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Multifocus image fusion using modified pulse coupled neural network for improved image quality

IET image processing 2010年第6期4卷 443-451页

作者： Agrawal, D. Singhai, J. MANIT Dept Elect & Commun Engn Bhopal MP India

The present day camera systems have the limitation of acquiring the clearer image of a scene having objects at different distances. This limitation can be overcome by fusion of multiple images of the scene taken with different camera settings. The fusion of these images comes under the category of multifocus image fusion. In the existing method of image, fusion partitioned source image blocks are fused by pulse coupled neural network (PCNN) based on their clarity measure. PCNN plays an important role in the image fusion process in choosing the best-quality image block for fused image. Fusion method becomes tedious and time consuming because of the inherent complexity of PCNN. In this study, a modified approach of PCNN suitable for application in image fusion technique is proposed by reducing the processing time and computational complexity. The modifications proposed are in linking and feeding field of PCNN. This study presents a method for multifocus image fusion by using modified PCNN (MPCNN) with spatial frequency (SF) and energy of Laplacian (EOL) as clarity measures. The proposed method of image fusion using MPCNN results in better quality of fused image with reduced root mean square error (RMSE) and computational time requirements as compared to conventional PCNN.

关键词： Neural computing techniques pulse coupled neural network image quality scene image acquisition spatial frequency neural nets best-quality image block EOL image processing computer vision and image processing techniques image fusion Optical, image and video signal processing multifocus image fusion energy of Laplacian day camera system root mean square error method Interpolation and function approximation (numerical analysis) Computational complexity computational complexity mean square error methods cameras modified PCNN

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Fusion of panchromatic and multispectral images using temporal Fourier transform

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IET image processing 2010年第4期4卷 255-260页

作者： Mahyari, A. G. Yazdi, M. Shiraz Univ Fac Elect & Comp Engn Shiraz Iran

image fusion techniques can be used to enhance the resolution of multispectral (MS) image, which is helpful for categorisation, recognition as well as other decision making processes. In this paper, a new class of image fusion algorithms is proposed that decomposes images into similar and non-similar information and fuses the corresponding non-similar one. It is based on temporal Fourier analysis. The details of panchromatic (PAN) image are fused with details of MS image by eliminating their similar information which is done by high-pass filtering of their temporal Fourier transform. The cut-off frequency of this filtering is obtained adaptively based on the input MS and PAN images. It results in minimum spectral and spatial distortion. The experimental results demonstrate the superior performance of the proposed method

关键词： image resolution PAN images cut-off frequency multispectral image resolution Filtering methods in signal processing spectral analysis high pass filtering computer vision and image processing techniques image recognition panchromatic images image fusion temporal Fourier transform Optical, image and video signal processing Integral transforms image classification decision making processes filtering theory Fourier transforms image fusion algorithm MS images

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Secure private fragile watermarking scheme with improved tampering localisation accuracy

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IET INFORMATION SECURITY 2010年第3期4卷 137-148页

作者： Bravo-Solorio, S. Gan, L. Nandi, A. K. Aburdene, M. F. Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GZ Merseyside England

In some applications, such as surveillance cameras, it is essential that images acquired with the same device be deemed distinct. To fulfil this requirement, many fragile watermarking methods need to employ a different key for every single image. Keeping track of the correct key associated with each image may become a challenging task as the number of images increases. An existing image index-based scheme as a suitable solution for this problem is revisited, and its security limitations in applications are highlighted where higher localisation accuracy is required. Then, a new fragile watermarking scheme that enhances the localisation accuracy is proposed, while hindering brute force attacks. Experiments demonstrate that the proposed scheme outperforms the state-of-the-art private fragile watermarking methods.

关键词： private fragile watermarking scheme data privacy computer vision and image processing techniques image and video coding image coding brute force attacks watermarking Data security image index-based scheme tampering localisation accuracy

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Urban road user detection and classification using 3D wire frame models

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IET computer vision 2010年第2期4卷 105-116页

作者： Buch, N. Orwell, J. Velastin, S. A. Kingston Univ London Digital Imaging Res Ctr Kingston upon Thames KT1 2EE Surrey England

This study presents a detection and classification system for vehicles and pedestrians in urban traffic scenes. This aims to guide surveillance operators and reduce human resources for observing hundreds of cameras in urban traffic surveillance. The authors perform per frame vehicle detection and classification using 3D models on calibrated cameras. Motion silhouettes (from background estimation) are extracted and compared to a projected model silhouette to identify the ground plane position and class of vehicles and pedestrians. The system is evaluated with the reference i-LIDS datasets from the UK Home Office. Performance for varying numbers of classes for three different weather conditions and for different video input filters is evaluated. The full system including detection and classification achieves a recall of 87% at a precision of 85.5% outperforming similar systems in the literature. The i-LIDS dataset is available to other researchers to compare with our results. The authors conclude with an outlook to use local features for improving the classification and detection performance.

