ORCID Profile
0000-0002-7086-1629
Current Organisations
RMIT University
,
Freelancer
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Publisher: IEEE
Date: 10-2008
Publisher: SPIE
Date: 30-05-2000
DOI: 10.1117/12.386677
Publisher: SPIE-Intl Soc Optical Eng
Date: 08-2002
DOI: 10.1117/1.1489427
Publisher: SPIE-Intl Soc Optical Eng
Date: 08-2000
DOI: 10.1117/1.1305262
Publisher: Elsevier BV
Date: 05-2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-1997
DOI: 10.1109/97.641398
Publisher: IEEE
Date: 2009
Publisher: IEEE
Date: 2002
Publisher: IEEE
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2001
DOI: 10.1109/97.889633
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2006
Publisher: IEEE
Date: 2002
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 11-2017
Publisher: Springer Science and Business Media LLC
Date: 16-08-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1991
DOI: 10.1109/78.80854
Publisher: Wiley
Date: 06-10-2020
DOI: 10.1002/POL.20200560
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2023
Publisher: IEEE
Date: 08-2014
Publisher: ACTAPRESS
Date: 2011
Publisher: Korean Society for Internet Information (KSII)
Date: 09-2011
Publisher: IEEE
Date: 10-2008
Publisher: IEEE
Date: 2007
Publisher: IEEE
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2001
DOI: 10.1109/97.895368
Publisher: Springer Singapore
Date: 2021
Publisher: Elsevier BV
Date: 2009
Publisher: IEEE
Date: 11-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2010
Publisher: Elsevier BV
Date: 03-2016
Publisher: Queensland Univ. Technol
Date: 1999
Publisher: IEEE
Date: 02-2009
Publisher: Elsevier BV
Date: 10-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2019
Publisher: IEEE
Date: 11-2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12
Publisher: SPIE-Intl Soc Optical Eng
Date: 06-2003
DOI: 10.1117/1.1572156
Publisher: Springer Berlin Heidelberg
Date: 2005
DOI: 10.1007/11552413_85
Publisher: Springer Science and Business Media LLC
Date: 03-1998
DOI: 10.1007/BF01413711
Publisher: Elsevier BV
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: SPIE-Intl Soc Optical Eng
Date: 04-2005
DOI: 10.1117/1.1883696
Publisher: SPIE
Date: 25-08-2013
DOI: 10.1117/12.633509
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2012
Publisher: IEEE
Date: 11-2017
Publisher: Elsevier BV
Date: 12-0088
Publisher: SPIE
Date: 07-2005
DOI: 10.1117/12.632657
Publisher: IEEE
Date: 05-2011
Publisher: SPIE
Date: 07-2005
DOI: 10.1117/12.633225
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2005
Publisher: IEEE
Date: 05-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2006
Abstract: A partition-based adaptive vector filter is proposed for the restoration of corrupted digital color images. The novelty of the filter lies in its unique three-stage adaptive estimation. The local image structure is first estimated by a series of center-weighted reference filters. Then the distances between the observed central pixel and estimated references are utilized to classify the local inputs into one of preset structure partition cells. Finally, a weighted filtering operation, indexed by the partition cell, is applied to the estimated references in order to restore the central pixel value. The weighted filtering operation is optimized off-line for each partition cell to achieve the best tradeoff between noise suppression and structure preservation. Recursive filtering operation and recursive weight training are also investigated to further boost the restoration performance. The proposed filter has demonstrated satisfactory results in suppressing many distinct types of noise in natural color images. Noticeable performance gains are demonstrated over other prior-art methods in terms of standard objective measurements, the visual image quality and the computational complexity.
Publisher: MDPI AG
Date: 25-03-2021
Abstract: Surface texturing is a common modification method for altering the surface properties of a material. Predicting the response of a textured surface to scratching is significant in surface texturing and material design. In this study, scratches on a thermoplastic material with textured surface are simulated and experimentally tested. The effect of texture on scratch resistance, surface visual appearance, surface deformation and material damage are investigated. Bruise spot scratches on textured surfaces are found at low scratch forces ( N) and their size at different scratch forces is approximately the same. There is a critical point between the bruise spot damage and the texture pattern damage caused by continuous scratching. Scratch resistance coefficients and an indentation depth-force pattern are revealed for two textured surfaces. A texture named “Texture CB” exhibits high effectiveness in enhancing scratch visibility resistance and can increase the scratch resistance by more than 40% at low scratch forces. The simulation method and the analysis of the power spectral density of the textured surface enable an accurate prediction of scratches.
