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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1341-1350 订阅

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Harris Corner Detection based Leaf image Segmentation for Ancient Chinese Books 10

Harris Corner Detection based Leaf Image Segmentation for An...

引用

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

作者： Zhang, Jingjing Cai, Yuan Jiang, Wei Wang, Chuanyu Commun Univ China Fac Sci & Technol Beijing Peoples R China Minist Culture Key Lab Acoust Visual Technol & Intelligent Contr Beijing Peoples R China Beijing Key Lab Modern Entertainment Technol Beijing Peoples R China

ISBN: (纸本)9781538619377

In the image digitization process of ancient Chinese books, sometimes it is needed to extract the leaf from the original image and separate the left half and the right half. This paper proposes a leaf segmentation method based on Harris corner detection to solve this problem. Firstly, the original image is preprocessed to recognize the contour information, and the minimum bounding rectangle is determined by the contour. Then, Harris corner detection is used. In order to recognize the edge of the leaf, we traverse the corner points in the special area and save those points which meet the threshold requirement. At last, the edges of left half leaf and right half leaf are determined, and the segmentation is completed. The experimental results show that the proposed method achieves good performance in a class of leaf image segmentation.

关键词： corner detection Harris algorithm leaf image segmentation

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Fusion of Infrared and Visible images Based on a Hybrid Decomposition via the Guided and Gaussian Filters 10

Fusion of Infrared and Visible Images Based on a Hybrid Deco...

引用

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

作者： Rong, Chuanzhen Jia, Yongxing Yue, Zhenjun Yang, Yu PLA Army Engn Univ Coll Commun Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781538619377

Due to the characteristics of infrared and visible images, in the process of image fusion, the two kinds of heterogeneous image information will interfere with each other, which lead to the fusion image become blurred, confused and suffers contrast degradation. Therefore, a novel fusion method for infrared and visible images based on a hybrid decomposition via guided and Gaussian filters is proposed in this paper. Firstly, guided and Gaussian filters are used to achieve a hybrid decomposition for infrared and visible images, then the small-scale texture details, large-scale edges and coarse image information of the input images can be obtained. The sub-information is then merged, and the fusion weights are determined by the large-scale edges which can represent the features of the infrared image. In this way, the prominent infrared image information can be injected into the visible image. In the end, the fused image can be reconstructed by assembling the merged sub-information. Experimental results show that the proposed is better than the typical multi-scale decomposition based image fusion algorithms both in subjective and objective evaluation.

关键词： guided image filtering Gaussian filter image fusion infrared and visible image

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Positive and Negative HEp-2 image Classification Fusing Global and Local Features 10

Positive and Negative HEp-2 Image Classification Fusing Glob...

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

作者： Zhou, Jiancan Li, Yuexiang Zhou, Xiande Shen, Linlin Shenzhen Univ Coll Comp Sci & Software Engn Comp Vis Inst Shenzhen Peoples R China

ISBN: (纸本)9781538619377

Human Epithelial type 2 (HEp-2) cells play an important role in the diagnosis of autoimmune disorder. Traditional approach relies on specialists to observe HEp-2 slides via the fluorescence microscope, which suffers from a number of shortcomings like being subjective and labor intensive. Pattern recognition techniques have been recently introduced to this research issue to make the process automatic. The diagnosis includes two stages, the first stage is to classify the positive and negative cell images, the second stage is to classify the positive cells into different categories. We propose in this paper a framework using global and local features for positive and negative HEp-2 image classification. By using global feature firstly for a rough classification, cells segmentation and local feature extraction were applied further for more accurate classification. The proposed framework was evaluated with SZU dataset. The results indicate that the proposed framework can achieve as high as 98.68% accuracy.

关键词： HEp-2 image classification global feature local feature segmentation SVM

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Blind Super-resolution image Reconstruction Based On Novel Blur Type Identification 10

Blind Super-resolution Image Reconstruction Based On Novel B...

