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IEEE International Conference on image processing

作者： Zhuoshi Wei Yufei Han Zhenan Sun Tieniu Tan National Laboratory of Pattern Recognition Institute of Automation Chinese Academy and Sciences Beijing China

In this paper we present a preliminary study of palmprint image synthesis and propose a framework for synthesizing palmprint texture. We first extract principal lines of real palm- prints using edge detection and synthesize wrinkles and ridges of palm using patch-based sampling. Then we incorporate principal lines, wrinkles and ridges to obtain the final synthetic image. After that multiple images are derived from each artificial palm to simulate the intra-class images. Our approach can generate large palmprint databases which preserve inter-class and intra-class variations. Experimental results demonstrate that the synthetic images bear a close resemblance to real palmprints in terms of appearance as well as statistical properties, showing a promising usage in algorithms evaluation and comparison.

关键词： image generation image sampling image databases Biometrics Fingerprint recognition stochastic processes Iris Sampling methods Sun Laboratories

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A Robust Iterative Multiframe SRR using stochastic Regularization Technique based on Hampel Estimation

A Robust Iterative Multiframe SRR using Stochastic Regulariz...

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5th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON 2008), vol.1

作者： Vorapoj Patanavijit Department of Computer and Network Engineering Faculty of Engineering Assumption University Bangkok Thailand

Within the traditional research community in this decade, there has been recently a great deal of work developing Super-Resolution Reconstruction (SRR) algorithms for combining a set of low quality images to produce a set of higher quality images. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation. Hence, these algorithms are usually too sensitive to their assumed noise model limiting their application. In this paper, we propose a novel SRR framework using the stochastic regularization technique by minimizing a cost function. The Hampel norm is used for measuring the difference between the projected estimate of the high quality image and each low high quality image and for removing outliers in the data. To remove artifacts from the final answer and improve the rate of convergence, Tikhonov regularization is incorporated in the proposed framework. Finally, a number of experimental results are presented to demonstrate the performance of the proposed algorithm in comparison to several previously published methods using a several noise models such as noiseless, AWGN, Poisson Noise and Salt & Pepper Noise.

关键词： image resolution Noise Robustness Estimation Minimization IP networks Signal processing algorithms

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Detection of rolling leukocytes by marked point processes

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JOURNAL OF ELECTRONIC IMAGING 2007年第3期16卷 033013.1-033013.11页

作者： Dong, Gang Acton, Scott T. Univ Virginia Dept Elect & Comp Engn Dept Biomed Engn Charlottesville VA 22904 USA

The marked point process (MPP) provides a useful and theoretically well-established tool for integrating spatial information into the image analysis process. We consider the problem of detecting rolling leukocytes within intravital microscopy images. A first stage of the detection method reduces the detection to a set of points, each one representing a possible leukocyte. Our task is then to decide which points are actual leukocytes. We propose an MPP-based approach that aims at improving both the accuracy and efficiency of the detection process by means of exploiting the spatial interrelationships. We construct a Markov chain Monte Carlo algorithm to obtain the maximum a posteriori (MAP) estimation of a set of points corresponding to the centroids of leukocytes observed in the image. The optimal solution, in terms of the MAP principle, is computed with respect to all leukocytes, rather than a single leukocyte. A quantitative study of our detection approach demonstrates results that compare very well to those achieved by manual detection and exceed the solution quality given by two competing methods. Our approach can serve as a fully automated substitute to the tedious and time-consuming manual rolling leukocyte detection process. (c) 2007 SPIE and IS&T.

关键词： image processing Data modeling Process modeling Microscopy Monte Carlo methods Video statistical analysis stochastic processes image analysis Magnetorheological finishing

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A robust iterative super-resolution reconstruction of image sequences using a Lorentzian Bayesian approach with fast affine block-based registration

A robust iterative super-resolution reconstruction of image ...

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IEEE International Conference on image processing (ICIP 2007)

作者： Patanavijit, V. Tae-O-Sot, S. Jitapunkul, S. Assumption Univ Fac Engn Bangkok Thailand Naresuan Univ Fac Engn Tha Pho Thailand Chulalongkorn Univ Fac Engn Bangkok Thailand

ISBN: (纸本)9781424414369

Although the topic of Super-Resolution Reconstruction (SRR) has recently received considerable attention within the traditional research community, the SRR estimations are based on L1 or L2 statistical norm estimation. Therefore, these SRR methods are very sensitive to their assumed data and noise models, which limit their applications. The real noise models that corrupt the measure sequence are unknown;consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. The robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes an alternate SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Lorentzian norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. In order to cope with real sequences and complex motion sequences, the fast affine block-based registration is used in the registration step of SRR. The experimental results show that the proposed reconstruction can be applied on real sequences such as Suzie sequence and confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norm for several noise models such as AWGN, Poisson and Salt & Pepper noise.

