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检索条件"任意字段=International Congress on Image and Signal Processing, BioMedical Engineering and Informatics"

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Digital watermarking based on interleaving extraction block compressed sensing in Contourlet domain 9

Digital watermarking based on interleaving extraction block ...

引用

9th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2016

作者： Bi, Hongbo Zhao, Chunhui Liu, Ying Li, Ning College of Information and Communication Engineering Harbin Engineering University Harbin China School of Electronic and Information Engineering Northeast Petroleum University Daqing China

ISBN: (纸本)9781509037100

Traditional digital watermarking in transform domain computes complex and has limited anti-attack. Different ways of signal sparsity of compressed sensing represent different domains, which expands the embeddable space of watermarking. To take advantage of its multi-direction and multi-resolution characteristics to sparse image, Contourlet transform was analyzed. For the limitation of traditional block decision, we adopt the interleaving extraction to reduce the block artifacts. Furthermore, we presented the watermarking algorithm based on interleaving extraction block compressed sensing in Contourlet domain, simulation results verifies that the algorithm enhances the peak signal to noise ratio. Attack tests show that the proposed digital watermarking has better robustness against JPEG compression, noising, cutting etc. © 2016 IEEE.

关键词： Extraction

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Infrared image Saliency Detection based on Human Vision and Information Theory 9

Infrared Image Saliency Detection based on Human Vision and ...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Qin, Shiyu Wang, Lan Cheng, Hua Feng, Qi Zhang, Minwen Gao, Chenqiang Chongqing Univ Posts & Telecommun Key Lab Signal & Informat Proc Chongqing Chongqing Peoples R China China Ship Dev & Design Ctr Wuhan Peoples R China

ISBN: (纸本)9781509037100

Robust image saliency detection can process the image correctly without any prior knowledge and additional assumptions. Therefore, the saliency detection is still one of the important steps in the field of computer vision including object recognition and tracking, image and video encoding and image segmentation. Although infrared imaging has extensive applications, there is few saliency extraction algorithms based on infrared spectroscopy. We propose an infrared image- based saliency extraction algorithm based on human vision and information theory. The proposed algorithm uses both human visual attention mechanism and theory of information, and it can also produce a saliency image with full resolution. The detection results of the proposed algorithm get a higher accuracy and better recall rate, when tested on one of the largest infrared data sets which is publicly now and a data set created by ourselves.

关键词： Infrared image Saliency Detection Human Vision Information Theory

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A Kernel Fuzzy Clustering Infrared image Segmentation Algorithm Based on Histogram and Spatial Restraint 9

A Kernel Fuzzy Clustering Infrared Image Segmentation Algori...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Li, Shaoyi Ma, Jun Northwestern Polytech Univ Sch Marine Sci & Technol Xian Peoples R China China Ordnance Ind Res Inst 203 Xian Peoples R China

ISBN: (纸本)9781509037100

Because the contrast of the image for guiding the highspeed infrared air-to-air missile is low, its signal to noise ratio is poor and the target and its background gray-scale coupling is strong, the paper analyzes the reasons why the threshold value segmentation method and the fuzzy C-means clustering method have the over-segmentation and under-segmentation in segmenting the above type of image. Hence we propose the kernel fuzzy clustering segmentation algorithm based on histogram and spatial constraint, which utilizes the global first-moment histogram of the infrared image to restrict the number of clusters and the clustering center, improves the spatial correlation function that fully manifests the correlations among pixels inside a neighbor domain and reconstructs the membership degree matrix and the clustering central function, thus segmenting the infrared image with the kernel fuzzy clustering algorithm. The results on the experiments on a sequential infrared image show preliminarily that, compared with the traditional threshold value segmentation algorithm, the fuzzy C-means segmentation algorithm and the kernel fuzzy clustering algorithm, the improved algorithm proposed in the paper can reduce entropy segmentation by about 60% on average and increase the correlation degrees among clusters by around 10%, thus enhancing to a certain extent the efficiency and precision for segmenting the fuzzy image whose target gray-scale and background gray-scale are strongly coupled.

