Spatial clustering is a widely used technique in spatial analysis that groups similar objects together based on their proximity in space. However, traditional clustering algorithms often fail to ensure the accessibili...
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Spatial clustering is a widely used technique in spatial analysis that groups similar objects together based on their proximity in space. However, traditional clustering algorithms often fail to ensure the accessibility of cluster centers, which limits their validity in practical applications such as facility location problems. To address this issue, this article introduces a novel mean shift algorithm that incorporates reachable distance and an iterative mechanism to accurately locate cluster centers. The proposed algorithm initially labels clustering elements with road network coordinates to facilitate the calculation of reachable distance and the cluster center iterative mechanism. Subsequently, the meanshift vector function is modified to employ reachable distance as the measure of geographic reachable similarity. Unlike existing algorithms, our approach allows for cluster centers to be positioned independently of the clustering elements, guaranteeing geographical accessibility. Through simulation experiments, we demonstrate that our proposed algorithm not only outperforms existing methods in terms of solution quality, but also effectively addresses the limitations of disregarding geographical obstacles and unreachable cluster centers.
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 salie...
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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.
In this research, we have measured the physical distance between the robot and its surroundings using a laser distance measuring device that we have developed, designed controllers for, and tested operationally. We wi...
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In this research, we have measured the physical distance between the robot and its surroundings using a laser distance measuring device that we have developed, designed controllers for, and tested operationally. We will record the distance using the USB camera and integrate the LDMSB board into the laser distance measuring design. We will fasten these two parts to the robot's underside. Developing the experiment in LabVIEW is the next step. The meanshift method enables us to move the robot's position by relocating a laser-based distance measurement device and capturing a photo at that location. In order to record that area, we will perform a perspective camera calibration. This will allow us to set up or adjust the camera system's value, or provide visual assistance to ensure that the viewing angle is precisely aligned with the intended view angle. The laser measurement results ranged from one to fifteen meters. A device that makes use of lasers has 99.25% accuracy. Every calibration location throughout the 10 has a precision rating of 94.03%.
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 hybr...
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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 meanshift process and the color histogram process is proposed. Using the statistical robustness of the meanshift 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.
A subspace constrained meanshift (SCMS) algorithm is a non-parametric iterative technique to estimate principal curves. Principal curves, as a nonlinear generalization of principal components analysis (PCA), are smoo...
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A subspace constrained meanshift (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 meanshift (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.
作者：
Wang, JinSch Police Adm
Railway Police Coll 31 Nong Ye Rd Zhengzhou Henan Peoples R China
Facing COVID-19 epidemic, many countries have recently strengthened epidemic prevention and control measures. The reliability of safety management is of great significance to personnel management and control during th...
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Facing COVID-19 epidemic, many countries have recently strengthened epidemic prevention and control measures. The reliability of safety management is of great significance to personnel management and control during the COVID-19 epidemic period. The focus of security management of early warning is to monitor and identify the moving target. The current optical flow method is vulnerable to the influence of light changes and background movement, and it is not very accurate for moving target detection in dynamic complex background. In this paper, aiming at the traditional Lucas Kanade optical flow method, the inter frame difference method, meanshift clustering algorithm and morphological processing are combined to optimize and improve on the original basis, so that the moving target detection effect in both simple and complex environments is significantly improved. At the same time, the improved algorithm also reduces the execution time to a certain extent, and has a certain resistance to noise interference such as light changes. This has a certain ability test value for personnel control during the epidemic.
Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morph...
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Exudates are a serious complication causing blindness in diabetic retinopathy patients. The main objective of this paper is to develop a novel method to detect exudates lesions in color retinal images by using a morphology mean shift algorithm. The proposed method start with a normalization of the retinal image, contrast enhancement, noise removal, and the localization of the OD. Then, a coarse segmentation method by using meanshift provides a set of exudates and non-exudates candidates. Finally, a classification using the mathematical morphology algorithm (MMA) procedure is applied in order to keep only exudates pixels. The optimal value parameters of the MMA will facilitate an increase of the accuracy results from the solely MSA method by 13.10%. Based on a comparison between the results and ground truth images, the proposed method obtained an average sensitivity, specificity, and accuracy of detecting exudates as 98.40%, 98.13%, and 98.35%, respectively.
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(MSR...
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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.
Based on the basic principle of mean shift algorithm, this paper proposes an improved target detection and tracking method based on mixture gauss model and mean shift algorithm, aiming at the complex background proble...
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Based on the basic principle of mean shift algorithm, this paper proposes an improved target detection and tracking method based on mixture gauss model and mean shift algorithm, aiming at the complex background problems such as occlusion, shadow, illumination change, etc. The method uses the color feature in YCbCr color space as the target feature, uses the weighted operation of background and target to eliminate the interference of environmental noise, and highlights the effective information of the target itself. In the process of tracking, the target template is constantly updated to keep as consistent as possible with the target state, so as to achieve accurate, real-time and stable tracking of the target in the video stream. The experimental results show that the improved algorithm can effectively reduce the number of iterations and has a good tracking effect.
The meanshift (MS) algorithm is an iterative method introduced for locating modes of a probability density function. Although the MS algorithm has been widely used in many applications, the convergence of the algorit...
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The meanshift (MS) algorithm is an iterative method introduced for locating modes of a probability density function. Although the MS algorithm has been widely used in many applications, the convergence of the algorithm has not yet been proven. In this study, the authors modify the MS algorithm in order to guarantee its convergence. The authors prove that the generated sequence using the proposed modified algorithm is a convergent sequence and the density estimate values along the generated sequence are monotonically increasing and convergent. In contrast to the MS algorithm, the proposed modified version does not require setting a stopping criterion a priori;instead, it guarantees the convergence after a finite number of iterations. The proposed modified version defines an upper bound for the number of iterations which is missing in the MS algorithm. The authors also present the matrix form of the proposed algorithm and show that, in contrast to the MS algorithm, the weight matrix is required to be computed once in the first iteration. The performance of the proposed modified version is compared with the MS algorithm and it was shown through the simulations that the proposed version can be used successfully to estimate cluster centres.
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