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检索条件"主题词=Mean Shift algorithm"

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A Novel Strategy for Kernel-Based Small Target Tracking against Varying Illumination with Multiple Features Fusion

A Novel Strategy for Kernel-Based Small Target Tracking agai...

引用

International Conference on Information and Computer Technologies (ICICT)

作者： Chen, Weibin Niu, Ben Gu, Hongbin Zhang, Xin Nanjing Univ Aeronaut & Astronaut Coll Civil Aviat Nanjing Jiangsu Peoples R China Wenzhou Med Univ Sch Biomed Engn Wenzhou Peoples R China

ISBN: (纸本)9781538653845

This paper presents a novel kernel-based method for small Target Tracking with multi-feature fusion against Varying Illumination. Firstly, the conventional tracker based on color histogram is unreliable or even failed under varying illumination. Therefore, a new fuzzy color histogram creation is proposed based on the HSV color space and utilizes the local background information around tracking target to dynamically correct its fuzzy color histogram model and eliminates the sensitive of conventional color histogram to illumination change and noise. Secondly, there is still not an effective method to cope with object occlusion, angle variation, scale change etc. The tracking algorithm utilizes feature points extracted by improved SIFT as the reference points of mean-shift and calculates the target area center, which combines the two methods together seamlessly. Lastly, the whole tracking algorithm utilizes fuzzy color histogram model and combination of improved SIFT as the reference points of mean-shift for small target tracking. Experiment results show that the proposed algorithm can keep tracking object of varying scales and various illumination even when the surrounding background being similar to the object's appearance.

关键词： object tracking mean shift algorithm kernel illumination change

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Customer Segmentation using K-means Clustering 1

Customer Segmentation using K-means Clustering

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1st International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS)

作者： Kansal, Tushar Bahuguna, Suraj Singh, Vishal Choudhury, Tanupriya UPES Dept Informat Sch Comp Sci Dehra Dun Uttar Pradesh India

ISBN: (纸本)9781538677094

The zeitgeist of modern era is innovation, where everyone is embroiled into competition to be better than others. Today's business run on the basis of such innovation having ability to enthral the customers with the products, but with such a large raft of products leave the customers confounded, what to buy and what to not and also the companies are nonplussed about what section of customers to target to sell their products. This is where machine learning comes into play, various algorithms are applied for unravelling the hidden patterns in the data for better decision making for the future. This elude concept of which segment to target is made unequivocal by applying segmentation. The process of segmenting the customers with similar behaviours into the same segment and with different patterns into different segments is called customer segmentation. In this paper, 3 different clustering algorithms (k-means, Agglomerative, and meanshift) are been implemented to segment the customers and finally compare the results of clusters obtained from the algorithms. A python program has been developed and the program is been trained by applying standard scaler onto a dataset having two features of 200 training sample taken from local retail shop. Both the features are the mean of the amount of shopping by customers and average of the customer's visit into the shop annually. By applying clustering, 5 segments of cluster have been formed labelled as Careless, Careful, Standard,Target and Sensible customers. However, two new clusters emerged on applying mean shift clustering labelled as High buyers and frequent visitors and High buyers and occasional visitors.

关键词： Customer Segmentation k-means algorithm mean shift algorithm Agglomerative algorithm Machine learning Python

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A Two-stage Charging Facilities Planning Method for Electric Vehicle Sharing Systems 56

A Two-stage Charging Facilities Planning Method for Electric...

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IEEE/IAS 56th Industrial and Commercial Power Systems Technical Conference

作者： Wang, Ning Wang, Chengfu Niu, Yuanfang Yang, Ming Yu, Yixiao Shandong Univ Jingshi Rd Jinan Peoples R China Jingshi Rd Jinan Peoples R China Shandong Elect Power Engn Consulting Inst Co Ltd Jinan Peoples R China

ISBN: (纸本)9781728171951

The large-scale sharing electric vehicle (SEV) need adequate charging infrastructure to maintain the optimal operation. Considering the convenience of SEV users' charging during driving, a two-stage planning method combining charging stations and scattered charging piles for SEV is proposed to balance the economy of charging system and the convenience of SEVs user. In the first stage, according to the urban area division and trip characteristics of SEV, charging power demand of SEV in planning area can be estimated exactly. Then, aiming for the minimum annual total cost, and the mean clustering algorithm and queuing theory are used to determine the location and capacity of charging stations. In the second stage, an improved S-shaped function is used to assessment the unsatisfactory charging demand, and the compensated charging pile without in the station is planned based on the mean shift algorithm. Based on the re-division of the service scope, the capacity of the charging stations is updated. Finally, the simulation results verify the proposed model and algorithm is effective and feasible.

关键词： charging facility mean clustering mean shift algorithm queuing theory sharing electric vehicle two-stage planning

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Research on Hybrid Tracking algorithm Based on Particle Filter 3

Research on Hybrid Tracking Algorithm Based on Particle Filt...

