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An adaptive segmentation method combining MSRCR and mean shift algorithm with K-means correction of green apples in natural environment

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Information Processing in Agriculture 2019年第2期6卷 200-215页

作者： Sashuang Sun Huaibo Song Dongjian He Yan Long College of Mechanical and Electronic Engineering Northwest A&F UniversityYanglingShaanxi 712100China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural AffairsYanglingShaanxi 712100China Shaanxi Key Laboratory of Agricultural Information Perception and Intelligent Service YanglingShaanxi 712100China

During the recognition and localization process of green apple targets,problems such as uneven illumination,occlusion of branches and leaves need to be *** this study,the multi-scale Retinex with color restoration(MSRCR)algorithm was applied to enhance the original green apple images captured in an orchard environment,aiming to minimize the impacts of varying light *** enhanced images were then explicitly segmented using the mean shift algorithm,leading to a consistent gray value of the internal pixels in an independent *** that,the fuzzy attention based on information maximization algorithm(FAIM)was developed to detect the incomplete growth position and realize threshold ***,the poorly segmented images were corrected using the K-means algorithm according to the shape,color and texture *** users intuitively acquire the minimum enclosing rectangle localization results on a PC.A total of 500 green apple images were tested in this *** with the manifold ranking algorithm,the K-means clustering algorithm and the traditional mean shift algorithm,the segmentation accuracy of the proposed method was 86.67%,which was 13.32%,19.82%and 9.23%higher than that of the other three algorithms,***,the false positive and false negative errors were 0.58%and 11.64%,respectively,which were all lower than the other three compared *** proposed method accurately recognized the green apples under complex illumination conditions and growth ***,it provided effective references for intelligent growth monitoring and yield estimation of fruits.

关键词： Green fruit Adaptive segmentation MSRCR algorithm mean shift algorithm K-means clustering algorithm Manifold ranking algorithm

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Adaptive strategy for sports video moving target detection and tracking technology based on mean shift algorithm

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INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2021年 1-11页

作者： Yu, Hongquan Sharma, Amit Sharma, Parv Jingdezhen Univ Phys Educ Dept Jingdezhen 333000 Peoples R China Chitkara Univ Dept Comp Sci & Engn Chandigarh Punjab India JUIT Waknaghat Dept Comp Sci Solan Himachal Prades India

The continuous improvement in the level of sports competition has led to many recent research designs for providing easy and quick ways for athlete training. The aim behind this research is to present an adaptive hybrid non-rigid target tracking method by adopting (mean-shift) and color histogram algorithm to process the characteristics of sports video. This work attempts in designing a tracking algorithm by implementing mean shift algorithm for tracking the object characteristics of sports objects. The experimental analysis presents the ideal effects of proposed approach in precision tracking. mean shift algorithm uses the gradient method to iteratively calculate the extreme points of the probability density function using its characteristics of no parameters and fast pattern matching. In order to realize the tracking of human targets in sports videos, a tracking approach combining the mean shift process and the color histogram process is proposed. Using the statistical robustness of the mean shift process and the characteristics of rapid convergence along the direction of the density gradient, matching of the color histogram to the target shape is done. It solves the problem of variable target shape and high tracking complexity. The proposed method yields 96.04% precision and 97.10% accuracy value for tracking and recognition. The experimental outcomes obtained for the research provides the suitable evidence that the approach presented in this paper has an ideal effect.

关键词： Sports video Target detection Target tracking mean shift algorithm Tracking complexity

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On the convergence of the mean shift algorithm in the one-dimensional space

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PATTERN RECOGNITION LETTERS 2013年第12期34卷 1423-1427页

作者： Ghassabeh, Youness Aliyari Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

The mean shift algorithm is a non-parametric and iterative technique that has been used for finding modes of an estimated probability density function. It has been successfully employed in many applications in specific areas of machine vision, pattern recognition, and image processing. Although the mean shift algorithm has been used in many applications, a rigorous proof of its convergence is still missing in the literature. In this paper we address the convergence of the mean shift algorithm in the one-dimensional space and prove that the sequence generated by the mean shift algorithm is a monotone and convergent sequence. (C) 2013 Elsevier B.V. All rights reserved.

