEEE Computer Society, Nov. 2006.
 [25]

N. Faggian, A. P. Paplinski, and T.J. Chin, "Face recognition from video
using active appearance model segmentation," in Proc. 18th Int. Conf.
Pattern Recognition, Hong Kong, Aug. 2006.
 [26]

S. N. R. Wijewickrema, A. P. Paplinski, 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, Aug. 2006.
 [27]

, "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, Aug. 2006.
 [28]

, "Reconstruction of spheres using occluding contours from stereo
images," in Proc. 18th Int. Conf. Pattern Recognition, Hong Kong,
Aug. 2006.
 [29]

, "Reconstruction of ellipsoids on rollers from stereo images using
occluding contours," in Proc. Int. Conf. Computer Vision Theory and
Applications, Setubal, Portugal, Feb. 2006.
 [30]

N. Bhattacharjee and A. P. Paplinski, "Synthetic ellipsoidal wavefront
imaging," in Proc. 5th WSEAS Int. Conf. Circuits, Systems,
Electronics, Control and Signal Processing, Dallas, USA, Nov. 2006, pp.
300305.
 [31]

P. Xu, C.H. Chang, and A. Paplinski, "Selforganizing topological tree for
online vector quantization and data clustering," IEEE Tran. System,
Man and Cybernetics, Part B: Cybernetics, vol. 35, no. 3, pp. 515526, June
2005.
 [32]

A. P. Paplinski and L. Gustafsson, "Multimodal feedforward selforganizing
maps," in Lect. Notes in Comp. Sci., vol. 3801. Springer, 2005, pp. 8188.
 [33]

D. Schmidt, A. P. Paplinski, and G. Lowe, "Adaptive control of hydraulic
systems with MML inferred RBF networks," in Proc. Int. Conf.
Robotics and Automation, Barcelona, Spain, April 2005, pp. 16.
 [34]

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: IEEE Computer Society, Dec. 2005.
 [35]

L. Gustafsson and A. P. Paplinski, "Selforganizing 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, Aug. 2005, pp. 201208.
 [36]

S. N. R. Wijewickrema and A. P. Paplinski, "Generalized Hebbian learning
for ellipse fitting," in Proc. 13th Int. Conf. in Central Europe on
Computer Graphics, Visualization and Computer Vision, Plzen, Czech Republic,
February 2005, pp. 1116.
 [37]

, "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, February
2005, pp. 16.
 [38]

L. Gustafsson and A. P. Paplinski, "Neural network modelling of autism,"
in Recent developments in autism research, M. F. Casanova, Ed. Hauppauge, New York: Nova Science
Publishers, Inc., November 2005, pp. 100134.
 [39]

, "Selforganization 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, no. 2, pp. 189198, April 2004.
 [40]

E. Makalic, L. Allison, and A. P. Paplinski, "MML inference of RBF
neural networks for regression," in Proc. Brazilian Symp. Artificial
Neural Networks (SBRN), São Luis do Maranhão, Brazil, Sept. 2004,
pp. 101108.
 [41]

A. P. Paplinski and L. Gustafsson, "An attempt in modelling early
intervention in autism using neural networks," in Proc. Int. Joint
Conf. Neural Networks, Budapest, Hungary, July 2004, pp. 101108.
 [42]

R. Prain and A. P. Paplinski, "A distributed arithmetic online rotator for
signal processing applications," in EUROMICRO Symp. Digital System
Design: Architectures, Methods and Tools, Rennes, France, September 2004,
pp. 301306.
 [43]

D. Schmidt, A. P. Paplinski, 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, May 2004, pp.
30013006.
 [44]

L. Gustafsson and A. P. Paplinski, "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. Springer, 2003, pp.
11221129.
 [45]

A. P. Paplinski 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. Springer, 2003, pp. 830838.
 [46]

L. Gustafsson and A. P. Paplinski, "Autisticlike 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.
 [47]

A. P. Paplinski and L. Gustafsson, "An attempt in modelling autism using
selforganizing maps," in Proc. 9th Intern. Conf. Neural Information
Processing, Singapore, November 2002, pp. 301304.
 [48]

D. Schmidt and A. P. Paplinski, "An experiment in neurocomputed 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, May 2002, pp. 429433.
 [49]

