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An Automatic Urban Function District Division Method Based on Big Data Analysis of POI

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2021年第3期17卷 645-657页

作者： Guo, Hao Liu, Haiqing Wang, Shengli Zhang, Yu Shandong Univ Sci & Technol Coll Gernnat Qingdao Peoples R China Shandong Univ Sci & Technol Coll Transportat Qingdao Peoples R China Shandong Univ Sci & Technol Ocean Sci & Engn Coll Qingdao Peoples R China

Along with the rapid development of the economy, the urban scale has extended rapidly, leading to the formation of different types of urban function districts (UFDs), such as central business, residential and industrial districts. Recognizing the spatial distributions of these districts is of great significance to manage the evolving role of urban planning and further help in developing reliable urban planning programs. In this paper, we propose an automatic UFD division method based on big data analysis of point of interest (POI) data. Considering that the distribution of POI data is unbalanced in a geographic space, a dichotomy-based data retrieval method was used to improve the efficiency of the data crawling process. Further, a POI spatial feature analysis method based on the mean shift algorithm is proposed, where data points with similar attributive characteristics are clustered to form the function districts. The proposed method was thoroughly tested in an actual urban case scenario and the results show its superior performance. Further, the suitability of fit to practical situations reaches 88.4%, demonstrating a reasonable UFD division result.

关键词： Big Data Analysis Dichotomy Method mean shift algorithm POI Data Urban Function District

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Scale and Orientation-Based Background Weighted Histogram for Human Tracking

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3D RESEARCH 2016年第3期7卷 1-17页

作者： Laaroussi, Khadija Saaidi, Abderrahim Masrar, Mohamed Satori, Khalid Sidi Mohamed Ben Abdellah Univ Fac Sci Dhar el Mahraz Dept Math & Comp Sci LIIAN PO 1796 Atlas Fez Morocco Sidi Mohamed Ben Abdellah Univ Polydisciplinary Fac Taza Lab Engn Sci LSI PO 1223 Taza Morocco

The mean shift procedure is a popular object tracking algorithm since it is fast, easy to implement and performs well in a range of conditions. However, classic mean shift tracking algorithm fixes the size and orientation of the tracking window, which limits the performance when the target's orientation and scale change. In this paper, we present a new human tracking algorithm based on mean shift technique in order to estimate the position, scale and orientation changes of the target. This work combines moment features of the weight image with background information to design a robust tracking algorithm entitled Scale and Orientation-based Background Weighted Histogram (SOBWH). The experimental results show that the proposed approach SOBWH presents a good compromise between tracking precision and calculation time, also they validate its robustness, especially to large background variation, scale and orientation changes and similar background scenes.

关键词： Human tracking mean shift algorithm Scale and orientation estimation Moment features Background information

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Extraction and Segmentation of Sputum Cells for Lung Cancer Early Diagnosis

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algorithmS 2013年第3期6卷 512-531页

作者： Taher, Fatma Werghi, Naoufel Al-Ahmad, Hussain Donner, Christian Khalifa Univ Dept Elect & Comp Engn Sharjah U Arab Emirates Univ Osnabrueck Dept Comp Engn Osnabruck Germany

Lung cancer has been the largest cause of cancer deaths worldwide with an overall 5-year survival rate of only 15%. Its symptoms can be found exclusively in advanced stages where the chances for patients to survive are very low, thus making the mortality rate the highest among all other types of cancer. The present work deals with the attempt to design computer-aided detection or diagnosis (CAD) systems for early detection of lung cancer based on the analysis of sputum color images. The aim is to reduce the false negative rate and to increase the true positive rate as much as possible. The early detection of lung cancer from sputum images is a challenging problem, due to both the structure of the cancer cells and the stained method which are employed in the formulation of the sputum cells. We present here a framework for the extraction and segmentation of sputum cells in sputum images using, respectively, a threshold classifier, a Bayesian classification and mean shift segmentation. Our methods are validated and compared with other competitive techniques via a series of experimentation conducted with a data set of 100 images. The extraction and segmentation results will be used as a base for a CAD system for early detection of lung cancer which will improve the chances of survival for the patient.

