检索结果-内蒙古大学图书馆

Improved decision-based detail-preserving variational method for removal of random-valued impulse noise

IET image processing 2012年第7期6卷 976-985页

作者： Zhou, Y. Y. Ye, Z. F. Huang, J. J. Univ Sci & Technol China Dept Elect Engn & Informat Sci Inst Stat Signal Proc Hefei 230027 Anhui Peoples R China

The authors propose an improved decision-based detail-preserving variational method (DPVM) for removal of random-valued impulse noise. In the denoising scheme, adaptive centre weighted median filter (ACWMF) is first ameliorated by employing the variable window technique to improve its detection ability in highly corrupted images. Based on the improved ACWMF, a fast iteration strategy is used to classify the noise candidates and label them with different noise marks. Then, all the noise candidates are restored one-time by weight-adjustable detail-preserving variational method. The weights between the data-fidelity term and the smooth regularisation term of the convex cost-function in DPVM are decided by the noise marks. After minimisation, the restored image is obtained. Extensive simulation results show that the proposed method outperforms some existing algorithms, both in vision and quantitative measurements. Moreover, our method is faster than some decision-based DPVM. Therefore it can be ported into practical application easily.

关键词： iterative methods Filtering methods in signal processing data fidelity term random valued impulse noise removal highly corrupted images decision based detail preserving variational method computer vision and image processing techniques image restoration impulse noise variable window technique Optical, image and video signal processing median filters convex cost function detection ability adaptive centre weighted median filter adaptive filters noise marks Interpolation and function approximation (numerical analysis) ACWMF smooth regularisation term noise candidates image denoising iteration strategy

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Face recognition based on a two-view projective transformation using one sample per subject

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IET computer vision 2012年第5期6卷 489-498页

作者： Kuo, C. -H. Lee, J. -D. Chang Gung Univ Dept Elect Engn Tao Yuan 33302 Taiwan Chung Chou Univ Sci & Technol Dept Elect Engn & Energy Technol Changhua 51003 Taiwan

In this study, the authors propose a novel face recognition algorithm based on geometric features to alleviate the one sample per subject problem, called the robust estimation system. Our application adopts both local and global information for robust estimation. The authors utilise the original images from the ORL and Yale databases for evaluation. The images of the FERET database are pre-processed to extract the pure face region and execute the affine transformation. The authors roughly divide the face images into four block images that are most significant for the face: left eye, right eye, nose and mouth. The feature extraction using magnitude of first-order gradients, based on geometric features, is ideal for estimating a single sample. While conducting the classification stage, local features are putatively matched before processing or the global random sample consensus robust estimation features, with the aim of identifying the fundamental matrix between two matched face images. Finally, similarity scores are calculated, and the candidate awarded the highest score is designated the correct subject. Experiments were implemented using the FERET, ORL and Yale databases to demonstrate the efficiency of the proposed method. The experimental results show that our algorithm greatly improves recognition performance compared with the existing methods.

关键词： Optimisation techniques geometric features ORL databases Other topics in statistics affine transforms robust estimation system image recognition similarity scores first-order gradients gradient methods Integral transforms Yale databases recognition performance matrix algebra FERET database affine transformation global random sample consensus robust estimation features local information Algebra face recognition two-view projective transformation matched face images face recognition algorithm image classification feature extraction computer vision and image processing techniques one sample per subject problem estimation theory global information fundamental matrix face region random processes block images classification stage

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Characterisation framework for epileptic signals

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IET image processing 2012年第9期6卷 1227-1235页

作者： Vazquez, D. M. Rubio, J. J. Pacheco, J. Inst Politecn Nacl ESIME Azcapotzalco Secc Estudios Posgrad & Invest Mexico City 02250 DF Mexico

In this study, two epileptic signals are extracted from scanned images and three algorithms are proposed to characterise epileptic signals: the modified least squares, modified nearest neighbour, and slopes algorithms... 详细信息

关键词： modified least squares slopes algorithms computer vision and image processing techniques image processing scanned images Signal processing and detection characterisation framework Optical, image and video signal processing modified nearest neighbour epileptic signals

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Low-angle tracking of two objects in a three-dimensional beamspace domain

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IET RADAR SONAR AND NAVIGATION 2012年第1期6卷 9-20页

作者： Kim, J. Yang, H. J. Kwak, N. LIG Nex1 Co ISR R&D Lab Yongin 446798 South Korea Korea Ocean Res & Dev Inst Dept Ocean Engn Res Taejon 305343 South Korea Ajou Univ Dept Elect & Comp Engn Suwon 443749 South Korea

Among various low-angle tracking methods, the three-dimensional beamspace domain maximum likelihood (3-D BDML) estimation proposed by Zoltowski is a computationally attractive and optimal method that can be processed in the reduced beamspace domain. However, the estimation performance of 3-D BDML deteriorates in the presence of interference or an additional target, especially at low altitudes, because the dimension of the signal and noise exceeds the dimension provided by the three beams. This study proposes a new low-angle tracking method for two objects in a 3-D beamspace domain using a linearly constrained adaptive array. The increased signal dimension owing to the interference or the additional target is reduced in the beamspace domain by using the beamforming weight that is designed to remove the largest principal component in the covariance matrix. Numerical simulation results are provided to show the estimation performance of the proposed method.