M. Shnaider and A. P. Paplinski, "Still image compression with lattices in
the wavelet domain," in Advances in Imaging and Electron Physics.
Aspects of Image Processing and Compression. Academic Press, 2001, vol. 119, pp. 56123.
 [50]

A. P. Paplinski, "Current improvements in interpretation of posterior
capsular opacification images," in Proc. Sixth International Symposium
on Signal Processing and its Application, Kuala Lumpur, Malaysia, August
2001, p. 4pp.
 [51]

L. Gustafsson and A. P. Paplinski, "Attention shift impairments and
novelty avoidance  effects of characteristics of autism on the
selforganization of an artificial neural network," in Xth European
Conference on Developmental Psychology, Uppsala, Sweden, August 2001.
 [52]

A. P. Paplinski, N. Bhattacharjee, and C. Greif, "Rotating ultrasonic
signal vectors with a wordparallel CORDIC processor," in Proc.
EUROMICRO Symposium on Digital System Design: Architectures, Methods and
Tools, Warsaw, Poland, September 2001, pp. 254261.
 [53]

N. Bhattacharjee, A. P. Paplinski, and C. Greif, "FPGA implementation of
a pipelined wordparallel CORDIC processor for an ultrasonic imaging
system," in Proc. 2001 IEEE International Symposium on Intelligent
Signal Processing and Communication Systems, Nashville, Tennessee, USA,
November 2001, pp. 429433.
 [54]

A. P. Paplinski, "Curvaturedriven min/max flow and anisotropic diffusion
in image enhancement," in Proc. 2nd Int. Symp. Advanced Concepts for
Intelligent Vision Systems (ACIVS'2000). BadenBaden, Germany: Int. Inst. for Advanced Studies in Systems
Research and Cybernetics, August 2000, pp. 4148.
 [55]

A. P. Paplinski, 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, February 2000, pp. 951958.
 [56]

S. A. Barman, J. F. Boyce, and A. P. Paplinski, "Automatic quantification
of posterior capsular opacification," in Proc. SPIE: Medical Imaging
2000, vol. 3979, San Diego, California, USA, February 2000, pp. 119128.
 [57]

B. M. Garner and A. P. Paplinski, "An interpretation of the function of
the striate cortex," in Proc. SPIE: Medical Imaging 2000, vol. 3981,
San Diego, California, USA, February 2000, pp. 248255.
 [58]

M. Shnaider and A. P. Paplinski, "Lattice vector quantization for wavelet
based image coding," in Advances in Imaging and Electron
Physics. Academic Press, 1999, vol.
109, pp. 199263.
 [59]

, "Selecting lattices for quantization of wavelet coefficients of
images," Optical Engineering, vol. 39, pp. 13271337, May 2000.
 [60]

A. P. Paplinski 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, October 1999, pp. 134138.
 [61]

, "Application of an anisotropic diffusion equation in processing a
class of ophthalmological images," in Proc. International Symposium on
Advanced Concepts for Intelligent Vision Systems (ACIVS'99).
BadenBaden, Germany: The International Institute for
Advanced Studies in Systems Research and Cybernetics, August 1999, pp.
3339.
 [62]

A. P. Paplinski, "Convergence monitoring in generalised Hebbian
learning," in Proc. 1988 IEEE International Joint Conference on Neural
Networks, IJCNN'98, Anchorage, Alaska, May 1998, pp. 13721376.
 [63]

A. P. Paplinski and J. F. Boyce, "A
firstorder wave equation in modelling
the behaviour of epithelial cells in an eye posterior capsule," in
Proc. 6th IEEE International Workshop on Intelligent Signal Processing
and Communication Systems (ISPACS'98), Melbourne, Australia, November 1998.
 [64]

M. Shnaider and A. P. Paplinski, "Image coding through Dlattice
quantization of wavelet coefficients," Graphical Models and Image
Processing, vol. 59, no. 4, pp. 193204, July 1997.
 [65]

N. Rode and A. P. Paplinski, "Dynamic gait changing for hexapod walking
robots," in Proc. IASTED International Conference on Robotics and
Manufacturing, Cancun, Mexico, May 1997, pp. 3740.
 [66]

A. P. Paplinski and J. F. Boyce, "Segmentation of a class of
ophthalmological images using a directional variance operator and
cooccurrence arrays," Optical Engineering, vol. 36, no. 11, pp.
31403147, November 1997.
 [67]