关键词： vehicles road user classification Video signal processing UK Home Office Traffic engineering computing urban traffic scenes image and video coding motion estimation motion silhouettes road vehicles i-LIDS datasets pedestrians 3D wire frame models computer vision and image processing techniques surveillance operators road traffic urban road user detection calibrated cameras video surveillance video signal processing

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Segmentation of DNA microarray images using an adaptive graph-based method

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IET image processing 2010年第1期4卷 19-27页

作者： Karimi, N. Samavi, S. Shirani, S. Behnamfar, P. Isfahan Univ Technol Dept Elect & Comp Engn Esfahan Iran McMaster Univ Dept Elect & Comp Engn Hamilton ON Canada Univ British Columbia Dept Elect Engn Vancouver BC Canada

Microarray is a powerful tool for simultaneous study of the behaviour of thousands of genes through analysis of produced images. The correct segmentation of each 'spot' of the microarray image is a critical step in the analysis of the results of an experiment. A graph-based method is proposed which automatically performs the segmentation. The performance of the algorithm is tested both on real and simulated images. The proposed algorithm successfully detected spots of different sizes and shapes under the presence of variable noise levels. The simulation results proved that the suggested approach has high segmentation accuracy.

关键词： medical image processing Biomedical measurement and imaging Biology and medical computing adaptive graph-based method computer vision and image processing techniques Physics of subcellular structures DNA microarray images Patient diagnostic methods and instrumentation DNA Optical, image and video signal processing genetics image segmentation

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Video sensor network for real-time traffic monitoring and surveillance

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IET INTELLIGENT TRANSPORT SYSTEMS 2010年第2期4卷 103-112页

作者： Semertzidis, T. Dimitropoulos, K. Koutsia, A. Grammalidis, N. Ctr Res & Technol Hellas Informat & Telemat Inst Thessaloniki 57001 Greece

Sensor networks and associated infrastructures become ever more important to the traffic monitoring and control because of the increasing traffic demands in terms of congestion and safety. These systems allow authorities not only to monitor the traffic state at the detection sites, but also to obtain real-time related information (e. g. traffic loads). This study presents a real-time vision system for automatic traffic monitoring based on a network of autonomous tracking units (ATUs) that capture and process images from one or more pre-calibrated cameras. The proposed system is flexible, scalable and suitable for a broad field of applications, including traffic monitoring of tunnels at highways and aircraft parking areas at airports. Another objective of this work is to test and evaluate different image processing and data fusion techniques in order to be incorporated to the final system. The output of the image processing unit is a set of information for each moving object in the scene, such as target ID, position, velocity and classification, which are transmitted to a remote traffic control centre, with remarkably low bandwidth requirements. This information is analysed and used to provide real-time output (e. g. alerts, electronic road signs, ramp meters etc.) as well as to extract useful statistical information (traffic loads, lane changes, average velocity etc.).

关键词： remote traffic control centre autonomous tracking units automatic traffic monitoring computer vision and image processing techniques image processing Video recording data fusion techniques sensor fusion Optical, image and video signal processing Applications of television systems real-time vision system multicamera video surveillance system real-time traffic monitoring video surveillance video sensor network Video signal processing video cameras Sensing devices and transducers wireless sensor networks image processing unit

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Multifocus image fusion based on redundant wavelet transform

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IET image processing 2010年第4期4卷 283-293页

作者： Li, X. He, M. Roux, M. NW Polytech Univ Sch Elect & Informat Xian 710072 Peoples R China Telecom ParisTech Inst TELECOM F-75013 Paris France

image fusion is a process of integrating complementary information from multiple images of the same scene such that the resultant image contains a more accurate description of the scene than any of the individual source images. A method for fusion of multifocus images is presented. It combines the traditional pixel-level fusion with some aspects of feature-level fusion. First, multifocus images are decomposed using a redundant wavelet transform (RWT). Then the edge features are extracted to guide coefficient combination. Finally, the fused image is reconstructed by performing the inverse RWT. The experimental results on several pairs of multifocus images show that the proposed method can achieve good results and exhibit clear advantages over the gradient pyramid transform and discrete wavelet transform techniques.