关键词： lung cancer detection image segmentation sputum cells threshold algorithm bayesian classification mean shift algorithm

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Application of 3D image processing technology based on image segmentation in packaging design

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年 1-12页

作者： Jin, Xiaoxiao Shanghai Publishing & Printing Coll Dept Art Design Shanghai 200093 Peoples R China

Recent years, the methods to combine artificial intelligence technology with 3D image processing technology has become a hub for research in packaging design. Traditional 3D images are mostly produced by professional equipment, but this method is small in scope and high in cost, which does not meet the needs of most people. To solve the above problems, this study combines the mean shift algorithm with the confidence propagation algorithm, and obtains the confidence propagation-mean shift algorithm. In addition, the Lucascanard-confidence factor optical flow algorithm is improved by introducing the confidence factor to the Lucascanard-confidence factor optical flow algorithm. The research continues to combine the confidence propagation-mean shift algorithm with the Lucaskarnad-confidence factor optical flow algorithm to extract parallax maps and then synthesize 3D images. The results show that the iteration times and iteration time of the confidence propagation-mean shift algorithm are 9 times and 97.05 s, respectively. The number of parallax templates and the number of regions is 6 and 43 respectively. The confidence propagation-mean shift algorithm has 4 iterations, 36.8 s iteration time, 14 parallax templates and 65 regions in the category of portrait images. The accuracy of foreground depth, background depth and depth are 99.72, 99.87 and 99.80%, respectively, for the Lucas Kanard-confidence factor optical flow algorithm. In summary, the two algorithms proposed in this study have excellent performance, which can extract parallax map well and generate 3D image accurately, owning certain promotion value in the field of product packaging design.

关键词： Packaging design field mean shift algorithm Confidence propagation algorithm Confidence factor Multi-resolution stepwise refinement strategy

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An efficient and robust hybrid method for segmentation of zebrafish objects from bright-field microscope images

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MACHINE VISION AND APPLICATIONS 2018年第8期29卷 1211-1225页

作者： Guo, Yuanhao Xiong, Zhan Verbeek, Fons J. Leiden Univ Imaging & BioInformat LIACS Niels Bohrweg 1 NL-2333 CA Leiden Netherlands

Accurate segmentation of zebrafish from bright-field microscope images is crucial to many applications in the life sciences. Early zebrafish stages are used, and in these stages the zebrafish is partially transparent. This transparency leads to edge ambiguity as is typically seen in the larval stages. Therefore, segmentation of zebrafish objects from images is a challenging task in computational bio-imaging. Popular computational methods fail to segment the relevant edges, which subsequently results in inaccurate measurements and evaluations. Here we present a hybrid method to accomplish accurate and efficient segmentation of zebrafish specimens from bright-field microscope images. We employ the mean shift algorithm to augment the colour representation in the images. This improves the discrimination of the specimen to the background and provides a segmentation candidate retaining the overall shape of the zebrafish. A distance-regularised level set function is initialised from this segmentation candidate and fed to an improved level set method, such that we can obtain another segmentation candidate which preserves the explicit contour of the object. The two candidates are fused using heuristics, and the hybrid result is refined to represent the contour of the zebrafish specimen. We have applied the proposed method on two typical datasets. From experiments, we conclude that the proposed hybrid method improves both efficiency and accuracy of the segmentation of the zebrafish specimen. The results are going to be used for high-throughput applications with zebrafish.

关键词： Zebrafish segmentation Bright-field microscope Hybrid method mean shift algorithm Level set method High-throughput imaging

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The development of image processing board based on target tracking

The development of image processing board based on target tr...

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2nd International Conference on Information Technology and Management Innovation (ICITMI 2013)

作者： Hu, Yahui Guo, Lejiang Xiao, Lei Cheng, Min Air Force Early Warning Acad Wuhan Peoples R China

ISBN: (纸本)9783037858646

This paper selects the target tracking algorithm suitable for specific target environment: using mean shift algorithm based on space edge direction histogram at initialization, selecting tracking algorithm based on block when there is a shelter. On the basis of algorithm analysis and software experiment and studying of TI Company's TMS320DM642 DSP chip internal structure and development process, these two algorithms researched in this paper were transplanted to DSP platform and a series of optimization were been made to the algorithms codes after transplanted,implementing target tracking and identified via DSP development board instead of PC.