关键词： segmentation algorithm image processing gray-scale histogram infrared image spatial constraint kernel fuzzy clustering

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Joint Subspace Learning for Reconstruction of 3D Facial Dynamic Expression from Single image 9

Joint Subspace Learning for Reconstruction of 3D Facial Dyna...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Seo, Masataka Chen, Yen-Wei Ritsumeikan Univ Coll Informat Sci & Engn Kusatsu Shiga Japan

ISBN: (纸本)9781509037100

Recently, the synthesis of 3D dynamic expressions has become an important concern in computer graphics, facial recognition, etc. In this study, we propose a regression based joint subspace learning method for the automatic synthesis of 3D dynamic expression images. This method synthesizes 3D dynamic expression images from a single 2D facial image. We use two subspaces (the view subspace and the frame subspace) to synthesize a 3D image. First, we use the view subspace to estimate multi-view facial images from a front image. Next, we construct a 3D image using the estimated multi-view facial images. Finally, we estimate the 3D images in different frames by using the frame subspace to synthesis 3D dynamic expression images. This approach is unlike the conventional joint subspace learning in which, the coefficients estimated by the input image are directly used for synthesis. Furthermore, we propose using textural information to improve the accuracy of synthesized images.

关键词： 3D dynamic expressions multi-view facial shape joint subspace learning regression

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A detection algorithm of ballast track fastener loss based on shooting direction 9

A detection algorithm of ballast track fastener loss based o...

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9th international congress on image and signal processing, biomedical engineering and informatics, CISP-BMEI 2016

作者： Wang, Mingyu Zhang, Xinfeng College of Electronic and Control Engineering Beijing University of Technology Beijing China Engineering Research Center of Digital Community Ministry of Education Beijing China Beijing Laboratory for Urban Mass Transit Beijing China

ISBN: (纸本)9781509037100

In this paper, we propose a new positioning and automatic detection algorithm of ballast track fastener missing, which is based on different shooting direction. The crossover method based on Fast-Kirsch edge detection arithmetic and HOUGH straight line detection is presented for the direction-based positioning algorithm. It has very strong transplantability without the effect of shadow, inclination of track, obstacles and in relatively accurate location areas for missing fastener scanning, FAST-PCA is used as the feature extraction method of identification after the comparison of several algorithms. © 2016 IEEE.

关键词： Edge detection

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A Case Study Of BoVW For Keyword Spotting On Historical Mongolian Document images 9

A Case Study Of BoVW For Keyword Spotting On Historical Mong...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Guo, Xing Wei, Hongxi Su, Xiangdong Inner Mongolia Univ Sch Comp Sci Hohhot 010021 Peoples R China

ISBN: (纸本)9781509037100

This paper proposes a Bag of Visual Words (BoVW) based approach for keyword spotting on the Mongolian historical document images. In this paper, the first step is dividing the scanned Mongolian historical document images into word images by some preprocessing steps, such as connected component analysis, binarization etc. Then, all of image in our training set are processed in the following steps, including extracting keypoints, obtaining local descriptors and formulating visual word. Finally, each word image can be represented as a histogram of visual words by a codebook. In the retrieval stage, a provided query keyword image is also converted into a histogram of visual words through the above-mentioned procedure. After that, similarities between a query keyword image and whole candidate of word images can be calculated. Therefore, a sorted list will be returned in descending order of the similarities. Moreover, spatial information of visual word is introduced into the original framework of BoVW by the spatial pyramid matching (SPM) technology. Experimental results show that addition of spatial information obtains a good performance on our dataset.

关键词： Bag of Visual Words Keyword Spotting Document image Retrieval SIFT Descriptors

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An Exponent-based Anisotropic Variational PDE Regularized image Reconstruction Method for Digital Tomosynthesis 9

An Exponent-based Anisotropic Variational PDE Regularized Im...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Liu, Yangchuan Gao, Xin Tang, Yuguo Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Med Imaging Dept Suzhou Peoples R China Chinese Acad Sci CAS Key Lab Biomed Diagnost Suzhou Inst Biomed Engn & Technol Suzhou Peoples R China

ISBN: (纸本)9781509037100

A novel image reconstruction method based on exponent-based anisotropic variational partial differential equation (PDE) for digital tomosynthesis (DT) is proposed. An exponent map instead of fixed values 1 or 2 is used as the order of total variation (TV), which can balance staircase artifact and over smoothing. The smoothed structure tensor (SST) capable of characterizing of different image features is used to build the exponent map, which has good ability of noise suppressing as well as detail preserving. Numerical experiment demonstrates that our method achieves better performance in many aspects (such as noise level, structure similarity) than existing methods, including adaptive steepest descent-projection onto convex sets (ASD-POCS) and selective diffusion regularized simultaneous algebraic reconstruction technique (SART).

