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

共 2040 条记录，以下是1331-1340 订阅

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Color Blurred image Restoration Based on Multi-channel Nonlinear Diffusion Model 10

Color Blurred Image Restoration Based on Multi-channel Nonli...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Wu, Nan Wang, Guodong Zhang, Hong Hou, Guojia Huang, Baoxiang Qingdao Univ Coll Comp Sci & Technol Qingdao Shandong Peoples R China

ISBN: (纸本)9781538619377

The blurring of the image will bring great inconvenience to recognize and analyse the content of them. In order to get rid of the blur, many scholars make a lot of contributions. In order to elevate the effect of deblurring, we propose a non-blind restoration variational model for color image deblurring based on the variational method. The proposed method uses the nonlinear diffusion equation as a regular term in the variational energy equation. Besides, we process the image by extending the single channel to the multi-channel, which does well in smoothing the filter and preserving the edge information and furthermore improving the quality of the restored image. In order to accelerate the speed of calculation, the Split Bregman method is introduced. Multiple experiments verify the validity of the proposed method, and the proposed method is superior to traditional algorithms in both visual and objective evaluation.

关键词： Color image deblurring non-blind deconvolution variational method PM term

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a More Precise Multi-modal image Registration Using Self-similarities 10

a More Precise Multi-modal Image Registration Using Self-sim...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Guan, Qiu Jin, Qinqin Long, Haixia Li, Kangjie Hu, Haigen Wang, Xiaoyan Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781538619377

With the development of medical imaging technology, an increasing number of images of different modalities are used in diagnosis and fused together for getting more information. Multi-modality image registration is the key technology of multimodal image fusion. It is also a basic topic in computer vision and widely used in image analysis. However, the images of different modalities would not show the same representation. Therefore, images of different modalities cannot be aligned directly with each other. Currently, a new descriptor which is calculated from different modality images by self-similarities context has been proposed. This descriptor makes images of different modalities align with each other directly. However, it is still affect by noise. In this paper, a new framework of multi-modal registration is proposed to improve the method of multi-modal image registration with self-similarities contexts. Firstly, a series of operations of de-noise is implemented in images before registration. Then a new strategy of filter is proposed in the later image registration. Experiment shows that, this framework of image registration is more robust and precise than the original one.

关键词： image registration Robustness Mutual information biomedical imaging Protocols Magnetic resonance imaging Noise reduction

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image Mosaic Based on SIFT and Morphological Component Analysis 10

Image Mosaic Based on SIFT and Morphological Component Analy...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Chen, Shuai Lu, Yixiang Gao, Qingwei Sun, Dong Xia, Yi Peng, Xueming Anhui Univ Sch Elect Engn & Automat Hefei 230601 Anhui Peoples R China Shanghai Huawei Technol Co Ltd Shanghai 200120 Peoples R China

ISBN: (纸本)9781538619377

Due to traditional image mosaicing resulting in remarkable seams, we propose a novel method to address the problem to make the result more smooth. The approach is constructed based on SIFT algorithm and morphological component analysis(MCA) over trained dictionaries. The two cores of image mosaic are image registration and fusion. First, feature points are extracted by the SIFT algorithm which are used to realize image registration. Then we employ K-SVD algorithm to train an overcomplete dictionary based on the original images and use the MCA to decompose the images so that the decomposed components cartoon, texture can be used to fuse images, respectively. This method leads to a state-of-the-art mosaic performance, meanwhile, the noise contained by original images can also be filtered.

关键词： SIFT image registration trained dictionary K-SVD MCA

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Fall Detection Based on Depth images via Wavelet Moment 10

Fall Detection Based on Depth Images via Wavelet Moment

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Ding, Yupeng Li, Hongjun Li, Chaobo Xu, Ke Guo, Pengzhen Nantong Univ Sch Elect & Informat Nantong Peoples R China

ISBN: (纸本)9781538619377

Fall detection plays an important role in the detection of human abnormal behaviors. In this paper, a fall detection algorithm based on depth images via wavelet moment is proposed. Firstly, we normalize the image according to each pixel in the image relative to the distance from the centroid, then polar coordinate the normalized image. Secondly, Fast Fourier Transform (FFT) of the picture is performed. Thirdly, the feature vectors of the image are extracted by using wavelet transform. Finally, using the minimum distance and Support Vector Machine (SVM) classification methods recognizes human behaviors. Numerous experiments are carried out on a large number of human behavior samples and the averaged success rate of this algorithm is more than 90%. The experimental results show that the proposed algorithm is robust and has good detecting ability and application prospect.