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

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

ISBN: (纸本)9781538619377

Blind super-resolution image reconstruction is to obtain a high-resolution image from a sequence of low-resolution images which are degraded by unknown blur, noise, and down sample. Conventional super-resolution image reconstruction algorithms assumed that the blur type is known, thus automatic blur identification is of important significance in blind super resolution image reconstruction. This paper proposed a novel blur type identification algorithm for blind image super resolution. The proposed blur type identification method uses a dictionary learning to identify three blur kernels. It includes the logarithmic normalized feature matrix, the structural similarity index, and the best structural similarity between observed images and dictionary images. Furthermore, we applied the proposed blur type identification method to blind image super-resolution. The experimental result shows that the identification accuracy of proposed method can achieve 98% above. More importantly, the proposed blur type identification-based algorithm for blind image super-resolution can enhance the performance of reconstruction quality according to visual quality and evaluation index.

关键词： blind image super-resolution blur kernel type identification

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Low-Light image Enhancement Algorithm Based on HSI Color Space 10

Low-Light Image Enhancement Algorithm Based on HSI Color Spa...

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

作者： Wu, Fan KinTak, U. Macau Univ Sci & Technol Fac Informat Technol Macau Peoples R China

ISBN: (纸本)9781538619377

To improve the quality of low-light image, we proposed a new HSI based enhancement algorithm. This new algorithm enhances the luminance of low-light level images while preserving image contrast and details. First, the original RGB image is converted into HSI color space, then the intensity and saturation components are processed with different enhancement methods, but the hue component remains unchanged, the segmentation exponential enhancement algorithm is applied to saturation component S, then apply the histogram equalization to intensity component I and then the intensity component I is divided into high and low frequency sub-bands with wavelet transform, the Retinex algorithm is applied to the low frequency sub-band to adjust image luminance while the improved fuzzy enhancement is applied to the high frequency sub-band to enhance image details. Finally, reconstruct the component I with inverse wavelet transform, and the reconstructed component I will be synthesized with H and the enhanced S components to get a clear RGB image. By taking advantage of HSI color space and the improved enhancement algorithm, the enhancement of low illumination color image has been achieved. According to the experiment results, this algorithm can obviously improve the visual effect of low light color image.

关键词： HSI low-light wavelet transform histogram equalization Retinex

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An advanced web-based Digital image processing Virtual Lab using LabVIEW 10

An advanced web-based Digital Image Processing Virtual Lab u...

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

作者： Atrish, Abhay Kumar, Vinod Natl Inst Technol Uttarakhand Comp Sci & Engn Uttarakhand India Indian Inst Technol Roorkee Dept Elect Engn Uttarakhand India

ISBN: (纸本)9781538619377

Digital image processing (DIP) has found its applications in various domains and has become a highly active research area. Practical implementation and experimentation play an integral role in education as well as in research activities. For the easy and quick implementation of DIP operations, this paper proposes an advanced web-based virtual lab for digital image processing known as DIP VLab. It is a very simple, intuitive, user-friendly and high-performance virtual lab for research and development related experimentation and exploration. The proposed application enables the user to upload an input image, choose and perform any of the available DIP operations and generate the output image accordingly. It provides a large number of basic and advanced image processing operations and supports binary, color and grayscale image types. It also generates the detailed report of the experiments performed and sends it to the user's email which improves the user's overall experience. DIP VLab outperforms other virtual labs in terms of its features, functionalities, user-friendliness, efficiency, effectiveness, robustness and scalability. DIP VLab is publicly available for everyone on the internet (http://***). This application is useful for novice as well as researchers as it allows the user to focus on experimentation instead of writing custom scripts.

关键词： Digital image processing e-learning application information and communications technology labview research and development application virtual lab web-based remote lab

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Brain MR image Segmentation Based on Gaussian Filtering and Improved FCM Clustering Algorithm 10

Brain MR Image Segmentation Based on Gaussian Filtering and ...

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

作者： Wan, Chunyuan Ye, Mingquan Yao, Chuanwen Wu, Changrong Wannan Med Coll Sch Med Informat Wuhu Peoples R China Anhui Normal Univ Sch Math & Comp Sci Wuhu Peoples R China

ISBN: (纸本)9781538619377

Traditional fuzzy C-means (FCM) clustering method is not able to get the desired results of brain segmentation in the brain magnetic resonance (MR) image. In this paper, a new method of brain image segmentation based on Gaussian filtering and FCM clustering algorithm is proposed. The method adopted Gaussian filtering to remove noise and the initial cluster center was determined by the gray histograms to obtain. Using this method, 20 samples of brain MR images with 9% and 5% noise interference provided by Brain Web were segmented. The experimental results showed the proposed method was accuracy and efficiency.