关键词： mean shift algorithm Mode estimate sequence Monotone sequence Kernel function Convex function Convergence

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Modified Subspace Constrained mean shift algorithm

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JOURNAL OF CLASSIFICATION 2021年第1期38卷 27-43页

作者： Ghassabeh, Youness Aliyari Rudzicz, Frank Univ Toronto Dept Comp Sci Toronto ON Canada St Michaels Hosp Li Ka Shing Knowledge Inst Toronto ON Canada Surg Safety Technol Inc Toronto ON Canada Vector Inst Artificial Intelligence Toronto ON Canada

A subspace constrained mean shift (SCMS) algorithm is a non-parametric iterative technique to estimate principal curves. Principal curves, as a nonlinear generalization of principal components analysis (PCA), are smooth curves (or surfaces) that pass through the middle of a data set and provide a compact low-dimensional representation of data. The SCMS algorithm combines the mean shift (MS) algorithm with a projection step to estimate principal curves and surfaces. The MS algorithm is a simple iterative method for locating modes of an unknown probability density function (pdf) obtained via a kernel density estimate. Modes of a pdf can be interpreted as zero-dimensional principal curves. These modes also can be used for clustering the input data. The SCMS algorithm generalizes the MS algorithm to estimate higher order principal curves and surfaces. Although both algorithms have been widely used in many real-world applications, their convergence for widely used kernels (e.g., Gaussian kernel) has not been sown yet. In this paper, we first introduce a modified version of the MS algorithm and then combine it with different variations of the SCMS algorithm to estimate the underlying low-dimensional principal curve, embedded in a high-dimensional space. The different variations of the SCMS algorithm are obtained via modification of the projection step in the original SCMS algorithm. We show that the modification of the MS algorithm guarantees its convergence and also implies the convergence of different variations of the SCMS algorithm. The performance and effectiveness of the proposed modified versions to successfully estimate an underlying principal curve was shown through simulations using the synthetic data.

关键词： mean shift algorithm Subspace constrained mean shift algorithm Convergent sequence Principal curves Principal surfaces Clustering

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Application of cultural elements of dunhuang murals in landscape design based on mean shift algorithm extraction

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2024年第1期24卷 473-487页

作者： Cui, Yongbin Mianyang Teachers Coll Sichuan Collegiate Engn Res Ctr Chuanxibei RHS Co Mianyang Sichuan Peoples R China

Although picture extraction is challenging, the murals at Dunhuang are historically significant and offer rich content. The work suggests an image segmentation model based on the mean shift algorithm and an area salience prioritisation model to extract the cultural aspects in the Dunhuang murals for landscape design. First, an image segmentation model based on the mean shift algorithm is established, and then a region salience value calculation method and a region prioritisation method are designed to establish a region salience prioritisation model. The outcomes showed that a segmentation model built using the mean shift algorithm in the study processed a 405175 image with a processing time of 3.18 seconds, an edge integrity rate of 88.9%, an accuracy rate of 87.4%, an F-value of 88.7%, and a total of 302 regions. The segmented Dunhuang image featured few noise points and a distinct shape. Salient region transfer path is more regular and more in line with the human visual transfer mechanism thanks to the research design of the region saliency value calculation method, which also improves saliency detection performance. The highest correct rate when dividing the image is 0.97, the highest check rate is 0.8, and the highest F1 value is 1. In conclusion, the study's methodology has some favourable implications for landscape design and may be effectively used to extract cultural components from photographs.

关键词： mean shift algorithm Image extraction landscaping salient region

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A sufficient condition for the convergence of the mean shift algorithm with Gaussian kernel

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JOURNAL OF MULTIVARIATE ANALYSIS 2015年 135卷 1-10页

作者： Ghassabeh, Youness Aliyari Queens Univ Dept Math & Stat Kingston ON K7L 3N6 Canada

The mean shift (MS) algorithm is a non-parametric, iterative technique that has been used to find modes of an estimated probability density function (pdf). Although the MS algorithm has been widely used in many applications, such as clustering, image segmentation, and object tracking, a rigorous proof for its convergence is still missing. This paper tries to fill some of the gaps between theory and practice by presenting specific theoretical results about the convergence of the MS algorithm. To achieve this goal, first we show that all the stationary points of an estimated pdf using a certain class of kernel functions are inside the convex hull of the data set. Then the convergence of the sequence generated by the MS algorithm for an estimated pdf with isolated stationary points will be proved. Finally, we present a sufficient condition for the estimated pdf using the Gaussian kernel to have isolated stationary points. (C) 2014 Elsevier Inc. All rights reserved.

