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Adaptive scalar and vector median filtering of noisy colour images based on noise estimation

IET image processing 2011年第6期5卷 541-553页

作者： Liu, S. Dalian Univ Technol Sch Elect & Informat Engn Dalian 116024 Peoples R China Dalian Naval Acad Dept Informat & Commun Engn Dalian 116018 Peoples R China

To address the problem of removing impulsive noise with different density from colour images, a new filtering algorithm is proposed based on noise estimation as well as adaptive scalar (SMF) and vector median filter (VMF). Two-level noise estimation scheme is adopted for noise detection, where the first-level estimation is based on maximum and minimum intensity value of each colour channel, and the second-level estimation uses weighted directional operators. For noise restoration, the uncorrupted pixels are remained unchanged, and the corrupted pixels of low-to-medium density are restored by the double weighted VMF, where the term double weighted means that the pixels' spatial distance and magnitude value are weighted together for the vector ordering in the computation of vector median filtering. In addition, the corrupted pixels of high density are restored by the SMF based on M estimator and the neighbourhood processed pixels. According to the estimated noise density, the proposed SMF and VMF are switched adaptively. The experimental results show that the new algorithm can filter the noise effectively while protecting the image colour, contrast and fine details well for the impulsive noise of different density (even as high as 99%).

关键词： pixel spatial distance magnitude value Filtering methods in signal processing image colour analysis vector median filtering computer vision and image processing techniques noisy colour images image restoration impulse noise Optical, image and video signal processing median filters weighted directional operators noise detection two-level noise estimation adaptive filters second-level estimation colour channel noise restoration image denoising uncorrupted pixels impulsive noise adaptive scalar filtering

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Tree-structured Bayesian network learning with application to scene classification

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ELECTRONICS LETTERS 2011年第9期47卷 540-541页

作者： Wang, Z. F. Wang, Z. H. Xie, W. J. Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

A tree-structured Bayesian network is one of the best probabilistic models for scene classification. A simple and successful learning algorithm for a tree-structured Bayesian network classifier is presented without taking arc reversal into account. A systematic performance study on the Lazebnik 15 dataset shows the proposed method is more efficient than the traditional learning scheme.

关键词： Optical, image and video signal processing learning (artificial intelligence) File organisation scene classification belief networks Lazebnik dataset Knowledge engineering techniques tree data structures tree-structured Bayesian network classifier learning algorithm image classification computer vision and image processing techniques probabilistic models

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image enlargement via interpolatory subdivision

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IET image processing 2011年第6期5卷 567-571页

作者： Liu, C. Luo, X. Sun Yat Sen Univ Sch Informat Sci & Technol Guangzhou 510275 Guangdong Peoples R China Natl Engn Res Ctr Digital Life Guangzhou Guangdong Peoples R China Minist Educ Engn Res Ctr Digital Life Guangzhou Guangdong Peoples R China

A novel image enlargement method based on a modified interpolatory subdivision scheme is proposed in this study. The subdivision scheme is a modification from the 4-point interpolatory subdivision by substituting the interpolation rule for a tangent-constrained Hermite interpolation and in surface case the subdivision is derived from a Ferguson patch. By estimating the gradients of the Ferguson patch, the authors present an image enlargement algorithm preserving the sharp edges. Benefit from the advantages of subdivision, this algorithm runs fast. Numerical experiments illustrate the efficiency of the novel method.

关键词： image enlargement image resolution inverse problems computer vision and image processing techniques tangent-constrained Hermite interpolation wavelet transforms interpolatory subdivision Integral transforms Optical, image and video signal processing image enhancement Mathematical analysis Ferguson patch

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Noise adaptive switching median-based filter for impulse noise removal from extremely corrupted images

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IET image processing 2011年第5期5卷 472-480页

作者： Fabijanska, A. Sankowski, D. Tech Univ Lodz Dept Comp Engn PL-90924 Lodz Poland

In this study an approach to impulse noise removal is presented. The introduced algorithm is a switching filter which identifies the noisy pixels and then corrects them by using median filter. In order to identify pixels corrupted by noise an analysis of local intensity extrema is applied. Comprehensive analysis of the algorithm performance [in terms of peak signal-to-noise ratio (PSNR) and Structural SIMilarity (SSIM) index] is presented. Results obtained on wide range of noise corruption (up to 98%) are shown and discussed. Moreover, comparison with well-established methods for impulse noise removal is provided. Presented results reveal that the proposed algorithm outperforms other approaches to impulse noise removal and its performance is close to ideal switching median filter. For high noise densities, the method correctly detects up to 100% of noisy pixels.