关键词： maximum likelihood estimation array signal processing signal dimension covariance matrix low-angle tracking covariance matrices 3D BDML estimation three-dimensional beamspace domain maximum likelihood estimation computer vision and image processing techniques Optical, image and video signal processing object tracking Algebra beamforming weight Other topics in statistics

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Comprehensive analysis of spectral minutiae for vein pattern recognition

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IET BIOMETRICS 2012年第1期1卷 25-36页

作者： Hartung, D. Olsen, M. Aastrup Xu, H. Nguyen, H. Thanh Busch, C. Hogskolen Gjovik Norwegian Informat Secur Lab NISlab N-2815 Gjovik Norway Ctr Adv Secur Res Darmstadt CASED D-64293 Darmstadt Germany Univ Twente NL-7500 AE Enschede Netherlands

Similar to biometric fingerprint recognition, characteristic minutiae points - here end and branch points - can be extracted from skeletonised vein images to distinguish individuals. An approach to extract those vein minutiae and to transform them into a fixed-length, translation and scale invariant representation where rotations can be easily compensated is presented in this study. The proposed solution based on spectral minutiae is evaluated against other comparison strategies on three different datasets of wrist and palm dorsal vein samples. The authors' analysis shows a competitive biometric performance while producing features that are compatible with state-of-the-art template protection systems. In addition, a modified and more distinctive, but not transform or rotation invariant, representation is proposed and evaluated.

关键词： scaling phenomena vein recognition skeletonised vein images scale invariant representation spectral minutiae palm dorsal vein samples template protection systems vein pattern recognition wrist vein samples image recognition end and branch points image representation competitive biometric performance computer vision and image processing techniques feature extraction characteristic minutiae points

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Discriminatively trained patch-based model for occupant classification

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IET INTELLIGENT TRANSPORT SYSTEMS 2012年第2期6卷 132-138页

作者： Huang, S. -S. Natl Kaohsiung First Univ Sci & Technol Dept Comp & Commun Engn Kaohsiung Taiwan

This study presents a vision-based occupant classification method which is essential for developing a system that can intelligently decide when to turn on airbags based on vehicle occupancy. To circumvent intra-class variance, this work considers the empty class as a reference and describes the occupant class by using appearance difference rather than the traditional methods of using appearance itself. Each class in this work is modelled using a set of representative parts called patches. Each patch is represented by a Gaussian distribution. This approach successfully alleviates the mis-classification problem resulting from severe lighting change which makes the image locally overexposed or underexposed. Instead of using maximum likelihood for patch selection and estimating the parameters of the proposed generative models, the proposed method discriminatively learns models through a boosting algorithm by minimising training error. Experimental results from many videos (approximately 1 630 000 frames) from a camera deployed on a moving platform demonstrate the effectiveness of the proposed approach.

关键词： Traffic engineering computing generative models computer vision vision-based occupant classification method computer vision and image processing techniques Knowledge engineering techniques misclassification problem patch selection vehicle occupancy Optical, image and video signal processing discriminatively trained patch-based model image classification boosting algorithm Gaussian distribution learning (artificial intelligence) road safety intraclass variance Other topics in statistics traffic engineering computing

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Oriented diffusion filtering for enhancing low-quality fingerprint images

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IET BIOMETRICS 2012年第2期1卷 105-113页

作者： Gottschlich, C. Schoenlieb, C. -B. Univ Gottingen Inst Math Stochast D-37077 Gottingen Germany Univ Cambridge Dept Appl Math & Theoret Phys Cambridge CB3 0WA England

To enhance low-quality fingerprint images, we present a novel method that first estimates the local orientation of the fingerprint ridge and valley flow and next performs oriented diffusion filtering, followed by a locally adaptive contrast enhancement step. By applying the authors' new approach to low-quality images of the FVC2004 fingerprint databases, the authors are able to show its competitiveness with other state-of-the-art enhancement methods for fingerprints like curved Gabor filtering. A major advantage of oriented diffusion filtering over those is its computational efficiency. Combining oriented diffusion filtering with curved Gabor filters led to additional improvements and, to the best of the authors' knowledge, the lowest equal error rates achieved so far using MINDTCT and BOZORTH3 on the FVC2004 databases. The recognition performance and the computational efficiency of the method suggest to include oriented diffusion filtering as a standard image enhancement add-on module for real-time fingerprint recognition systems. In order to facilitate the reproduction of these results, an implementation of the oriented diffusion filtering for Matlab and GNU Octave is made available for download.