关键词： Fall detection Depth image Wavelet moment

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An Improved Watershed in the Medical image Segmentation Based on the Bi-dimensional Ensemble Empirical Mode Decomposition 10

An Improved Watershed in the Medical Image Segmentation Base...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Li, Fangzhao Peng, Yuxing Lai, Chao Jin, Shiyao Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9781538619377

In the brain MR image segmentation applied the watershed algorithm, there are some problems like the segmented precision is affected by the noise easily and edges are blurred. In order to solving these problems, we improve the watershed algorithm based on the bi-dimensional ensemble empirical mode decomposition (BEEMD), and apply the improved algorithm to the brain MR image segmentation. In this paper, the BEEMD is used to decompose the MR images into multiple BIMFs with different frequency. For different segmented objects, these BIMFs are given different weights to enhance the details of the various regions. Then, the enhanced image is segmented by applying improved watershed algorithm. Finally, we correct the segmentation results and fix their blurring details. Our method improves the over-segmentation problem of watershed and the details blurred problem of the improved watershed. In addition, this method has good robustness to different MR images.

关键词： BEEMD MR image segmentation Watershed BIMFs Key region-based segmentation

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Medical image Retrieval Based on the Deep Convolution Network and Hash Coding 10

Medical Image Retrieval Based on the Deep Convolution Networ...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Qiu, Changyan Cai, Yiheng Gao, Xurong Cui, Yize Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China

ISBN: (纸本)9781538619377

Recent years CNN (Convolutional Neural Network) has performed well in image processing, including image retrieval. However, since the features of CNN extraction are usually high-dimensional, and in the massive data conditions, it is a rather time-consuming process to traverse all the images and calculate the distance between the feature vectors to accurately find the closest Top K images. The proposed paper uses an effective deep learning framework in which Deep Convolution Network is combined with Hash Coding to learn rich medical image representing through CNN. First, a hash layer is added to the network to represent the image information as binary hashing codes;Simultaneously, the dimension of feature vector is effectively reduced by the framework;then, In order to improve the accuracy of image retrieval, rough searching and fine searching are combined. The experimental results show that our method is optimal than several hashing algorithms and CNN methods on the TCIA-CT database and VIA/I-ELCAP database.

关键词： CNN image retrieval image representing hash codes feature vector

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Palmprint Recognition Based on Harris Corner Detection and Statistical Offset Matching 10

Palmprint Recognition Based on Harris Corner Detection and S...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Wang, Hua Xiao, Kai Zhao, Weiqiang Pang, Liaojun Zhao, Heng Xidian Univ Sch Life Sci & Technol Xian Shaanxi Peoples R China

ISBN: (纸本)9781538619377

Palmprint deformation, including rotation, displacement, swelling and so on, is a crucial factor for the palmprint recognition performance. The traditional algorithms mainly focus on the feature-based matching and the block-based matching. In this paper, a novel block-based matching palmprint recognition scheme is proposed. The sampling points on the template image are built by the harris corner detection algorithm. Furthermore, the corresponding sampling points on the query palmprint image are estimated by image pyramids algorithm for reducing the impact of distortion. At last, a matching method based on statistical offset is proposed to calculate the final matching score. Extensive experiments on public palmprint database shows that our method can achieve excellent recognition performance compared with many other classic methods.

关键词： Plmprint Recognition image Pyramids Harris Corner Detection

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Classification of computerized tomography images of endemic liver hydatid in Xinjiang based on Decision Tree

Classification of computerized tomography images of endemic ...