关键词： mean shift algorithm Mode estimate sequence Convex hull Isolated stationary points Kernel function Gaussian KDE Convergence

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An Improved mean shift algorithm for Moving Object Tracking 28

An Improved Mean Shift Algorithm for Moving Object Tracking

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IEEE 28th Canadian Conference on Electrical and Computer Engineering (CCECE)

作者： Li, Ning Zhang, Dan Gu, Xiaorong Huang, Li Liu, Wei Xu, Tao Nanjing Univ Aeronaut & Astronaut Key Lab Radar Imaging & Microwave Photon Minist Educ Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Elect & Informat Engn Nanjing 210016 Jiangsu Peoples R China Nanjing Univ Aeronaut & Astronaut Coll Sci Nanjing 210016 Jiangsu Peoples R China Civil Aviat Univ China Informat Technol Res Base Civil Aviat Adm China Tianjin 300300 Peoples R China

ISBN: (纸本)9781479958290

Moving object tracking is one of the key technologies in video surveillance. mean shift algorithm fails to track the moving object in complicated environment. In this paper, a new strategy is proposed to improve the tracking ability of mean shift algorithm, in which the contrast between object and background along with similarity evaluation are applied for generating and updating object model. To eliminate the interference of the most similar features between tracking object and background, the coefficient ratio of the object to surrounding environment is first imported to generate the object model. To make sure the accuracy of updating object model, the effective way that combines similarity evaluation and Kalman filtering prediction is then applied for judge whether the tracking object is sheltered by other objects or background. The experimental results have shown that the proposed method can tack the moving object stably.

关键词： moving object tracking video surveillance object model generation mean shift algorithm Kalman filtering prediction

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VISUAL OBJECT TRACKING USING KALMAN FILTER, mean shift algorithm AND SPATIOTEMPORAL ORIENTED ENERGY FEATURES 4

VISUAL OBJECT TRACKING USING KALMAN FILTER, MEAN SHIFT ALGOR...

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4th International Conference on Computer and Knowledge Engineering (ICCKE)

作者： Ghahremani, Amir Mousavinia, Amir KN Toosi Univ Technol Dept Elect Engn Tehran Iran

ISBN: (纸本)9781479954865

Many multimedia applications need to track moving objects. Consequently, designing a robust tracking system is a vital requirement for them. This paper proposes a new method for visual object tracking, which uses the mean shift tracking algorithm to derive the most similar target candidate to the target model. Bhattacharyya coefficient is employed to determine the similarities. Target's structure is represented by multiscale oriented energy feature set, which presents extra robustness by including dynamic information of the pixels. Likewise, the Kalman filtering framework is employed to predict the location of the moving objects. Experimental results demonstrate the proposed algorithm's superior performance, chiefly when encountering with the full occlusion situation.

关键词： visual object tracking mean shift algorithm Kalman filler energy full occlusion

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Object Tracking using mean shift algorithm with Illumination Invariance 5

Object Tracking using Mean Shift Algorithm with Illumination...

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2015 Fifth International Conference on Communication Systems and Network Technologies (CSNT)

作者： Baig, Mirza Zeeshan Gokhale, Aniket V. YCCE Dept Elect Engn Nagpur Maharashtra India

ISBN: (纸本)9781479917976

Visual tracking is a very challenging task in real life applications because of the instability in illuminations. An illumination or brightness variation is the significant issue in the tracking of object of interest. A satisfying method is proposed in this paper to correct the non uniformity in illumination. In this method discrete cosine transformation (DCT) is used to normalize the variations in illumination in logarithmic domain. High frequency coefficients of discrete cosine transformation are discarded because illuminations are reflected in low frequency coefficients only. We compensate the illumination variations by modifying DC coefficients and taking average of these DC coefficients of neighboring frames of current frame. The corrected illumination videos are applied to mean shift algorithm to track the object of interest.

关键词： mean shift algorithm illumination DCT DC coefficients

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Application of mean shift algorithm in Real-time Facial Expression Recognition

Application of Mean Shift Algorithm in Real-time Facial Expr...

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1st International Symposium on Computer Network and Multimedia Technology

作者： Peng Zhao-yi Wen Zhi-qiang Zhou Yu Hunan Univ Technol Sch Comp & Commun Zhuzhou Hunan Peoples R China Hunan Univ Technol Sch Elect & Informat Engn Zhuzhou Hunan Peoples R China

ISBN: (纸本)9781424452729

In a dynamic real-time facial expression recognition, accurate and fast face tracking is a very important preparatory work that is in order to obtain the image sequence of facial expressions. For this problem, we proposed a mean shift algorithm for real-time tracking human faces, and using this method we can obtain the facial expressions image sequence. In order to obtain the initial target of the face image, we used an adaptive skin-color face detection method. Then we used the geometric model based on human face to locate the region of facial expression features, and can estimate the optical flow to calculate the Eigen-flow vectors. At last, hidden semi-Markov model is used for facial expression recognition. The experimental results show that the application of mean shift algorithm in real-time facial expression recognition is very effective for obtaining the facial expression image sequence quickly and accurately.

关键词： mean shift algorithm face tracking facial expression recognition image sequence HSMM

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