关键词： median filters extreme corrupted image noisy pixels noise adaptive switching median-based filter SSIM image denoising computer vision and image processing techniques adaptive filters noise density impulse noise removal PSNR Filtering methods in signal processing Optical, image and video signal processing structural similarity peak signal-to-noise ratio comprehensive analysis

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Real-time physiological and vision monitoring of vehicle driver for non-intrusive drowsiness detection

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IET COMMUNICATIONS 2011年第17期5卷 2461-2469页

作者： Lee, B. -G. Jung, S. -J. Chung, W. -Y. Pukyong Natl Univ Dept Elect Engn Pusan 608737 South Korea

This study presents a novel approach to detect driver's drowsiness by applying two distinct methods in computer vision and image processing. The objective of this study is to combine both methods under one single profile instead of relied solely on a detection method to enhance the driver's drowsiness detection resolution. Therefore a non-intrusive drowsy-monitoring system is developed to alert the driver if driver falls into low arousal state. In physiological part, photoplethysmography (PPG) is analysed for its changes in signals waveform from awake to drowsy state. Meanwhile, eyes pattern or motion in image processing is addressed to detect driver fatigue. Genetic algorithm with template-matching approach is designed to detect eye region and estimate the drowsiness in different metric standard based on eyes behaviour. Moreover, PPG drowsy signals are integrated with eyes motion to derive the final probability model for delivering valid and reliable drowsiness detection system. Indeed, the proposed system provides high competitive edge over existing arbitrary drowsiness detection system where the driver's health and mental states can be monitored in real-time without constraints.

关键词： computer vision and image processing techniques vision monitoring genetic algorithm physiology Optical, image and video signal processing photoplethysmography vehicle driver nonintrusive drowsiness detection real-time physiological monitoring template-matching approach image processing genetic algorithms computer vision

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Improved bi-dimensional empirical mode decomposition based on 2D-assisted signals: analysis and application

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IET image processing 2011年第3期5卷 205-221页

作者： Xu, G. L. Wang, X. T. Xu, X. G. Dalian Naval Acad Dept Nav Dalian 116018 Peoples R China Dalian Naval Acad Dept Automatizat Dalian 116018 Peoples R China Dalian Naval Acad Inst Photoelect Technol Dalian 116018 Peoples R China

Mode mixing, boundary effects, necessary extrema lacking and so on are the main problems involved in bi-dimensional empirical mode decomposition (BEMD). The study presents an improved BEMD based on 2D-assisted signals: 2D Gaussian noises. Firstly, the given 2D Gaussian noise and its negative counterpart are added to the original image, respectively, to construct the two images to be decomposed. Secondly, the decomposed intrinsic mode functions (IMFs) from the two images are added together to obtain the IMFs, in which the added noises are cancelled out with less mode mixing and boundary effects. The other contribution of the method lies in its overcoming of the problem of necessary extrema lacking that the previous BEMD fails. Some instructive conclusions are obtained in the improved BEMD. Lastly, the efficiency and performance of the method are given through theoretical analysis and its application in image enhancement, which outperforms some previous approaches.

关键词： 2D Gaussian noise decomposed intrinsic mode functions Gaussian noise computer vision and image processing techniques bidimensional empirical mode decomposition image enhancement Other topics in statistics Optical, image and video signal processing 2D-assisted signals

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Eigen-based traffic sign recognition

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IET INTELLIGENT TRANSPORT SYSTEMS 2011年第3期5卷 190-196页

作者： Fleyeh, H. Davami, E. Dalarna Univ Dept Comp Sci Sch Technol & Business Studies S-78188 Borlange Sweden

This study's purpose is to introduce eigen-based traffic sign recognition. This technique is based on invoking the principal component analysis (PCA) algorithm to choose the most effective components of traffic sign images to classify an unknown traffic sign. A set of weights are computed from the most effective eigen vectors of the traffic sign. By using the Euclidean distance, unknown traffic sign images are then classified. The approach was tested on two different databases of traffic sign's borders and speed limit pictograms that were extracted automatically from real-world images. A classification rate of 96.8 and 97.9% was achieved for these two databases. To check the robustness of this approach, non-traffic sign objects and occluded signs were invoked. A performance of 71% was achieved when occluded signs are used. When signs were rotated 10 degrees around their centre, the performance became 89% when traffic signs' outer shapes were used and for rotated speed limit pictograms the result was 80%.