关键词： tomosynthesis structure tensor PDE image reconstruction total variation

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A Hybrid Extraction-Classification Method For Brain Segmentation In MR image 9

A Hybrid Extraction-Classification Method For Brain Segmenta...

引用

9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Lu, Siyu Lei, Ling Huang, Hui Xiao, Liang Beijing Univ Chem Technol Beijing Peoples R China Chinese PLA 309th Hosp Beijing Peoples R China

ISBN: (纸本)9781509037100

In this paper, we presented a hybrid method to distinguish normal brain tissue from lesion regions based on the T1-weighted and T2-weighted MR images of the same anatomic structure. Regions of interest were extracted using an iterative Otsu's thresholding method with normal brain tissue extracted from T1-weighted MR images, and lesion regions extracted from T2-weighted MR images. Markov random field and maximum-aposteriori approaches were used to classify areas dually identified as normal brain tissue in T1-weighted images and lesion regions in T2-weighted images. The approach was validated using synthetic and real MR images to demonstrate its ability to clearly distinguish normal brain tissue from lesion regions.

关键词： Medical imaging Brain segmentation Otsu's thresholding method Markov random field Maximum-aposteriori

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A Novel Noise Reduction Method for Optical Coherence Tomography images 9

A Novel Noise Reduction Method for Optical Coherence Tomogra...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Liu, Xiaoming Yang, Zhou Wang, Jia Wuhan Univ Sci & Technol Coll Comp Sci & Technol Wuhan 430065 Peoples R China Hubei Prov Key Lab Intelligent Informat Proc & Re Wuhan 430065 Peoples R China

ISBN: (纸本)9781509037100

In the field of image processing, compared with noisy image, high-quality or clean image usually has a positive impact on the final results, which lead to the emergence of image denoising. Recently, the patch-based methods have attracted considerable interest of researchers, which leverage the self-similarity of the noisy image or information of other relevant images. In this paper, we proposed a novel patch-based method to reduce the noise in optical coherence tomography (OCT) image, which consist of two steps. A combination of internal denoising and external denoising is adopted to select similar patches for each patch in the noisy image. Next, we use low rank technique to denoise the group of the noisy patch and its similar patches. After all patches in a noisy image are denoised, the corresponding clean image is constructed by aggregating all denoised pixels and the first step denoising is accomplished. A new noisy image is constructed and taken as the input of the second step denoising. Experimental results demonstrate that our method achieves better effect and performance, compared with the existing state-of-the-art methods.

关键词： low rank optical coherence tomography image denoising self-similarity

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image Denoising based on Spatially Adaptive High Order Total Variation Model 9

Image Denoising based on Spatially Adaptive High Order Total...

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9th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Deng, Lizhen Fang, Qi Zhu, Hu Zhou, Gang Nanjing Univ Posts & Telecommun Sch Telecommun & Informat Engn Nanjing 210003 Jiangsu Peoples R China Huazhong Univ Sci & Technol Sch Automat Wuhan 430074 Peoples R China

ISBN: (纸本)9781509037100

In order to reduce the staircasing effect of total variation (TV) denoising results, the high order difference is regarded as the regularization item in the high order total variation model (HOTV). The degradation rate of HOTV is faster than the degradation rate of TV in the edge area of the image, so the edge details are lost in the HOTV model while the noise are removed. For reducing the staircasing effect and preserving the edge details effectively, spatially adaptive HOTV model is introduced which difference eigenvalue is as improved adaptive coefficient. The experiment results have demonstrated that the new spatially adaptive HOTV model can preserve detail information and reduce the staircasing effect, at the same time, the de-noising rate and visual effect are superior to both of them.

关键词： image denoising Spatially adaptive high order total variation Difference eigenvalue Split Bregman iteration

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