关键词： computer vision and image processing techniques redundant wavelet transform wavelet transforms feature extraction image fusion Optical, image and video signal processing Integral transforms multifocus image fusion image reconstruction

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Shape from shading using wavelets and weighted smoothness constraints

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IET computer vision 2010年第1期4卷 1-11页

作者： Chen, D. Dong, F. Brunel Univ Sch Informat Syst Comp & Math Uxbridge UB8 3PH Middx England

A new method that allows capturing shapes from an input image using an optimisation-based approach is presented. An objective function is designed by introducing two terms: the first term is used to minimise the difference between the shading of the reconstructed shape and the input image, and the second term is to apply smoothness constraints to the reconstructed shape. To achieve shape reconstruction in high quality, the authors propose weighted smoothness constraint, which is designed to be anti-proportional to the intensity gradients in the input image. Under this constraint,. at image areas make more contribution towards the smoothness of the reconstructed shape, while the. ne details from the image areas with large intensity gradients are preserved in the reconstructed result. Given the objective function, wavelets are used to obtain the solution effectively. Since wavelets accurately preserve high-frequency data, they can be used to solve the objective function with the advantage of allowing for a good recovery of. ne details from the input image. The authors have chosen to use the Daubechies wavelets, which are orthonormal and compactly supported. Here the formulation of the algorithm based on the mathematical details is provided. Finally, the authors present experimental results on a number of different images and compare them against some well-known methods and ground truth (where available). The comparison shows that the method is effective and offers good results.

关键词： objective function shape reconstruction image reconstruction shape from shading smoothing methods image recognition wavelet transforms Integral transforms optimisation weighted smoothness constraint Daubechies wavelets computer vision and image processing techniques shape recognition

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Modelling visual saliency using degree centrality

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IET computer vision 2010年第3期4卷 218-229页

作者： Pal, R. Mukherjee, A. Mitra, P. Mukherjee, J. Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

Visual attention is an indispensable component of complex vision tasks. A multi-scale, complex network-based approach for determining visual saliency is described. It uses degree centrality (conceptually and computationally the simplest among all the centrality measures) over a network of image regions to form a saliency map. The regions used in the network are multiscale in nature with scale selected automatically. Experimental evaluation establishes the superiority of the method over existing saliency methods, even in noisy environments.

关键词： visual saliency modelling visual attention degree centrality Optical, image and video signal processing computer vision computer vision and image processing techniques complex vision tasks complex network-based approach

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Different non-linear diffusion filters combined with triangle method used for noise removal from polygonal shapes

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IET image processing 2010年第4期4卷 313-333页

作者： Zeljkovic, V. Tameze, C. Vincelette, R. Izquierdo, E. Delaware State Univ Appl Math & Theoret Phys Dept Dover DE 19901 USA Univ London Dept Elect Engn London E1 4NS England

A two-step process for removing noise from polygonal shapes is presented in this study. The authors present a polygonal shape as its turning function and then apply a non-linear diffusion filter and a triangle method on it. In the first step the authors apply several different non-linear diffusion filters on the turning function and compare the performance of these filters later. Non-linear diffusion filters identify dominant vertices in a polygon and remove those vertices that are identified as noise or irrelevant features. The vertices in the turning function which diffuse until the sides that immediately surround them approach the same turning function are identified as noise and removed. The vertices that are enhanced are preserved without changing their coordinates and they are identified as dominant ones. After the authors carry this process as far as it will go without introducing noticeable shape distortion, and switch to the triangle method for further removal of vertices that are to be treated as noise. In the second step the authors remove the vertices that form the smallest area triangles. The authors submit experimental results of the tests that demonstrate that this two-step process successfully removes vertices that should be dismissed as noise while preserving dominant vertices that can be accepted as relevant features and give a faithful description of the shape of the polygon. In experimental tests of this procedure the authors demonstrate successful removal of noise and excellent preservation of shape, thanks to appropriate emphasis of dominant vertices.

关键词： shape recognition nonlinear diffusion filter image recognition filters computer vision and image processing techniques nonlinear diffusion equation Filters and other networks turning function triangle method shape distortion

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