关键词： brain MR image image segmentation Gaussian filtering fuzzy C-means clustering gray histogram

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A Robust Fuzzy C-Means Algorithm Based on Diffusion Equation for SAR image Segmentation 10

A Robust Fuzzy C-Means Algorithm Based on Diffusion Equation...

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

作者： Wan, Ling Zhang, Tao You, Hongjian Chinese Acad Sci Key Lab Technol Geospatial Informat Proc & Applic Inst Elect Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100039 Peoples R China

ISBN: (纸本)9781538619377

Fuzzy c-means (FCM) algorithm and many modified ones have been widely used in image segmentation. But these methods are not adaptable to SAR images owing to the intrinsic speckle noise. In order to improve the noise-resistibility and the detail-preserving in SAR image segmentation, we propose a robust FCM algorithm based on diffusion equation (FCM DE). Firstly, the SAR image is diffused based on the partial differential equation to generate an auxiliary image, which is robust to speckles. Secondly, to make the algorithm more robust to outlier, the maximum probability of the local gray-level histogram is used to design an adaptive factor to adjust the effect of the diffusion term automatically. Moreover, this method can be extend to other PDE-based noise removal approaches and applied to other kinds of images, such as MR images and optical remote sensing images. Experiments on the simulated and real SAR images demonstrate the efficiency of FCM DE compared with other five fuzzy clustering algorithms in SAR images segmentation.

关键词： SAR segmentation diffusion equation fcm

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Combining Multiple Feature for texture image classification 10

Combining Multiple Feature for texture image classification

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

作者： Yang, Ting Shi, Guohua Yang, Na Wang, Lirong Soochow Univ Sch Elect & Informat Engn Suzhou Peoples R China Chinese Acad Sci Suzhou Inst Biomed Engn & Technol Suzhou Peoples R China

ISBN: (纸本)9781538619377

In order to classify the texture image in the project of endoscopic Optical Coherence Tomography (OCT) tumor recognition, this paper studies a multi-feature fusion texture method for the classification of texture. This method extracted feature sets for images classification based on some methods of Autocorrelation, Tamura, Statistics, Gabor Filter, Gray-level co-occurrence matrix (GLCM), Local Binary Pattern (LBP). LBP, GLCM, Statistics, Tamura feature sets were extracted from original images. In addition to above feature sets, Autocorrelation feature set was calculated from processed images using Gabor filter. The dimension of features was reduced by principal component analysis (PCA). Then we used support vector machine (SVM) for classification. Through the accuracy reached to 95.72% of the test of the Brodatz texture database, which is higher than 94.14% that got from the experiment of S.R. Zhou. And using the Outex database to test, the accuracy is 96%, which is higher than 84% given by the university of oulu.

关键词： Brodatz database Outex database image classification LBP Tamura Statistics Gabor filter

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

作者： Wang, Mao Ming, Yuewei Liu, Qiang Yin, Jianping Natl Univ Def Technol Coll Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol State Key Lab High Performance Comp Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538619377

Similarity search is critical for image retrieval, which involves retrieving similar images among the database for queries. Current state-of-the-art approaches rely on matrix factorization, consisting of dictionary learning and sparse coding step. However, the extensive dictionary learning time and the non-smooth encoding restrict its application. In this paper, we have proposed a similarity search approach based on local-constrained linear coding (LLC). This approach can reduce the time cost of dictionary learning by simply using k-means clustering. In addition, the LLC schema can lead to a better representation over sparse coding, which can benefit for similarity search. Experimental results on three image retrieval datasets demonstrate that the proposed approach can reduce offline learning time and achieve better retrieval accuracy over the state-of-the-art approach for balanced data.

关键词： Similarity search image retrieval matrix factorization LLC

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