, "Tridirectional filtering in processing a class of ophthalmological
images," in Proc. IEEE Region 10 Annual Conference, TENCON'97,
Brisbane, December 1997, pp. 687690.
 [68]

, "Cooccurrence 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,, December 1997,
pp. 521528.
 [69]

G. Hampson and A. P. Paplinski, "Hardware implementation of an ultrasonic
beamformer," in Proc. IEEE Region 10 Annual Conference, TENCON'97,
Brisbane, December 1997, pp. 227230.
 [70]

A. Cocchiglia and A. P. Paplinski, "Implementation of an autoassociative
recurrent neural network for speech recognition," in Proc. IEEE Region
10 Annual Conference, TENCON'97, Brisbane, December 1997, pp. 245248.
 [71]

A. P. Paplinski and N. Bhattacharjee, "Hardware implementation of the
Lehmer random number generator," IEE Proc.Comput. Digit. Tech.,
vol. 143, no. 1, pp. 9395, January 1996.
 [72]

G. Hampson and A. P. Paplinski, "Fast implementation of the phaseshift
beamformer," in Proc. Fourth International Symposium on Signal
Processing and its Applications, ISSPA96, Gold Coast, Australia, August
1996, pp. 684687.
 [73]

, "The phaseshift beamformer using CORDIC," in Proc. IEEE
International Symposium on Phased Array Systems and Technology, Boston,
Massachusetts, USA, October 1996, pp. 2730.
 [74]

M. W. Mount and A. P. Paplinski, "Modelling realistic neural systems with
a biologicallymotivated neural processor chip," in Proc. Seventh
Annual International Conference on Signal Processing Applications and
Technology, ICSPAT96, Boston, Massachusets, October 1996, pp. 13231327.
 [75]

M. Shnaider and A. P. Paplinski, "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, August 1996, pp. 437440.
 [76]

, "An interactive wavelet image processor for XWindows," in
Proc. Seventh Annual International Conference on Signal Processing
Applications and Technology, ICSPAT96, Boston, Massachusets, October 1996,
pp. 19441948.
 [77]

, "Wavelet software package for image expansion," in Proc. IEEE
Region 10 Conference on Digital Signal Processing Applications, TENCON96,
Perth, Australia, November 1996, pp. 602607.
 [78]

N. J. Rode and A. P. Paplinski, "A simple biologically inspired walking
robot," in Proc. 27th International Symposium on Industrial Robots,
ISIR96, Milan, Italy, October 1996, pp. 901906.
 [79]

M. W. Mount and A. P. Paplinski, "Signal transfer and basic learning in a
biologicallymotivated neural processor model," in Proc. 8th Int.
Conf. on Industrial and Engineering Applications of Artificial Intelligence
and Expert Systems, IEA/AIE95, Melbourne, Australia, June 1995, pp.
3544.
 [80]

, "2D simulation of cortical networks in a neural processor array
model," in Proc. 1995 Int. Conf. on Neural Networks, ICNN95, vol. 2,
Perth, Australia, November 1995, pp. 947952.
 [81]

M. Shnaider and A. P. Paplinski, "A novel wavelet toolbox with optimal
vector quantizer," in Proc. 3rd Conference on Digital Image Computing:
Techniques and Applications, DICTA95, Brisbane, Australia, December 1995,
pp. 7479.
 [82]

A. P. Paplinski 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,
December 1995, pp. 503508.
Technical reports
 [83]

L. Gustafsson and A. P. Paplinski, "An experiment in modelling learning in
autism using selforganizing artificial neural networks," School of Comp.
Sci. and Soft. Eng., Monash University, Australia, Tech. Rep. 2001/93, June
2002.
 [84]

, "Simulation of the autistic characteristics of attention shift
impairment and novelty avoidance using selforganizing artificial neural
networks," School of Computer Science and Software Engineering, Monash
University, Australia, Tech. Rep. 2001/93, June 2001.
 [85]

A. P. Paplinski, N. Bhattacharjee, and C. Greif, "A new wordparallel
CORDIC processor for ultrasonic imaging applications," School of Computer
Science and Software Engineering, Monash University, Australia, Tech. Rep.
2001/90, March 2001.
 [86]

