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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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Recognition and Classification of Coating Film Defects on Automobile Body Based on image processing 10

Recognition and Classification of Coating Film Defects on Au...

引用

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

作者： Cheng, Pu Cui, An Yang, Yujia Luo, Yawei Sun, Wenlong Jilin Univ State Key Lab Automot Simulat & Control Changchun Jilin Peoples R China

ISBN: (纸本)9781538619377

To realize online detection of coating film defects on automobile body and improve the efficiency of the identification of defects, a new classification method of coating film defect based on image processing was proposed. According to the types and characteristics of coating film defects, this paper selected six geometric features and sixteen grayscale characteristics as defect feature parameters. image enhancement method based on morphology and image segmentation method based on graph theory were proposed to complete image processing. The process of using support vector machine (SVM) to identify the defects was elaborated, including data standardization and dimension reduction. The identification experiments on six kinds of coating film defects showed that four kinds of defects recognition rates including particles, sagging, scratches and orange were all beyond 90% and the other two kinds of defects recognition rates were 85.00% and 50.25% respectively. The time of testing a sample was about 2 seconds. The reason why the other two kinds of defects recognition rates were not ideal was that the characteristics of sparkling and pinhole were too close to recognize. But the recognition rate can be improved by the development of equipment and the increasing number of SVM training sample. Therefore, the method can satisfy the basic requirement of coating film defects on-line detection, and the method is convenient to apply.

关键词： coating film defects on automobile body image processing feature extraction SVM

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An image Patch Matching Method Based on Multi-feature Fusion 10

An Image Patch Matching Method Based on Multi-feature Fusion

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

作者： Yu, Xiangru Guo, Yukun Li, Jinping Cai, Fudong Univ Jinan Sch Informat Sci & Engn Jinan 250022 Shandong Peoples R China Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Shandong Peoples R China Shandong Senter Elect Co Ltd Zibo 255000 Shandong Peoples R China

ISBN: (纸本)9781538619377

Appropriate features are very important for the robustness and effectiveness of matching algorithms. In general, current algorithms depend on descriptors like SIFT, SURF, which makes them only care about information of key points while ignoring the knowledge of the whole image, thus those methods are easy to result in false matches. We propose a novel matching method called multi-feature fusion, which takes full advantage of geometric, gray, color and texture features. Then we validate the effect of our method using images captured from practical applications. Experiments show the method can effectively complete the matching task of image patch.

关键词： image patch matching multi-feature fusion texture energy

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An Improved Medical image Segmentation Algorithm Based On Clustering Techniques 10

An Improved Medical Image Segmentation Algorithm Based On Cl...

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

作者： Li, Xiang-Wei Kang, Yu-Xiu Zhu, Ya-Ling Zheng, Gang Wang, Jun-Di Lanzhou Inst Technol Inst Software Engn Lanzhou Gansu Peoples R China Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Lanzhou Gansu Peoples R China

ISBN: (纸本)9781538619377

aimed to solve the problem of the medical image segmentation and apply to medical treatment or diagnose in practice, the paper proposed an improved medical image segmentation algorithm based on improved clustering analysis. Firstly, in order to increase the quality of original medical segmental image, the preparation procedure based on histogram equalization is performed;secondly, for the generation of accurate segmental region, the improved clustering analysis techniques based on data analysis is introduced and applied to process medical image data. Finally, the image labels are determined according to achieved clustering results. Compared to the traditional medical image segmentation, the proposed algorithm has better results of vision effect, and can be extensively used in medical treatment or medical image diagnose. Experiments on real medical images generated from local hospital illustrated that proposed algorithm can achieve better performance outperformed the traditional methods.

关键词： medical image image clustering image segmentation

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Study on super - resolution reconstruction algorithm based on sparse representation and dictionary learning for remote sensing image 10

Study on super - resolution reconstruction algorithm based o...

