检索结果-内蒙古大学图书馆

Image feature extraction algorithm in big data environment

JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5109-5118页

作者： Zhang, Yubao Zhejiang Fash Inst Technol Sch Informat Media Ningbo 315211 Zhejiang Peoples R China

The purpose of this article is to explore effective image feature extraction algorithms in the context of big data, and to mine their potential information from complex image data. Based on the BRISK and sift algorithms, this paper proposes an image feature extraction and matching algorithm based on BRISK corner points. By combining the sift scale space and the BRISK algorithm, a new scale space construction method is proposed. The BRISK algorithm extracts the corner invariant features. Then, by using the improved feature matching method and eliminating the mismatching algorithm, the exact matching of the images is realized. A large number of experimental verifications were performed in the standard test Mikolajczyk image database and aerial image database. The experimental results show that the improved algorithm in this paper is an effective image matching algorithm. The highest accuracy of actual aerial image matching can reach 85.19%, and it can realize the actual aerial image matching that BRISK and sift algorithms cannot complete. The improved algorithm in this paper has the advantages of higher matching accuracy and strong robustness.

关键词： Big data image feature extraction corner point brisk algorithm sift algorithm

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Three-dimensional digital reconstruction of skin epidermis and dermis

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JOURNAL OF MICROSCOPY 2018年第2期270卷 170-175页

作者： Liu, P. Zhu, J. -Y. Tang, B. Hu, Z. -C. Guangzhou Red Cross Hosp Guangzhou Guangdong Peoples R China Sun Yat Sen Univ Affiliated Hosp 1 Guangzhou Guangdong Peoples R China

This study describes how three-dimensional (3D) human skin tissue is reconstructed, and provides digital anatomical data for the physiological structure of human skin tissue based on large-scale thin serial sections. Human skin samples embedded in paraffin were cut serially into thin sections and then stained with hematoxylin-eosin. Images of serial sections obtained from lighting microscopy were scanned and aligned by the scale-invariant feature transform algorithm. 3D reconstruction of the skin tissue was generated using Mimics software. Fibre content, porosity, average pore diameter and specific surface area of dermis were analysed using the ImageJ analysis system. The root mean square error and mutual information based on the scale-invariant feature transform algorithm registration were significantly greater than those based on the manual registration. Fibre distribution gradually decreased from top to bottom;while porosity showed an opposite trend with irregular average pore diameter distribution. A specific surface area of the dermis showed a V' shape trend. Our data suggested that 3D reconstruction of human skin tissue based on large-scale serial sections could be a valuable tool for providing a highly accurate histological structure for analysis of skin tissue. Moreover, this technology could be utilized to produce tissue-engineered skin via a 3D bioprinter in the future. Lay description This study is to reconstruct three-dimensional (3D) structure of the skin tissue to clarify the physiology of the skin tissue and to provide the anatomical data for 3D bio printer based on large scale serial skin tissue slices. Normal lateral thigh skin tissue were collected and processed to be a serial sections stained with hematoxylin-eosin. Whole serial slides were digitized by scanned lighting microscopy and aligned by the scale-invariant feature transform algorithm. Mimics software were used to generate 3D reconstructions of the skin tissue. Image analysis system was pe

关键词： Serial sections sift algorithm 3D reconstruction skin tissue

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Fast detection of moving objects based on sequential images processing

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2020年第4期39卷 5037-5044页

作者： Wei, Peng-Cheng He, Fangcheng Li, Jing Chongqing Univ Educ Sch Math & Informat Engn Chongqing Peoples R China Chongqing Univ Educ Coll Foreign Languages Literature Chongqing Peoples R China

Nowadays, moving object detection in sequence images has become a hot topic in computer vision research, and has a very wide range of practical applications in many fields of military and daily life. In this paper, fast detection of moving objects in complex background is studied, and fast detection methods for moving objects in static and dynamic scenes are proposed respectively. Firstly, based on image preprocessing, aiming at the difficulty of feature extraction of moving targets in low illumination at night, Gamma change is used to process. Secondly, for the fast detection of moving objects in static scenes, this paper designs a detection method combining background difference and edge frame difference. Finally, aiming at the fast detection of moving objects in dynamic scenes, a feature matching detection method based on the sift algorithm is designed in this paper. Simulation experiments show that the method designed in this paper has good detection performance.