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3rd IEEE International Conference on Control, Automation and Robotics (ICCAR)

作者： Du, Yunming Shi, Qingjun Jiang, Yongcheng Yan, Bingbing Jiamusi Univ Coll Informat & Elect Technol Jiamusi Heilongjiang Pr Peoples R China Jiamusi Univ Coll Mech Engn Jiamusi Heilongjiang Pr Peoples R China

ISBN: (纸本)9781509060887

Aiming at the problem that the traditional particle filter target tracking algorithm is poor in real time and has the degradation phenomenon, this paper proposes a hybrid tracking algorithm based on multi-technology fusion. The algorithm constructs the probability density function using unscented Kalman filter and adds the latest observations, so that the sample set is closer to the real state of the system. By employing mean shift algorithm to complete particles propagation, the particles aggregate to the high posterior probability region much further and effectively improves the sampling efficiency. The algorithm reduces the computational cost and solves the problem of degradation and real time through making the particle close to the actual state posterior distribution and increasing the number of effective particles. Simulation experiment results show that the proposed algorithm has obvious improvement in real-time and tracking error compared with the traditional particle filter tracking algorithm.

关键词： tracing algorithm particle filter unscented Kalman filter mean shift algorithm

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Moving object tracking using particle filter and observational model based on multi-feature composition 22

Moving object tracking using particle filter and observation...

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22nd Iranian Conference on Electrical Engineering (ICEE)

作者： Behzadfar, N. Ansarian, M. Sadaghiani, M. Saadat Co R&D Dept Tehran Iran Tarbiat Modares Univ Elect Engn Dept Tehran Iran

ISBN: (纸本)9781479944095

moving object tracking in a sequence of an image is one of the favorable issues in machine vision. Recently, particle filter have based developed as a powerful method in this field. Particle filter is a following method which estimates a target route in video image sequences by probability approaches. In many cases, the follower encounters with problems such as: local lighting variations or abrupt movements. In most of these cases, target tracking is missing, so an appropriate filter coupled observational model is required to improve follower performance and increase the efficiency. An observational model is used in order to improve filter performance. However, this color feature based model contains less computational volume and is more rapid but does not provide good performance at the presence of background color or some objects with similar color. In this paper, an observational model is proposed that performs using particle filter accompanied by mean shift algorithm based on incorporated color and edge features. The results show that the introduced method is not sensitive to color and intensity change while it also has a good performance.

关键词： tracking particle filterr mean shift algorithm color edge

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On-line Data Analysis and Optimization of Crankshaft Dynamic Balance 3

On-line Data Analysis and Optimization of Crankshaft Dynamic...

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3rd International Conference on Artificial Intelligence Applications and Technologies (AIAAT)

作者： Zhou, Chuanhong Ding, Lei Lin, Lianghuang Shanghai Univ Sch Mechatron Engn & Automat Shanghai Key Lab Intelligent Mfg & Robot Shanghai Peoples R China

The crankshaft is a high-speed rotating component that is widely used in many fields. Dynamic balance of the crankshaft is an important process stage that affects the quality of the crankshaft. In order to optimize the quality of crankshaft dynamic balance machining, this paper illustrates the importance of the initial unbalance of crankshaft to the dynamic balance machining of crankshaft. For the initial imbalance distribution of crankshaft, the improved mean method, gradient descent method and mean shift algorithm are used to calculate the machining center. The position of the hole realizes the crankshaft imbalance correction with different effects and reduces the processing strength of the crankshaft dynamic balance phase. The study is applicable to the machining of crankshafts in geometric centering processes in batches.

关键词： crankshaft dynamic balance online control mean method gradient descent method mean shift algorithm

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Outdoor Image Region Recognition Based on MS-OS-BOW Model 5

Outdoor Image Region Recognition Based on MS-OS-BOW Model

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5th International Conference on Computer Science and Network Technology (ICCSNT)

作者： Li, You Zhang, Zhaogong Li, Jianzhong Liu, Yong Heilongjiang Univ Coll Comp Sci & Technol Harbin Heilongjiang Peoples R China

ISBN: (纸本)9781509021291

To recognize accessible regions successfully and accurately is an important part of mobile robot navigation in outdoor environment. This thesis studies that the modified mean shift algorithm is applied for region partition on outdoor images obtained by robot vision. Then all parts of these pictures are recognized through the combination BOW model and the Opponent SIFT feature. And finally the results of region recognition are optimized through SVM and the way of using MS-OS-BOW (mean shift-Opponent SIFT-Bag Of Words) model to solve the problem of outdoor image region recognition is proposed. Specifically, outdoor working robots need to analyze received visual images to search for accessible path. For the outdoor images with broad angle of view and simple layout, whether it is accessible path or not can be decided by the semantics of this region. Therefore, the understanding of outdoor images can be finished by region recognition. These final experimental results demonstrate the effectiveness and practicability of outdoor image region recognition based on MS-OS-BOW model.