关键词： DSP mean shift algorithm target tracking

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A Medical Tracking System for Contrast Media

A Medical Tracking System for Contrast Media

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International Conference on Life System Modeling and Simulation / International Conference on Intelligent Computing for Sustainable Energy and Environment (LSMS-ICSEE)

作者： Dai, Chuan Wang, Zhelong Zhao, Hongyu Dalian Univ Technol Fac Elect Informat & Elect Engn Dalian 116024 Peoples R China

ISBN: (纸本)9783642156144

Contrast media is a kind of chemical substance used to improve the image quality of Computed Tomography. However, due to its high speed of injection, emergencies (such as capillary hemorrhage) always exist. In view of this problem, a video object tracking system is implemented to monitor the injection site. The color feature is abstracted from image sequences and used for the mean shift tracking algorithm. The experiment results show that the tracking system is real-time, robust and efficient.

关键词： Tracking Visual tracking Color histogram mean shift algorithm

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Region based Segmentation using Dual Tree Complex Wavelet Transform and Rotated Complex Wavelet Filter

Region based Segmentation using Dual Tree Complex Wavelet Tr...

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2009 Annual IEEE India Conference

作者： Phadke, Anuradha C. Wadke, Sonali R. Rege, Priti R. Maharashtra Inst Technol Elect & Telecommun Engg Dept Pune Maharashtra India Coll Engn Elect & Telecommun Engn Dept Pune Maharashtra India

ISBN: (纸本)9781424448586

In this paper, we have proposed a novel approach for segmentation of textured images using combination of Dual Tree Complex Wavelet Transform (DT-CWT) and Dual Tree Rotated complex Wavelet Filters (DT-RCWFs). DT-CWT is used because of its properties such as shift invariance, good directional selectivity, limited redundancy and efficient computation. RCWF sets provide important complementary information to the DT-CWT filter set by extracting texture features in 6 different directions which are 45(0) apart from decomposition directions of DT-CWT. mean shift algorithm is used along with fuzzy c-means (FCM) to make segmentation automatic. The proposed technique is tested on a range of textured images including composite texture images, synthetic texture images as well as real scene images.

关键词： DT-CWT RCWF Region based segmentation mean shift algorithm FCM

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A Method of Distributed Multi-Satellite Mission Scheduling Based on Improved Contract Net Protocol 11

A Method of Distributed Multi-Satellite Mission Scheduling B...

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11th International Conference on Natural Computation (ICNC) / 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Feng, Peng Chen, Hao Peng, Shuang Chen, Luo Li, Longmei Natl Univ Def Technol Coll Elect Sci & Engn Changsha Hunan Peoples R China

ISBN: (纸本)9781467376792

With the increasing number of earth observing satellites and the growing demand for remote sensing data, satellite mission scheduling is undergoing a change from the traditional off-line mode to on-line mode. To overcome difficulties in handling large quantities of satellites and large-scale scenes by the current on-line multi-satellite mission scheduling method, we establish the mathematical model, design the computation framework based on multi-agent system contract net protocol. Then we put forward a load reduction method based on mean shift clustering for satellite scheduling agent and a bid evaluation method based on genetic algorithm for central cooperating agent. Finally, Experimental results are used to demonstrate the feasibility and effectiveness of the algorithms.

关键词： Multi-Satellite Distributed Mission Scheduling Earth Observing Satellites mean shift algorithm Genetic algorithm Improved Contract Net Protocol

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Mode Seeking with an Adaptive Distance Measure 12

Mode Seeking with an Adaptive Distance Measure

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12th European Conference on Computer Vision (ECCV)

作者： Pan, Guodong Shang, Lifeng Schnieders, Dirk Wong, Kwan-Yee K. Univ Hong Kong Dept Comp Sci Pokfulam Rd Hong Kong Hong Kong Peoples R China

ISBN: (纸本)9783642338854;9783642338847

The mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multi-body motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates and influence clustering accuracy. We propose to use both subspaces and observations to improve performance. Furthermore, while these mean shift methods use fixed metrics for computing distances, we prefer an adaptive distance measure. The insight is, we can use temporary modes in a mode seeking process to improve this measure and obtain better performance. In this paper, an adaptive mode seeking algorithm is proposed for clustering linear subspaces. By experiments, the proposed algorithm compares favorably to the state-of-the-art algorithm in terms of clustering accuracy.

关键词： mean shift algorithm Metric Learning

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