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

作者： Zhao, Xiangyu Yang, Ru Qin, Zhentao Wu, Jianbing Panzhihua Coll Sch Mathmat & Comp Sci Panzhihua Peoples R China Panzhihua Coll Sch Civil & Architectural Engn Panzhihua Peoples R China

ISBN: (纸本)9781538619377

Super-resolution image reconstruction plays a very important role in the interpretation of remote sensing images. Especially when the resolution of images is low, the size of the objects to be identified is close to the minimum resolution, and can be reconstructed by super-resolution better interpretation of the feature. In this paper, K-SVD algorithm is used to study the exampler of high resolution image library, and the dictionary of high resolution remote sensing image is obtained. The low resolution image is represented by high resolution dictionary, and the remote sensing reconstruction of remote sensing image is realized. Which improves the peak noise ratio and mean square error of the image, and has better performance than the interpolation algorithm. The method proposed in this paper has important significance and application prospect in remote sensing image application.

关键词： Super-resolution exampler K-SVD Sparse Representation

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Copy-Move Forgery Detection Based on Deep Learning 10

Copy-Move Forgery Detection Based on Deep Learning

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

作者： Ouyang, Junlin Liu, Yizhi Liao, Miao Hunan Univ Sci & Technol Sch Comp Sci & Engn Xiangtan Peoples R China

ISBN: (纸本)9781538619377

Copy-move forgery detection (CMFD) is probably one of the most active research areas within the blind image forensics field. Among existing algorithms, most of them are based on block and key-point methods, or combination of them. Recently, some deep convolutional neural networks methods have been applied in the image classification, image forensic, image hashing retrieval, and so on, which have shown better performance than the traditional method. In the work, a novel copy-move forgery detection method based on convolutional neural network is proposed. The proposed method uses existing trained model from large database as imageNet, and then adjusts slightly the net structure using small training samples. Experimental results show that the method we proposed obtains satisfactory performance to the forgery image generated automatically by computer with simple image copy-move operation

关键词： Tamper detection copy-move forgery image forensics convolutional neural network

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Improvement and Implementation of Video image De-hazing Algorithm Based on FPGA 10

Improvement and Implementation of Video Image De-hazing Algo...

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

作者： Liu, Guangwen Cai, Hua Yang, Yang Geng, Zhenye Changchun Univ Sci & Technol Coll Elect & Informat Engineer Changchun Jilin Peoples R China

ISBN: (纸本)9781538619377

Aiming at the problems of color distortion and halos existing in video images in the process of transplanting dark channel prior algorithm of de-hazing into FPGA and considering the algorithm implementation in FPGA and the real-time requirement of video processing, an improvement algorithm base on FPGA is proposed in this paper. By refining the dark channel to obtain the final transmittance for eliminating the halos and by effectively segmenting the bright regions like the sky regions, the color distortion can be eliminated through the non-linear compensation for the regional transmittance. Through the verification of MATLAB simulation and the experiment of FPGA hardware, the system can effectively solve above problems. The experimental results show that on the premise of ensuring the output video quality, the system can better de-haze and eliminate halos in video images.

关键词： FPGA De-hazing Dark Channel Prior Halo Phenomenon Color Distortion

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CT-based Automatic Identification and Localization of the Right Phrenic Nerve 10

CT-based Automatic Identification and Localization of the Ri...

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

作者： Bamps, Kobe Cuypers, Celine Claesen, Luc Koopman, Pieter Hasselt Univ Fac Engn Technol UHasselt Agoralaan Bldg H B-3590 Diepenbeek Belgium Jessa Hosp Heart Ctr Hasselt Stadsomvaart 11 B-3500 Hasselt Belgium

ISBN: (纸本)9781538619377

Atrial fibrillation is a cardiac arrhythmia that causes irregular contraction of the atria. It is caused by parasitic electric signals via the pulmonary veins. Current treatment methods involve point-by-point RF ablation, cryoablation or laser balloon ablation. Ablation creates a circumferential lesion resulting in an electrical isolation of the pulmonary veins, thereby disabling the parasitic signals causing atrial fibrillation. During ablation there is a danger that the right phrenic nerve (PN) is damaged, having a serious impact on the respiratory capability of the patient. This paper presents new automatic image processing methods and algorithms to identify and localize the right phrenic nerve starting from high quality CT scans. Nerve tissue is nearly undetectable from CT scan images. To select the most probable locations of PN candidates, a new algorithm: EXSAC is proposed. Based on CT scans of 27 test cases, the PN could be automatically identified in 89% of the cases, in comparison to the manual localization by a heart surgeon.