Publisher: IEEE
Date: 11-2005
Publisher: SPIE
Date: 16-06-2003
DOI: 10.1117/12.502888
Publisher: IEEE
Date: 2000
Publisher: IEEE
Date: 11-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2001
DOI: 10.1109/83.923279
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2012
Publisher: IEEE
Date: 12-2006
Publisher: IEEE
Date: 12-2006
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2009
Publisher: IEEE
Date: 2002
Publisher: IEEE
Date: 1999
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2002
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1994
DOI: 10.1109/9.362847
Publisher: IEEE
Date: 06-2011
Publisher: IEEE
Date: 2006
Publisher: Springer Science and Business Media LLC
Date: 02-02-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2010
Publisher: IEEE
Date: 05-2007
Publisher: Springer Science and Business Media LLC
Date: 30-10-2013
Publisher: IEEE
Date: 2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1989
DOI: 10.1109/29.31295
Publisher: IEEE
Date: 2005
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2008
Publisher: Queensland Univ. Technol
Date: 1999
Publisher: Elsevier BV
Date: 03-2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2013
Publisher: IEEE
Date: 12-2009
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2012
Publisher: Informa UK Limited
Date: 03-07-2014
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 2005
Publisher: IEEE
Date: 07-2012
Publisher: SPIE-Intl Soc Optical Eng
Date: 03-1998
DOI: 10.1117/1.601940
Publisher: IEEE
Date: 2003
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2001
DOI: 10.1109/76.920189
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2019
Publisher: Elsevier BV
Date: 11-1998
Publisher: Elsevier BV
Date: 09-2017
Publisher: IEEE
Date: 07-2009
Publisher: IEEE
Date: 2003
Publisher: IEEE
Date: 05-2009
Publisher: IEEE
Date: 1999
Publisher: Springer Science and Business Media LLC
Date: 28-06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2002
Publisher: IEEE
Date: 07-2010
Publisher: Springer International Publishing
Date: 16-10-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-1998
DOI: 10.1109/76.664096
Publisher: Elsevier BV
Date: 2012
DOI: 10.1016/J.NEUROBIOLAGING.2010.04.010
Abstract: Previous work suggests a general reduction in complexity with aging, referred to as the aging-complexity theory. Fractal dimension (FD) of the vessels in the retina is a global measure of the complexity of the vasculature. However, earlier works did not find any correlation between aging and FD of the retinal vasculature, in contrast to the findings of reduced complexity in other parts of the body. The authors tested the hypothesis that reduced complexity develops with advancing age in the structure of the retinal vasculature. To overcome the limitations of earlier works, a three-dimensional representation of the vasculature, together with Fourier fractal dimension (FFD) techniques, was used. Based on the analysis of 748 retinal images taken of persons aged 49-89 years, we observed a significant decrease in the FFD with aging (p < 0.0001). These data provide evidence supporting rarefaction (i.e. reduction) of the retinal vasculature with aging, consistent with observations from other human organ systems.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: SPIE
Date: 30-05-2000
DOI: 10.1117/12.386650
Publisher: IEEE
Date: 1999
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2002
DOI: 10.1109/5.982412
Publisher: IEEE
Date: 2010
Publisher: IEEE
Date: 07-2010
Publisher: ACTAPRESS
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2010
Publisher: IEEE
Date: 11-2005
Publisher: Elsevier BV
Date: 10-1999
Publisher: IEEE
Date: 09-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1989
DOI: 10.1109/29.31283
Publisher: Elsevier BV
Date: 09-2019
Publisher: Elsevier BV
Date: 08-2020
Publisher: Elsevier BV
Date: 07-2007
Publisher: Elsevier
Date: 2008
Publisher: Queensland Univ. Technol
Date: 1999
Publisher: IEEE
Date: 08-2014
Publisher: IEEE
Date: 2008
Publisher: Springer Berlin Heidelberg
Date: 2000
Publisher: IEEE
Date: 05-2002
Publisher: ACTAPRESS
Date: 2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2004
Publisher: IEEE
Date: 03-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2010
Publisher: Springer Science and Business Media LLC
Date: 09-02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 1998
DOI: 10.1109/72.712168
Abstract: A neural-network-based adaptive tracking control scheme is proposed for a class of nonlinear systems in this paper. It is shown that RBF neural networks are used to adaptively learn system uncertainty bounds in the Lyapunov sense, and the outputs of the neural networks are then used as the parameters of the controller to compensate for the effects of system uncertainties. Using this scheme, not only strong robustness with respect to uncertain dynamics and nonlinearities can be obtained, but also the output tracking error between the plant output and the desired reference output can asymptotically converge to zero. A simulation ex le is performed in support of the proposed neural control scheme.
Publisher: SPIE
Date: 30-05-2000
DOI: 10.1117/12.386544
Publisher: IEEE
Date: 12-2017
Publisher: SPIE
Date: 11-07-2010
DOI: 10.1117/12.863349
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2005
Abstract: A robust structure-adaptive hybrid vector filter is proposed for digital color image restoration in this paper. At each pixel location, the image vector (i.e., pixel) is first classified into several different signal activity categories by applying a modified quadtree decomposition to luminance component (image) of the input color image. A weight-adaptive vector filtering operation with an optimal window is then activated to achieve the best tradeoff between noise suppression and detail preservation. Through extensive simulation experiments conducted using a wide range of test color images, the filter has demonstrated superior performance to that of a number of well known benchmark techniques, in terms of both standard objective measurements and perceived image quality, in suppressing several distinct types of noise commonly considered in color image restoration, including Gaussian noise, impulse noise, and mixed noise.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2006
Publisher: Springer Science and Business Media LLC
Date: 03-2001
DOI: 10.1007/BF01201134
Publisher: IEEE
Date: 10-2014
Publisher: World Scientific Pub Co Pte Lt
Date: 04-2011
DOI: 10.1142/S0219467811004081
Abstract: Temporal fluctuations are often observed in digitally compressed videos. However, it is difficult to accurately measure these fluctuation intensities with the traditional peak signal-to-noise ratio (PSNR) since the PSNR only provides a generic quality measure. Although specialized metrics have been proposed for temporal fluctuation measurement, e.g., the sum of squared differences (SSD) and the motion compensated SSD (MCSSD), these first difference based algorithms may falsely treat smooth continuous change of pixel values as temporal fluctuations. To overcome this problem, a motion estimated mean scaled absolute second difference (MEMSASD) is proposed here. The performance of the MEMSASD is examined using a number of video sequences with varying degrees of temporal fluctuations, generated by an H.264/AVC compliant codec using standard test video sequences. Compared with the PSNR and the SSD, the behavior of the MCSSD and the proposed metric provide better reflections of temporal fluctuation intensities as perceived by the human visual system (HVS), in terms of the Pearson correlation coefficient. The MEMSASD metric has an advantage over MCSSD in that it avoids misclassification of temporal fluctuations of pixels with smooth continuous change along the temporal axis.
No related grants have been discovered for Hong Ren Wu.