关键词： SRR stochastic regularization technique Lorentzian norm affine block-based registration

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Fast iterative kernel PCA 19

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20th Annual Conference on Neural Information processing Systems, NIPS 2006

作者： Schraudolph, Nicol N. Günter, Simon Vishwanathan, S.V.N. Statistical Machine Learning National ICT Australia Locked Bag 8001 Canberra ACT 2601 Australia School of Information Sciences and Engineering Australian National University Canberra ACT 0200 Australia

ISBN: (纸本)9780262195683

We introduce two methods to improve convergence of the Kernel Hebbian Algorithm (KHA) for iterative kernel PCA. KHA has a scalar gain parameter which is either held constant or decreased as 1/t, leading to slow convergence. Our KHA/et algorithm accelerates KHA by incorporating the reciprocal of the current estimated eigenvalues as a gain vector. We then derive and apply stochastic Meta-Descent (SMD) to KHA/et;this further speeds convergence by performing gain adaptation in RKHS. Experimental results for kernel PCA and spectral clustering of USPS digits as well as motion capture and image de-noising problems confirm that our methods converge substantially faster than conventional KHA.

关键词： image denoising

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A Lorentzian stochastic estimation for a robust iterative multiframe super-resolution reconstruction with Lorentzian-Tikhonov regularization

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EURASIP JOURNAL ON ADVANCES IN SIGNAL processing 2007年第1期2007卷 034821-034821页

作者： Patanavijit, V. Jitapunkul, S. Chulalongkorn Univ Fac Engn Dept Elect Engn Bangkok 10330 Thailand

Recently, there has been a great deal of work developing super-resolution reconstruction (SRR) algorithms. While many such algorithms have been proposed, the almost SRR estimations are based on L1 or L2 statistical norm estimation, therefore these SRR algorithms are usually very sensitive to their assumed noise model that limits their utility. The real noise models that corrupt the measure sequence are unknown;consequently, SRR algorithm using L1 or L2 norm may degrade the image sequence rather than enhance it. Therefore, the robust norm applicable to several noise and data models is desired in SRR algorithms. This paper first comprehensively reviews the SRR algorithms in this last decade and addresses their shortcomings, and latter proposes a novel robust SRR algorithm that can be applied on several noise models. The proposed SRR algorithm is based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. For removing outliers in the data, the Lorentzian error norm is used for measuring the difference between the projected estimate of the high-resolution image and each low-resolution image. Moreover, Tikhonov regularization and Lorentzian-Tikhonov regularization are used to remove artifacts from the final answer and improve the rate of convergence. The experimental results confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on L1 and L2 norms for several noise models such as noiseless, additive white Gaussian noise (AWGN), poisson noise, salt and pepper noise, and speckle noise.

关键词： LU1 image sequences Cost functions speckle noise super-resolution Noise models image resolution AWGN data models

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Bayesian filtering using a convex hull criterion to estimate multi-phase distributions in electrical process tomography 5

Bayesian filtering using a convex hull criterion to estimate...

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5th World Congress in Industrial Process Tomography

作者： Watzenig, D. Steiner, G. Fuchs, A. Zangl, H. Wegleiter, H. Institute of Electrical Measurement and Measurement Signal Processing Graz University of Technology Kopernikusgasse 24/4 GrazA-8010 Austria

ISBN: (纸本)9780853163213

A key difference between regularization and Bayesian methods to solve inverse ill-posed problems is that Bayesian approaches, instead of giving only single point estimates, present averages over all feasible solutions. The most natural way to solve inverse problems in the framework of Bayesian statistics is the application of computationally expensive Markov chain Monte Carlo methods. However especially in industrial process tomography, e.g. in automatic control of certain process states, the aspect of computational cost plays an important role. Particle filtering - a sequential Monte Carlo method that relies on importance sampling - is able to feature real-time operation while maintaining the advantages of statistical inversion such as yielding robust estimates of properties calculated from the *** this paper, a particle filter approach to reconstruct inclusions in an otherwise uniform background material from measured electrical capacitance data using a convex hull criterion is presented. This criterion allows for reconstructing both material inclusions, as well as phase interfaces in sedimentation processes without increasing the computational complexity. By taking into account measurement uncertainties via an appropriate measurement noise model, a range of parameters consistent with the measured data can be quantified, from which one can calculate any statistics of interest. The forward map is numerically implemented using finite elements while the inclusion boundaries are modelled by means of parameterized curves. In order to solve the forward problem, the inclusion boundary is mapped on the mesh grid. By prescribing the order of the used Fourier descriptor contour model, prior information is incorporated that leads to smooth boundaries in the image space. Filter convergence is improved due to a fragmented process covariance matrix scaled with different stochastic weights for inclusion position and inclusion shape. The proposed approach is validated fo

关键词： Covariance matrix

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A Robust Iterative Super-Resolution Reconstruction of image Sequences using a Lorentzian Bayesian Approach with Fast Affine Block-Based Registration

A Robust Iterative Super-Resolution Reconstruction of Image ...