N. Bhattacharjee, A. P. Paplinski, and G. Hampson, "Phaseshift
beamforming," School of Computer Science and Software Engineering,
Australia, Tech. Rep. 2000/53, January 2000.
 [87]

A. P. Paplinski, "Generalized Hebbian learning and its application in
dimensionality reduction," Digital Systems, Monash University, Australia,
Tech. Rep. 972, June 1997.
 [88]

, "Improving edge detection by directional filtering," Digital Systems,
Monash University, Australia, Tech. Rep. 961, February 1996.
 [89]

, "Image segmentation using tridirectional filtering, conjugate images,
and cooccurrence arrays," Digital Systems, Monash University, Australia,
Tech. Rep. 963, April 1996.
 [90]

, "A note on parallel calculation of the 2D discrete convolution,"
Robotics and Digital Technology, Monash University, Australia, Tech. Rep.
955, April 1995.
 [91]

A. P. Paplinski and J. F. Boyce, "An implementation of the active contour
method for noisy images using a local minimisation algorithm," Robotics and
Digital Technology, Monash University, Australia, Tech. Rep. 951, January
1995.
 [92]

, "Circular region extraction using a filtered radial gradient method,"
Robotics and Digital Technology, Monash University, Australia, Tech. Rep.
952, January 1995.
 [93]

, "Computational aspects of segmentation of a class of medical images
using the concept of conjugate images," Robotics and Digital Technology,
Monash University, Australia, Tech. Rep. 956, April 1995.
 [94]

G. Hampson and A. P. Paplinski, "Simulation of beamforming techniques for
the linear array of transducers," Robotics and Digital Technology, Monash
University, Australia, Tech. Rep. 953, March 1995.
 [95]

M. Shnaider and A. P. Paplinski, "Wavelet transform for image coding,"
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G. Hampson and A. P. Paplinski, "A VHDL implementation of a CORDIC
arithmetic processor," Robotics and Digital Technology, Monash University,
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A. M. Khan, "Automatic fruit inspection and classification in real time,"
Ph.D. dissertation, Clayton School of Information Technology. Monash
University, Melbourne, Australia, 2009, Main supervisor: Andrew P.
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N. Faggian, "Morphable human face modelling," Ph.D. dissertation, Clayton
School of Information Technology. Monash University, Melbourne, Australia,
2008, Main supervisor: Andrew P. Paplinski.
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D. Schmidt, "Minimum message length inference of autoregressive moving average
models," Ph.D. dissertation, Clayton School of Information Technology.
Monash University, Melbourne, Australia, 2008, Main supervisor: Andrew P.
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S. N. R. Wijewickrema, "Reconstruction of quadrics from silhouettes of stereo
views with an application to automated fruit grading," Ph.D. dissertation,
Clayton School of Information Technology. Monash University, Melbourne,
Australia, 2007, Main supervisor: Andrew P. Paplinski.
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E. Makalic, "Reconstruction of quadrics from silhouettes of stereo views with
an application to automated fruit grading," Ph.D. dissertation, School of
Computer Science and Software Engineering. Monash University, Melbourne,
Australia, 2006, Supervisors: Lloyd Allison and Andrew P. Paplinski.
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N. Bhattacharjee, "Performance enhancement of synthetic aperture ultrasonic
imaging systems," Ph.D. dissertation, School of Computer Science and
Software Engineering. Monash University, Melbourne, Australia, 2006, Main
supervisor: Andrew P. Paplinski.
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M. W. Mount, "Development of hybrid processor arrays for modelling realistic
neural systems," Ph.D. dissertation, Faculty of Information Technology.
Monash University, Melbourne, Australia, 1999, supervisor: Andrew P.
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M. Shnaider, "A study of an image coding system based on the wavelet transform
and lattice vector quantisation," Ph.D. dissertation, Faculty of Computing
and Information Technology. Monash University, Melbourne, Australia, 1997,
supervisor: Andrew P. Paplinski.
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G. Hampson, "Implementing multidimensional digital hardware beamformers,"
Ph.D. dissertation, Faculty of Computing and Information Technology. Monash
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N. J. Rode, "On the design of a biologically inspired hexapod robot," Ph.D.
dissertation, Faculty of Information Technology. Monash University,
Melbourne, Australia, 1998, supervisor: Andrew P. Paplinski.
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