关键词： mathematics computing fingerprint identification image recognition GNU Octave visual databases low-quality fingerprint image enhancement fingerprint ridge MINDTCT curved Gabor filtering oriented diffusion filtering computer vision and image processing techniques Filtering methods in signal processing image enhancement FVC2004 fingerprint databases standard image enhancement add-on module locally adaptive contrast enhancement step valley flow Gabor filters BOZORTH3 Matlab real-time fingerprint recognition systems

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Cognition-based parameter setting of non-linear filters using a face recognition system

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IET image processing 2012年第8期6卷 1057-1063页

作者： Matsumoto, M. Univ Electrocommun Educ & Res Ctr Frontier Sci Chofu Tokyo 1828585 Japan

This study introduces cognition-based parameter setting of non-linear filters using a face recognition system. The parameter setting problem is generally solved by the maximisation or minimisation of some objective evaluation functions such as correlation and statistical independence. However, the tuned filter output is not always adequate for face recognition systems because filter and system are separately tuned using different criterion. When a single image with noise is considered, it is difficult to employ such objective functions because only a single image exists, and the original image cannot be known. When the authors consider some problems such as contrast adjustment and non-photo-realistic rendering, it is also difficult to set an objective criterion for parameter setting because there are no correct solutions other than humans' subjectivity. To handle such cases, the authors look to some subjective information such as a face in an image, and directly employ a face recognition system as the evaluation function for parameter setting. Experimental results show that cognition-based evaluation has the potential to handle these types of problems.

关键词： face recognition system rendering (computer graphics) objective evaluation function maximisation nonlinear filters image recognition face recognition computer vision and image processing techniques Graphics techniques contrast adjustment objective evaluation function minimisation minimisation nonlinear filter Filtering methods in signal processing cognition-based parameter setting Optimisation techniques nonphoto-realistic rendering

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Level-base compounded logarithmic curve function for colour image enhancement

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IET image processing 2012年第7期6卷 943-958页

作者： Lin, J. -S. Liu, P. -J. Liao, Y. -Y. Tai, S. -C. Natl Chin Yi Univ Technol Dept Comp Sci & Informat Engn Taichung 411 Taiwan Natl Cheng Kung Univ Inst Comp & Commun Engn Tainan 701 Taiwan

In this study, the authors present a new strategy to implement an illumination compensation-based contrast enhancement. Different from the traditional pixel-to-pixel transformation, the proposed method offers a level-to-level framework by generating the reference intensity level and the given target intensity level. Fundamentally, the traditional illumination compensation algorithms such as Histogram equalisation, log and gamma transformation are trade-off strategies and face the same dilemma. For example, the log function compresses the dynamic range of image with large variations in pixel values. The proposed method is an integration algorithm, the illumination compensation could be considered as the transformation from reference intensity to the chosen target intensity, and the contrast is enhanced by the given algebraic definition of the characteristic values of each pixel. The advantages are the adjusted intensity would be accomplished in the native phase according to the variant characteristic intensity values, clarifying the details of the darker areas as well as preserving the highlight areas, reducing HALO, colour constancy, removing colour cast and colour restoration. Shown as the experimental results, the proposed method can obtain better performance compared with other methods in subjective evaluation with contour plot and the objective evaluations by universal quality index, structural similarity index and average of standard deviations.

关键词： trade-off strategies colour restoration algebraic definition HALO image enhancement image colour analysis colour image enhancement illumination compensation-based contrast enhancement computer vision and image processing techniques lighting pixel-to-pixel transformation log function colour constancy gamma transformation image restoration Optical, image and video signal processing algebra structural similarity index standard deviations Algebra log transformation level-to-level framework level-base compounded logarithmic curve function histogram equalisation colour cast illumination compensation universal quality index

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Speckle reduction with edges preservation for ultrasound images: using function spaces approach

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IET image processing 2012年第7期6卷 813-821页

作者： Lee, M. -S. Yen, C. -L. Ueng, S. -K. Natl Taiwan Ocean Univ Dept Comp Sci & Engn Keelung Taiwan Keelung Chang Gung Mem Hosp Dept Internal Med Keelung Taiwan

In this study, a novel speckle reduction method is proposed for ultrasound images. This denoising method is designed to preserve both the edges and structural details of the image. Speckle noise is suppressed, without smearing the edges, by extending the smoothness of the image in the wavelet-based Holder spaces. A comparison of smoothing speckles with the other well-known methods is provided via the size of Besov norm. The authors validate the proposed method using synthetic data, simulated and real ultrasound images. Experiments demonstrate the performance improvement of the proposed method over other state-of-the-art methods in terms of image quality and edge preservation indices.

关键词： real ultrasound images ultrasonic imaging synthetic data Besov norm image quality Patient diagnostic methods and instrumentation speckle noise suppression edge preservation indices denoising method speckle smoothing function space approach Sonic and ultrasonic radiation (medical uses) Biology and medical computing wavelet-based Holder spaces computer vision and image processing techniques Optical, image and video signal processing Sonic and ultrasonic radiation (biomedical imaging/measurement) smoothing methods speckle reduction method image structural details medical image processing image denoising image smoothness simulated ultrasound images

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