关键词： component phrenic nerve atrial fibrillation pulmonary vein isolation laser balloon ablation cardiac arrythmia DICOM CT scan medical image processing RANSAC

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Medical image Mosaic Based on Low-overlapping Regions 10

Medical Image Mosaic Based on Low-overlapping Regions

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

作者： Chen, Xuerong Liu, Hongying Zhou, Mei Zhang, Feng Li, Qingli East China Normal Univ Sch Informat Sci & Technol Shanghai Key Lab Multidimens Informat Proc Shanghai Peoples R China Nantong Univ Affiliated Hosp 3 Dept Clin Lab Wuxi Peoples R China

ISBN: (纸本)9781538619377

This paper focuses on the mosaicing of microscopic medical images. Different from regular application scene where the source images usually have an overlapping ratio between 30%similar to 50%, source images in this paper have a rather low overlapping ratio less than 15%. Performances of several generic feature-based registration methods are compared in order to explore which one is most appropriate for our application. This paper presents a novel mosaic algorithm as follows. Firstly, Speeded-up Robust Feature (SURF) points in the regions surrounding the overlapped are extracted and then roughly matching results of feature points are acquired. Secondly, mismatched pairs are eliminated according to spatial distance of matching points, and then geometrical relationship between reference image and target image is determined. Finally, registered images are blended into a composite image with modified weighted smooth algorithm. Experimental results demonstrate that the proposed method is effective to create a visually pleasant panoramic image.

关键词： image mosaic SURF feature matching matching refining image fusion

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Fusion Algorithm of Infrared and Visible images Based on Joint Bilateral Filter 10

Fusion Algorithm of Infrared and Visible Images Based on Joi...

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

作者： Cai, Hua Chen, Guang-qiu Liu, Zhi Geng, Zhen-ye Changchun Univ Sci & Technol Sch Elect & Informat Engn Changchun Jilin Peoples R China

ISBN: (纸本)9781538619377

In view of the problem of the less correlative infrared(IFR) and visible(VI) images, a fusion methodology using joint bilateral filter (JBF) in the domain of non-subsampled contourlet transform (NSCT) is put forword. First, the images to be fused are divided into some sub-bands by NSCT. Then, the local window energy and the coefficient absolute value is regarded as the activity measure of approximate and detail sub-bands respectively. Decision maps are obtained by selecting max activity measure. Source images are regarded as the guided images and decision maps are used as input images in JBF. After filtering operation by JBF, the output images are treated as weight maps. The sub-band coefficients are fused by weighted average algorithm. Finally, the fused sub-bands are composed into a fused image by the inverse NSCT. The experiments on the IFR and VI image are carried out. For the assessment of fusion results, subjective and objective assessment methods are adopted. The results show that the proposed methodology can get better performance than some classical fusion method existed in the published literatures.

关键词： image processing non-subsampled contourlet transform shift-invariant joint bilateral filter

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Detection of Ginseng Leaf Cicatrices Base on K-means Clustering Algorithm 10

Detection of Ginseng Leaf Cicatrices Base on K-means Cluster...

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

作者： Liu, Yuan-yuan Zhou, Shi-yu Sun, Jia-hui Jilin Agr Univ JLAU Coll Informat Technol Changchun Jilin Peoples R China Agr Sci & Technol Network Collaborat Innovat Ctr Changchun Jilin Peoples R China Internet Things Engn Res Technol Ctr Jilin Prov Changchun Jilin Peoples R China

ISBN: (纸本)9781538619377

Diseases like black spot, anthracnose and Cylindrocarpon destructans always appear on ginseng leaf. The traditional way to distinguish the disease in farmland is checking by planter in person. This way takes more time and energy. In this paper, according to cicatrices are different with normal leaf in color, we use K-means clustering algorithm and combine the difference between a and b components in Lab color space to discern the cicatrices in leaf image. Firstly, we can distinguish cicatrices and normal leaves. Secondly, we take out useless part like petiole. Lastly, we calculate the ratio of area of cicatrices and normal leaf. The ratio can be used as reference of the ginseng disease and its degree. The result of experiment shows that K-means clustering algorithm can segment the leaf image, and the segmentation is based on the difference of color, then we can calculate the ratio of pixel in each part, and judge the degree of disease.

关键词： digital image processing K-means clustering algorithm detection of ginseng's disease RGB color space Lab color space

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