关键词： driver support systems intelligent vehicles Traffic engineering computing image recognition traffic sign borders object recognition driver information systems eigen-based traffic sign recognition computer vision and image processing techniques image classification principal component analysis geometry Euclidean distance Other topics in statistics Combinatorial mathematics principal component analysis algorithm speed limit pictograms traffic sign image classification

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Theoretical and experimental study of a signal feature extraction algorithm for measuring propeller acceleration in a port surveillance system

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IET RADAR SONAR AND NAVIGATION 2011年第2期5卷 172-181页

作者： Tao, R. Feng, Y. Wang, Y. Beijing Inst Technol Dept Elect Engn Beijing 10081 Peoples R China

Classification and identification of moving vessels by extracting particular features of the underwater radiated noise generated by the propellers for use within a port area are an attractive topic for a port underwater surveillance system. The authors first present a chirp-periodic signal model for the envelope of accelerating propeller noise, then derive the maximum-likelihood estimator (MLE) for the evaluation of acceleration and finally propose a novel algorithm for the extraction of acceleration feature on modulated noise (EAFOMN) of the propellers. In addition, a series of experiments were performed for an accelerating propeller in a large cavitation tunnel. The results of the tests show that the actual signal features of accelerating propeller noise is consistent with the presented theoretical analysis, and the proposed algorithm is efficient for passively extracting acceleration of propeller rotation speed.

关键词： computer vision and image processing techniques maximum likelihood estimator underwater radiated noise moving vessel classification acceleration measurement underwater surveillance maximum likelihood estimation port surveillance signal feature extraction Optical, image and video signal processing modulated noise propeller acceleration measurement cavitation tunnel feature extraction Velocity, acceleration and rotation measurement moving vessel identification Video signal processing propellers video surveillance propeller rotation speed chirp-periodic signal Other topics in statistics

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image registration based on criteria of feature point pair mutual information

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IET image processing 2011年第6期5卷 560-566页

作者： Lu, G. Yan, J. Kou, Y. Zhang, J. Xiamen Univ Dept Commun Engn Xiamen 361005 Peoples R China Shantou Univ Dept Elect Engn Shantou 515063 Peoples R China

Similarity measurement based on mutual information maximisation has been successful applied in image registration. However, it costs a lot of computation time and the interference of local maxima in the search process always makes the registration search into local maxima that may cause misregistration. In order to eliminate these shortcomings, a novel image registration method is presented in this study. In the method wavelet multi-scale product is calculated to extract the feature points and angle information of the two input images, then a new criterion of registration - criterion of feature point pair mutual information - is defined to acquire corresponding matching points. In the experiments, our method and image registration methods based on correlation criteria and alignment metric criteria are tested for comparison. Experimental results show that the registration of our method outperforms that of the other two methods and the registration errors are obviously reduced. The errors of coordinates are lower compared to the errors of 78% of the other two methods and the errors of rotation angle are lower compared to the errors of 6% of the other two methods. The seams of the registration are very smooth and the transition zones are very uniform.

关键词： correlation methods correlation criteria computer vision and image processing techniques image registration method feature point pair mutual information wavelet transforms image registration alignment metric criteria similarity measurement Optical, image and video signal processing Integral transforms wavelet multiscale product mutual information maximisation local maxima

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Visual tracking of partially observable targets with suboptimal filtering

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IET computer vision 2011年第1期5卷 1-13页

作者： Xu, M. Ellis, T. Godsill, S. J. Jones, G. A. Xian Jiaotong Liverpool Univ Dept Elect & Elect Engn Suzhou 215123 Jiangsu Peoples R China Kingston Univ Digital Imaging Res Ctr Kingston upon Thames KT1 2EE Surrey England Univ Cambridge Dept Engn Cambridge CB2 1PZ England

A comparative study of four suboptimal tracking algorithms that can cope with missing measurements of low-level visual features during partial occlusion is presented. An approach to identify missing measurements in two-dimensional object tracking is formulated. The comparison starts from a symbolic analysis in Kalman filtering and ends with a performance evaluation in Monte Carlo simulations in which the objects manoeuvre and undergo moderate size variations during occlusion. It is found that the algorithm for estimating unobservables from observables outperforms the others in terms of mean square error, robustness and readiness for implementation.

关键词： Kalman filtering two-dimensional object tracking Kalman filters Monte Carlo simulation suboptimal filtering suboptimal tracking algorithm partial occlusion object detection Interpolation and function approximation (numerical analysis) computer vision computer vision and image processing techniques mean square error Filtering methods in signal processing Optical, image and video signal processing mean square error methods partially observable target visual tracking Monte Carlo methods symbolic analysis

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