关键词： Sequence image fast detection of moving objects Gamma change sift algorithm

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Applying image registration algorithm combined with CNN model to video image stitching

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JOURNAL OF SUPERCOMPUTING 2021年第12期77卷 13879-13896页

作者： Cao, Weiran Zhejiang Yuexiu Univ Coll Network Commun Shaoxing Peoples R China Sangmyung Univ Coll Design Seoul South Korea

The purposes are to explore the video image stitching technique of Unmanned Aerial Vehicles (UAVs), expand the application of image registration algorithms and new sensing equipment in video image stitching, and improve the development of video remote sensing image processing. First, how to remotely stitch UAV video images is analyzed. Second, the Scale Invariant Feature Transform (sift) algorithm is improved by re-dividing the pixel region and incorporating the vector correlation coefficient. Then, the matching results of the improved sift algorithm are compared with those of the traditional sift algorithm and Speed Up Robust Feature algorithm. Second, the Random Sample Consensus algorithm is introduced to match the video images accurately, and the matching accuracy of the converted images is verified. Finally, the improved image registration algorithm, the homography matrix estimation model based on convolutional neural network, and the conformation equation of the video sensor are combined to complete the video image stitching of UAVs. Also, the stitching quality of the video image is analyzed. The results show that the improved sift algorithm is better than the traditional sift algorithm in terms of correct matching and matching time. The visually transformed finely-matched image has a higher matching accuracy rate, and the combination of the two registration algorithms eliminates mismatch points effectively. Compared with the traditional stitching method, the video image stitching method proposed in this study has a higher structural similarity index and edge difference spectrum index, which is feasible and effective. The combination of image registration algorithm with new sensors and deep learning has great application potential in video image stitching.

关键词： sift algorithm UAV Video image stitching Large plane array video sensor Convolutional neural network RANSAC algorithm Fine image stitching

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Vibration detection method for core loosening degree of high-voltage direct current converter valve saturable reactor based on variational mode decomposition-symmetrized dot pattern and image matching

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IET SCIENCE MEASUREMENT & TECHNOLOGY 2022年第8期16卷 454-466页

作者： Liu, Yunpeng Lai, Tingyu Liu, Jiashuo Wei, Xiaoguang Pei, Shaotong Wang, Chenghao Zhang, Xiaolong North China Elect Power Univ State Key Lab Alternate Elect Power Syst Renewabl Baoding Peoples R China North China Elect Power Univ Hebei Prov Key Lab Power Transmiss Equipment Secu Baoding Peoples R China Jining Power Supply Bur Jining Peoples R China State Key Lab Adv Power Transmiss Technol Beijing Peoples R China

Saturable reactor is the key equipment in converter valves;it is important to evaluate the core condition. In this paper, a recognition model of saturable reactor core loosening based on variational mode decomposition (VMD)-symmetrized dot pattern (SDP) feature fusion images and scale-invariant feature transform (sift) was proposed. Firstly, the saturable reactor vibration test under high frequency pulse excitation was carried out, and the vibration signals in different core loosening degrees were collected. Secondly, the VMD algorithm was used to decompose the broadband vibration signal into multiple narrowband functions, which were used to reflect the characteristics of each frequency band. Thirdly, each function and the original signal were transformed by SDP, the generated spiral arms were fused into a new image, and the typical templates in different loosening degrees were selected. Finally, the improved sift algorithm was used to obtain the matching results between test sets and templates. The results show that the recognition accuracy of the proposed model for core loosening is 97.5%, which is better than traditional algorithms. It can find the core loosening defect early and avoid further failures such as water pipe break and discharge, which can provide an important basis for saturable reactor monitoring.

关键词： saturable reactor monitoring scale-invariant feature transform vibrations saturable reactor core loosening saturable reactor vibration test water pipe break VMD algorithm power convertors broadband vibration signal high frequency pulse excitation image matching recognition model variational mode decomposition-symmetrized dot pattern feature extraction vibrational signal processing reactors (electric) transforms core loosening degree image fusion HVDC power convertors feature fusion images vibration detection method multiple narrowband functions sift algorithm SDP high-voltage direct current converter valve saturable reactor

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Research on control strategy of multi-source data fusion solar intelligent vehicle based on image recognition

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INTERNATIONAL JOURNAL OF LOW-CARBON TECHNOLOGIES 2021年第4期16卷 1363-1370页

作者： Zhang, Lulin Hubei Univ Technol Sch Elect & Elect Engn Lions Hill St Wuhan Hubei Peoples R China

Nowadays, the global energy and environmental problems are becoming more and more serious, which promotes the development and utilization of renewable and clean energy in various countries. Intelligent car involves many subjects such as electronic technology, artificial intelligence, automatic control technology, sensor technology and computer technology and has become an important part of the application of artificial intelligence. Solar cell is a necessary part of the normal operation of the solar intelligent car, which can provide clean energy for the intelligent car. In this paper, the image recognition technology is used to design the intelligent vehicle control system. According to the intelligent vehicle path recognition, the scale invariant feature transform (sift) algorithm is improved to improve the accuracy of intelligent vehicle recognition. Data fusion is used to process the data detected by multi-sensor, and the running state of intelligent vehicle is studied. An evaluation method of intelligent vehicle navigation parameters based on association rules and belief network is proposed. The maximum power point tracking control is realized by using the interference observation method to ensure that the intelligent vehicle can track the maximum power point of the solar cell.

关键词： sift algorithm multi-source data fusion image recognition solar smart car

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Research on Vehicle Detection Technology Based on sift Feature

Research on Vehicle Detection Technology Based on SIFT Featu...

