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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Lei ZHOU YiJun LI YiPeng SONG Yu QIAO Jie YANG Institute of Image Processing and Pattern Recognition Shanghai Jiaotong University China

ISBN: (纸本)9781479928941

In this work, a novel salient object detection method is proposed based on the saliency driven clustering. To capture visual patterns of an image, the color contrast prior and boundary prior are utilized to generate the image clusters automatically. Then, a simple operation like regional saliency computation is applied to refine the saliency maps generated by two priors. The final saliency map are obtained by combining the refined contrast prior saliency and boundary prior saliency. Extensive experiments show that our proposed model achieves better performance on salient region detection against the state-of-the-art methods.

关键词： image color analysis Visualization Computational modeling Conferences pattern recognition Computer vision Object detection

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Adaptive multi-level region merging for salient object detection 25

Adaptive multi-level region merging for salient object detec...

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25th British Machine Vision Conference, BMVC 2014

作者： Fu, Keren Gong, Chen Yun, Yixiao Li, Yijun Gu, Irene Yu-Hua Yang, Jie Yu, Jingyi Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden University of Delaware Newark United States

Most existing salient object detection algorithms face the problem of either under-or over-segmenting an image. More recent methods address the problem via multi-level segmentation. However, the number of segmentation levels is manually predetermined and only works well on specific class of images. In this paper, a new salient object detection scheme is presented based on adaptive multi-level region merging. A graph-based merging scheme is developed to reassemble regions based on their shared contour strength. This merging process is adaptive to complete contours of salient objects that can then be used for global perceptual analysis, e.g., foreground/ground separation. Such contour completion is enhanced by graph-based spectral decomposition. We show that even though simple region saliency measurements are adopted for each region, encouraging performance can be obtained after across-level integration. Experiments by comparing with 13 existing methods on three benchmark datasets including MSRA-1000, SOD and SED show the proposed method results in uniform object enhancement and achieves state-of-the-art performance. © 2014. The copyright of this document resides with its authors.

关键词： Merging

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image quality assessment with mean squared error in a log based perceptual response domain

Image quality assessment with mean squared error in a log ba...

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IEEE China Summit & International Conference on Signal and Information processing (ChinaSIP)

作者： Wufeng Xue Xuanqin Mou Institute of Image Processing and Pattern Recognition Xi'an Jiaotong University Xi'an China

Up to now, there existing a lot of models that predict subjective quality of the contents of natural images which have undergone some unknown distortion procedures. These models, no matter fall in to the bottom-up mechanism or belong to the top-down functional modelling, fail to provide an easy-applied and reliable solution. The complex computation procedure prevents them from being widely used in related image processing areas such as image enhancement, image reconstruction and video coding. In the present work, we start from a two stage nonlinear perception model, which transforms the input image into a decorrelated one and then further reduces the redundancy between neighboring pixels by another nonlinear normalization procedure which transforms the previous output into a perceptual response domain. The final quality prediction is computed as the Euclid distance of the reference image and the distorted one in this response domain, this will make the new model be readily applied in other areas.

关键词： Abstracts Indexes PSNR Visualization image processing

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A robust method for high-accuracy microbe identification based on image processing

A robust method for high-accuracy microbe identification bas...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Mingqi Cao Yuncai Liu Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Shanghai China

ISBN: (纸本)9781479920327

We present a novel fast method based on computer vision to identify microbe The proposed method is simple but absolutely effective It combines approximate parallel light source and industrial camera, to automatically accomplish the bacteria identification and monitor the growing states of bacteria during the progress of a drug sensitive test. Based on this method, the color information and turbidity information, which reflect the primary information of drug sensitive tests, can be obtained fast, while processing efficiency can be as high as hundreds of milliseconds per frame. The performance of our method is significantly accurate and robust.

关键词： Feature extraction image color analysis Drugs Monitoring image edge detection Noise Media

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Low energy-consuming cluster-based algorithm to enforce integrity and preserve privacy in data aggregation

Low energy-consuming cluster-based algorithm to enforce inte...

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13th International Symposium on Distributed Computing and Applications to Business,Engineering & Science（DCABES 2014）

作者： Zhengwei Guo Xiaojiao Ding College of Computer & Information Engineering Institute of Image Processing & Pattern Recognition Henan University

This paper proposes a low energy-consuming cluster-based algorithm to protect data integrity and privacy named ILCCPDA, which can dynamically elect cluster head by LEACH clustering protocol and take the simple cluster fusion approach to reduce the data transmission, thus reducing energy consumption. ILCCPDA can detect data integrity by adding homomorphic message authentication code and take the random key distribution mechanism for data encryption. It can solve the problem of the integrity, privacy and energy consumption in the wireless transmission of sensor data.