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IEEE International Conference on image processing

作者： V. Patanavijit S. Tae-O-Sot S. Jitapunkul Faculty of Engineering Assumption University Bangkok Thailand Faculty of Engineering Naresuan University (Phayao Cambus) Thailand Faculty of Engineering Chulalongkorn University Thailand Thailand

Although the topic of Super-Resolution Reconstruction (SRR) has recently received considerable attention within the traditional research community, the SRR estimations are based on LI or L2 statistical norm estimation. Therefore, these SRR methods are very sensitive to their assumed data and noise models, which limit their applications. The real noise models that corrupt the measure sequence are unknown; consequently, SRR algorithm using LI or L2 norm may degrade the image sequence rather than enhance it. The robust norm applicable to several noise and data models is desired in SRR algorithms. This paper proposes an alternate SRR approach based on the stochastic regularization technique of Bayesian MAP estimation by minimizing a cost function. The Lorentzian norm is used for measuring the difference between the projected estimate of the high-resolution image and each low resolution image, removing outliers in the data. Tikhonov regularization is used to remove artifacts from the final result and improve the rate of convergence. In order to cope with real sequences and complex motion sequences, the fast affine block-based registration is used in the registration step of SRR. The experimental results show that the proposed reconstruction can be applied on real sequences such as Suzie sequence and confirm the effectiveness of our method and demonstrate its superiority to other super-resolution methods based on LI and L2 norm for several noise models such as AWGN, Poisson and Salt & Pepper noise.

关键词： Robustness Iterative methods image resolution image reconstruction image sequences Bayesian methods Additive white noise Gaussian noise Noise measurement Degradation

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Speckle Suppression in Medical Ultrasound images Using Two Dimensional GARCH Model

Speckle Suppression in Medical Ultrasound Images Using Two D...

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IEEE Pacific Rim Conference on Communications, Computers and Signal processing

作者： Maryam Amirmazlaghani Hamidreza Amindavar Department of Electrical Engineering Amirkabir University of Technology슠 Tehran Iran

ISBN: (纸本)9781424411894;1424411890

In this paper, we introduce a novel speckle suppression method for medical ultrasound images, based on statistical model of wavelet coefficients. We use two-dimensional generalized autoregressive conditional heteroscedasticity (GARCH) model for statistical modeling of images wavelet coefficients. By using two-dimensional GARCH model on the wavelet coefficients, we are capable of taking into account important characteristics of wavelet coefficients, such as non-stationarity, heavy tailed marginal distribution, and the dependencies between the coefficients. Furthermore, we use minimum mean square error (MMSE) estimator for estimating the clean image wavelet coefficients. Here, to prove the performance of this method in speckle suppression, we have compared our proposed method with various speckle suppression methods applied on actual ultrasound medical images and we verify the performance improvement in utilizing the new strategy.

关键词： Speckle Biomedical imaging Ultrasonic imaging Wavelet coefficients Medical diagnostic imaging Wiener filter Wavelet transforms Noise reduction stochastic processes Mean square error methods

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Metropolis-Hasting techniques for finite-element-based registration

Metropolis-Hasting techniques for finite-element-based regis...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Frederic J.P. Richard Adeline M.M. Samson Laboratory MAP5 CNRS UMR University of Paris Descartes Paris France

In this paper, we focus on the design of Markov Chain Monte Carlo techniques in a statistical registration framework based on finite element basis (FE). Due to the use of FE basis, this framework has specific features. The main feature is that displacement random fields are Markovian. We construct two hybrid Gibbs/Metropolis-Hasting algorithms which take fully advantage of this Markovian property. The second technique is defined in a coarse-to-fine way by introducing a penalization on the sampled posterior distribution. We present some promising results suggesting that both techniques can accurately register images. Experiments also show that the penalized technique is more robust to local maxima of the posterior distribution than the first technique. This study is a preliminary step towards the estimation of model parameters in complex image registration problems.

关键词： Finite element methods image registration stochastic resonance Monte Carlo methods Parameter estimation Probability distribution Laboratories Robustness image processing Deformable models

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