关键词： mean shift algorithm region recognition BOW model SVM model

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Research on Target Tracking algorithm Based on Spatio-Temporal Context 9

Research on Target Tracking Algorithm Based on Spatio-Tempor...

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9th International Conference on Digital Image Processing (ICDIP)

作者： Li, Baiping Xu, Sanmei Kang, Hongjuan Xian Univ Sci & Technol Coll Commun & Informat Engn Xian 710054 Shaanxi Peoples R China

ISBN: (纸本)9781510613058;9781510613041

In this paper, a novel target tracking algorithm based on spatio-temporal context is proposed. During the tracking process, the camera shaking or occlusion may lead to the failure of tracking. The proposed algorithm can solve this problem effectively. The method use the spatio-temporal context algorithm as the main research object. We get the first frame's target region via mouse. Then the spatio-temporal context algorithm is used to get the tracking targets of the sequence of frames. During this process a similarity measure function based on perceptual hash algorithm is used to judge the tracking results. If tracking failed, reset the initial value of mean shift algorithm for the subsequent target tracking. Experiment results show that the proposed algorithm can achieve real-time and stable tracking when camera shaking or target occlusion.

关键词： target tracking mean shift algorithm spatio-temporal context perceptual hash

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IMAGE SEGMENTATION algorithm BASED ON CLUSTERING 17

IMAGE SEGMENTATION ALGORITHM BASED ON CLUSTERING

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Wu, Hao Li, Gong-Fa Sun, Ying Tao, Bo Kong, Jian-Yi Xu, Shuang Wuhan Univ Sci & Technol Key Lab Met Equipment & Control Technol Minist Educ Wuhan 430083 Peoples R China Wuhan Univ Sci & Technol Hubei Key Lab Mech Transmiss & Mfg Engn Wuhan 430083 Peoples R China Wuhan Univ Sci & Technol Res Ctr Biol Manipulator & Intelligent Measuremen Wuhan 430081 Peoples R China Wuhan Univ Sci & Technol 3D Printing & Intelligent Mfg Engn Inst Wuhan 430081 Peoples R China

ISBN: (纸本)9781538652145

Image segmentation plays an important role in image processing. Image segmentation algorithms have been proposed as early as the last century, and constantly find and optimize various algorithms. The quality of the image segmentation algorithm determines the result of image analysis and image understanding. The principle, advantages and disadvantages of traditional image segmentation algorithms are briefly introduced in this paper. The variety of image segmentation algorithms is determined by the complexity of the image itself. In recent years, scholars continue to improve a variety of image segmentation algorithms, the paper introduces the improvement of fuzzy C-means algorithm and mean-shift algorithm. The fuzzy C-means algorithm does not consider the spatial information of the image. Put forward an fuzzy C-means algorithm based on membership correction is proposed, taking into account the high correlation of pixels in image segmentation. The mean shift algorithm converges slowly, and mean shift algorithm based on conjugate gradient method is proposed to improve the convergence speed of the algorithm.

关键词： Image segmentation Clustering Fuzzy C-means algorithm mean shift algorithm

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Role of Hybrid Spectral Similarity Measures for Semi-Supervised Fuzzy Classifier

Role of Hybrid Spectral Similarity Measures for Semi-Supervi...

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作者： SURBHI SHARMA University of Twente

学位级别：硕士

To elucidate the information present in a remote sensing image, classification has helped significantly. With the presence of uncertainties in remotely sensed images, soft classification techniques are often considered. The desideratum for good classification accuracies with minimized efforts has led to the options where a few labeled data could enhance the classification accuracies. In this research, a semi- supervised approach is considered to minimize the efforts in collecting huge labeled training samples which is often strenuous and time-consuming. To handle uncertainties better, a Possibilistic c means classifier is used and considered with different approaches as per the labeled training data. The similarity between two pixel vectors becomes important when it comes to collecting unlabeled data in a semi- supervised approach, or when the classification is performed based on assigning more membership values to similar pixels. The hybrid spectral similarity measures, Spectral Information Divergence with Spectral Angle Measure and Spectral Information Divergence with Spectral Correlation Angle are used to measure the similarity between two pixel vectors due to their proven capabilities for capturing high band-to-band variability for a hyperspectral imagery. Their roles are studied in view of a multispectral imagery. The Possibilistic c means classifier is used with semi-supervised training data using different hybrid measures. Due to the availability of a few labeled training samples, mean shift algorithm is employed to refine the training data and to shift the mean for a Possibilistic c means classification algorithm to a higher density region. In addition, the proposed methods relate the bandwidth parameter from mean shift algorithm to the bandwidth or resolution parameter of Possibilistic c means classifier with an iterative procedure to capture the class variances. The methods are applied to an input LANDSAT-8 imagery with 30 m spatial resolution. The classif

关键词： Possibilistic c means Semi-supervised learning mean shift algorithm Spectral similarity measures

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