关键词： wireless sensor networks(WSN) data aggregation privacy protection

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Ensemble Kalman filter based on weights optimization of sampling particles

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Journal of Computational Information Systems 2014年第19期10卷 8355-8362页

作者： Hu, Zhentao Zhang, Yong Hu, Yumei Institute of Image Processing and Pattern Recognition Henan University Kaifeng China Finance Department Henan University Kaifeng China

Based on the thought of ensemble forecast, Ensemble Kalman filter (EnKF) gives a typical implementation of Bayesian estimation in Monte-Carlo simulation. However, the sampling process of particles excessively relies on the priori modeling information form the system state transition in EnKF, which inevitably causes the particle degeneracy phenomenon. In this paper, we propose a novel Ensemble Kalman filtering algorithm based on weights optimization of sampling particles. Firstly, combining with the importance-sampling technique, the contribution degree of state estimation from particles is effectively measured. Secondly, by increasing particles numbers with high weights and decreasing particles numbers with low weights, the sampling particles set is optimized in the global sense. In addition, the estimated method of importance weights on the basis of virtual observation is constructed in the framework of EnKF, and the adverse effects on the reliability and stability of particle weights caused by the observation random noise are improved. The experimental results show the feasibility and efficiency of the proposed algorithm.

关键词： Importance sampling

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Mean Shift Based Feature Points Selection Algorithm of DSA images

Mean Shift Based Feature Points Selection Algorithm of DSA I...

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第三届健康信息学国际学术会议（HIS 2014）

作者： Fan Zhang Congcong Li Shan Kong Shuyue Liu Yanbin Cui School of Computer and Information Engineering Henan University Institute of Image Processing and Pattern Recognition Henan University

In Digital Subtraction Angiography(DSA) image registration algorithm,the precision of the control points as well as their number and the distribution in image determine the accuracy of geometric correction and registrationControl points usually adopt the grid points;however,a more effective method is to extract control points adaptively according to the image featureIn this paper,a control point's selection algorithm of DSA images is proposed based on adaptive multi-Scale vascular enhancement,error diffusion and means shift algorithmsExperimental results show that the proposed algorithm can adaptively put the control points to blood vessels and other key image characteristics,and can optimize the number of control points according to practical needs,which will ensure the accuracy of DSA image registration.

关键词： Digital subtraction angiography,Error diffusion,Mean shift

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SALIENCY DETECTION BASED ON EXTENDED BOUNDARY PRIOR WITH FOCI OF ATTENTION

SALIENCY DETECTION BASED ON EXTENDED BOUNDARY PRIOR WITH FOC...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Yijun Li Keren Fu Lei Zhou Yu Qiao Jie Yang Bai Li Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China University of Nottingham UK

ISBN: (纸本)9781479928941

In this paper, we propose a novel bottom-up paradigm for detecting visual saliency. Regarding the boundary as potential background (boundary prior), we firstly transfer the input color image into a graph with additional four virtual nodes. With a new type of edge called feature edge defined considering both color information and spatial distribution, geodesic saliency measure is used to obtain four saliency maps. Then a combination strategy of four maps is proposed, rendering a uniform saliency map to better suppress background and avoid over-suppression of salient object. Finally, we introduce a way of determining foci of attention based on maximal deviation from norm (MDN) to enhance the quality of saliency map. Experimental results on a benchmark dataset demonstrate the better performance of our proposed approach compared with several state-of-art methods.

关键词： color image maximum error maps chromatic information boundary layer focused attention

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Low energy-consuming cluster-based algorithm to enforce integrity and preserve privacy in data aggregation

Low energy-consuming cluster-based algorithm to enforce inte...

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The 13th International Symposium on Distributed Computing and Applications to Business,Engineering and Science(DCABES 2014)(第十三届分布式计算及其应用国际学术研讨会)

作者： Zhengwei Guo Xiaojiao Ding College of Computer & Information Engineering Institute of Image Processing & Pattern Recognition Henan University KaifengChina

ISBN: (纸本)9781479941681

This paper proposes a low energy-consuming cluster-based algorithm to protect data integrity and privacy named ILCCPDA,which can dynamically elect cluster head by LEACH clustering protocol and take the simple cluster fusion approach to reduce the data transmission,thus reducing energy *** can detect data integrity by adding homomorphic message authentication code and take the random key distribution mechanism for data *** can solve the problem of the integrity,privacy and energy consumption in the wireless transmission of sensor data.

关键词： wireless sensor networks(WSN) data aggregation privacy protection

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A novel level set method for remote sensing image based on K-means

A novel level set method for remote sensing image based on K...

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2014 International Conference on Applied Mechanics, Mechatronics and Intelligent System, AMMIS 2014

作者： Xu, Er Jing Jia, Zhen Hong Wang, Lie Jun Hu, Ying Jie Yang, Jie College of Information science and engineering Xinjiang University Urumchi 830046 China Knowledge Engineering and Research Discovery Institute Auckland University of Technology Auckland New Zealand The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai 200240 China

ISBN: (纸本)9783038351757

Due to the characteristic of remote sensing image, we propose a novel method based on K-means algorithm also with the improved multi-phrase level set model. Comparing with the classical multi-phase C-V model, the improved model considers the region area information, gradient information and edge detection. Proper use of gradient information can overcome the inaccurate edge localization defects in image segmentation. The edge detection is used for keeping the boundary information better in the evolution process. For the reason of picking up the contour's convergent speed and enable the avoidance of trap. Four stages are constructed. Firstly, a median filtering is applied to smooth the original image and reduce parts of noise. Secondly, the usage of K-means algorithm gains more obvious differences of characteristics. Next, the reconstruction of gradient is obtained by using Sobel operator. Finally, segmentation result is achieved by using an improved method of multi-phase level set image segmentation. Experimental results show that the proposed approach has advantages in rapid and efficient application of remote sensing image segmentation. © (2014) Trans Tech Publications, Switzerland.

关键词： image segmentation

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