引用

international congress on image and signal processing, biomedical engineering and informatics

作者： Azmat Rozjan Roxangul Arxidin Nadiya Abdukeyim Chuan Bo.Yan Abdugheni Kutluk Murat Hamit Juan Yao Wei Liu School of Basic Medicine Xinjiang Medical University Urumqi China College of Medical Engineering Technology Xinjiang Medical University Urumqi China Department of Radiology Xinjiang Medical University Urumqi China School of Computer Science and Technology Xi'an University of Posts and Telecommunications Xi'an China

Objective: to assess the classification capability dealing with CT images, by means of Decision Tree classifier. Methods: the CT images were provided by the hospital was used as the data source. In this study, normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid CT images were selected, each 200 pieces. Then the texture features were extracted by gray gradient co-occurrence matrix (GGCM) and gray scale histogram. At last, Decision tree classifier were used for classification, which aimed to verify Which feature is more suitable for decision tree classification, and Parameter estimation is used to evaluate the classifier model. Results: For normal liver, Single cystic hepatic hydatid and Polycystic hepatic hydatid, the classification accuracy of gray level co-occurrence matrix was 71%, 69% and 69%, respectively. the classification accuracy of gray scale histogram was 74%, 63.5% and 69%. the classification accuracy of comprehensive features was 75%, 70.5% and 80.5%. Conclusion: The classification accuracy of comprehensive feature is 11.5% higher than that of single features, which are more suitable for the classification of polycystic hepatic hydatid images. This algorithm can provide reference for the classification of CT images.

关键词： Feature extraction Decision trees Computed tomography Liver Histograms biomedical imaging Gray-scale

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Manifold Inconsistency Constrained Sparse Coding for image Super-Resolution Reconstruction 10

Manifold Inconsistency Constrained Sparse Coding for Image S...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Zhu, Huasheng Xie, Kaiyan Ye, Jun Wu, Zhaoming Nanchang Inst Technol Sch Informat Engn Nanchang Jiangxi Peoples R China

ISBN: (纸本)9781538619377

In the most of super-resolution reconstruction algorithms, high-resolution (HR) and low-resolution (LR) images are assumed to have the same manifold. However, this assumption is not suitable for the practical applications. Therefore, this paper proposes a new algorithm with the consideration of inconsistent manifold between HR and LR images. In the proposed method, LR dictionary and spare representation coefficient set are trained first. Then, HR dictionary can be obtained based on HR sample set and spare representation coefficients which are obtained in the first step. PCA algorithm is introduced to reduce the dimensionality of LR feature image to reduce the dictionary training time. A global optimization algorithm is provided to eliminate the blocking effect of the reconstruction image. Results show that the proposed method can reduce training time rapidly and improve the quality of reconstruction image in the aspects of both objective evaluation and subjective visual effect.

关键词： super-resolution sparse model manifold inconsistency dictionary learning

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Two Dimensional Autoregressive Modeling-Based Interpolation Algorithms for image Super-resolution: A Comparison Study 10

Two Dimensional Autoregressive Modeling-Based Interpolation ...

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10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI)

作者： Lu, Meiyun Huang, Liqing Xia, Youshen Fuzhou Univ Coll Math & Comp Sci Fuzhou Fujian Peoples R China

ISBN: (纸本)9781538619377

image interpolation is a key technique of image super-resolution. Four two dimensional (2-D) autoregressive (AR) modeling-based image interpolation algorithms have been reported to have better performance in edge and texture preservation than conventional image polynomial interpolation algorithms. However, there is lack of performance comparison among them. For super-resolution reconstruction quality, this paper is going to fill up the gap by a comparison study on the four 2-D AR modeling-based interpolation methods: novel edge-directed interpolation (NEDI), soft-decision adaptive interpolation (SAI), sparse representation interpolation with nonlocal autoregressive modeling (SR-NARM), and adaptive super-pixel-guided AR modeling (ASARM). Furthermore, the four interpolation algorithms are compared in the light of peak signal to noise ratio, feature similarity index, mean squared error and structural similarity index. From comparative results we observe that ASARM method has relatively better performance than other three methods but is more time-consuming than the NEDI and SAI methods.

关键词： image super-resolution interpolation algorithms autoregressive modeling comparative analysis

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