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8th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Tu Yawen Guo Jinxu Wuhan Univ Technol Coll Informat Engn Wuhan Hubei Peoples R China

ISBN: (纸本)9781538657744

To aim at the problem of vehicle detection in the process of vehicle identification, to propose a method that identifies the possible regions in the image by collecting image classifier samples, using sift algorithm to extract the eigenvalues, using svm classifier to classify, and using Gaussian pyramid in combination with sliding window. Then, to extract the results by the non-maximal suppression algorithm to improve the recognition rate of vehicle recognition. Based on this, to perform the experimental verification. The experimental process and the results show that the vehicle identification accuracy rate of the above method reaches 93.4%.

关键词： vehicle detection sift algorithm svm classifier Gaussian pyramid sliding window

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A non-rigid cardiac image registration method based on an optical flow model

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OPTIK 2013年第20期124卷 4266-4273页

作者： Lu, Xiaoqi Zhao, Yongjie Zhang, Baohua Wu, Jianshuai Li, Na Jia, Weitao Inner Mongolia Univ Sci & Technol Sch Informat Engn Baotou City 014010 Inner Mongolia Peoples R China

According to non-rigid medical image registration, new method of classification registration is proposed. First, Feature points are extracted based on sift (Scale Invariant Feature Transform) from reference images and floating images to match feature points. And the coarse registration is performed using the least square method. Then the precise registration is achieved using the optical flow model algorithm. sift algorithm is based on local image features that are with good scale, rotation and illumination invariance. Optical flow algorithm does not extract features and use the image gray information directly, and its registration speed is faster. The both algorithms are complementary. sift algorithm is used for improving the convergence speed of optical flow algorithm, and optical flow algorithm makes the registration result more accurate. The experimental results prove that the algorithm can improve the accuracy of the non-rigid medical image registration and enhance the convergence speed. Therefore, the algorithm has some advantages in the image registration. (c) 2013 Elsevier GmbH. All rights reserved.

关键词： Non-rigid registration Medical image sift algorithm Optical flow model

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High-Precision Matching algorithm for Multi-Image Segmentation of Micro Animation Videos in Mobile Network Environment

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MOBILE NETWORKS & APPLICATIONS 2023年第5期28卷 1662-1672页

作者： Su, Yehui Djenouri, Youcef Jilin Inst Architecture & Technol Sch Creat Design Changchun 130114 Peoples R China Univ South Eastern Norway Kongsberg Norway

In the mobile network environment, the accuracy of related image matching algorithms is affected by factors such as bandwidth uncertainty and channel interference, resulting in significant limitations in image feature matching. This article designs a high-precision matching algorithm for multi-image segmentation of micro animation videos in mobile network environments. Fully denoise micro animation video images using 2D High Density Discrete Wavelet Transform (HD-DWT), and apply fixed block count segmentation to process micro animation video images;Using Harris algorithm to complete image corner detection and obtain corner features of sub images;In the K-means clustering algorithm, sift feature vectors are divided into clusters and paired with the nearest neighbor cluster in another sub image to form a sub image matching pair, completing block based sub image matching;Combine all sub image matching results to obtain video image matching results, and use the Improved Random Sampling Consistency (RANCAS) algorithm to remove incorrect matching during the matching process, improving matching accuracy. The experimental results show that the designed algorithm can effectively reduce image noise, improve image quality, and generate a large number of matching pairs in mobile network environments. After the application of the designed algorithm, the production effect of micro animated videos in mobile networks can be significantly improved.

关键词： Mobile network Micro animated videos Image segmentation Image matching Harris algorithm sift algorithm

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Automatic detection of slide transitions in lecture videos

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MULTIMEDIA TOOLS AND APPLICATIONS 2015年第18期74卷 7537-7554页

作者： Jeong, Hyun Ji Kim, Tak-Eun Kim, Hyeon Gyu Kim, Myoung Ho Korea Adv Inst Sci & Technol Dept Comp Sci Taejon 305701 South Korea Sahmyook Univ Dept Comp Engn Seoul 139742 South Korea

This paper presents a method to automatically detect slide changes in lecture videos. For accurate detection, the regions capturing slide images are first identified from video frames. Then, sift features are extracted from the regions, which are invariant to image scaling and rotation. These features are used to compare similarity between frames. If the similarity is smaller than a threshold, slide transition is detected. The threshold is estimated based on the mean and standard deviation of sample frames' similarities. Using this method, high detection accuracy can be obtained without any supplementary slide images. The proposed method also supports detection of backward slide transitions that occur when a speaker returns to a previous slide to emphasize its contents. In experiments conducted on our test collection, the proposed method showed 87 % accuracy in forward transition detection and 86 % accuracy in backward transition detection.

关键词： Video segmentation Video indexing Recursive interval pruning Backward slide transitions Adaptive threshold sift algorithm

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