Mean Shift Segmentation Algorithm Based on Hybridized Bacterial Chemotaxis

2012 ◽  
Vol 468-471 ◽  
pp. 2019-2023
Author(s):  
Yan Ling Li ◽  
Gang Li
Keyword(s):  
Mean Shift ◽  
Bacterial Chemotaxis ◽  
Local Maximum ◽  
Optimization Methods ◽  
Segmentation Algorithm ◽  
Global Maximum ◽  
Stochastic Local Search ◽  
Shift Vector ◽  
Operation Algorithm ◽  
Maximum Minimum

Mean shift, like other gradient ascent optimization methods, is susceptible to local maximum/minimum, and hence often fails to find the desired global maximum/minimum. For this reason, mean shift segmentation algorithm based on hybridized bacterial chemotaxis (HBC) is proposed in this paper. In HBC, particle swarm operation algorithm(PSO) is introduced before bacterial chemotaxis(BC) works. And PSO is firstly introduced to execute the global search, and then stochastic local search works by BC. Meanwhile, elitism preservation is used in the paper in order to improve the efficiency of the new algorithm. After mean shift vector is optimized using HBC algorithm, the optimal mean shift vector is updated using mean shift procedure. Experimental results show that new algorithm not only has higher convergence speed, but also can achieve more robust segmentation results.

Computationally Efficient Mean-Shift Parallel Segmentation Algorithm for High-Resolution Remote Sensing Images

2018 ◽  
Vol 46 (11) ◽  
pp. 1805-1814
Author(s):  
Tianjun Wu ◽  
Liegang Xia ◽  
Jiancheng Luo ◽  
Xiaocheng Zhou ◽  
Xiaodong Hu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Computationally Efficient ◽  
Remote Sensing Images

scholarly journals A Self-Adaptive Mean Shift Tree-Segmentation Method Using UAV LiDAR Data

2020 ◽  
Vol 12 (3) ◽  
pp. 515 ◽  
Author(s):  
Wanqian Yan ◽  
Haiyan Guan ◽  
Lin Cao ◽  
Yongtao Yu ◽  
Cheng Li ◽  
...  
Keyword(s):  
Mean Shift ◽  
Three Dimensional ◽  
Estimation Method ◽  
Global Maximum ◽  
Maximum Point ◽  
Individual Tree ◽  
3D Space ◽  
Segmentation Accuracy ◽  
Optimal Kernel ◽  
Self Adaptive

Unmanned aerial vehicles using light detection and ranging (UAV LiDAR) with high spatial resolution have shown great potential in forest applications because they can capture vertical structures of forests. Individual tree segmentation is the foundation of many forest research works and applications. The tradition fixed bandwidth mean shift has been applied to individual tree segmentation and proved to be robust in tree segmentation. However, the fixed bandwidth-based segmentation methods are not suitable for various crown sizes, resulting in omission or commission errors. Therefore, to increase tree-segmentation accuracy, we propose a self-adaptive bandwidth estimation method to estimate the optimal kernel bandwidth automatically without any prior knowledge of crown size. First, from the global maximum point, we divide the three-dimensional (3D) space into a set of angular sectors, for each of which a canopy surface is simulated and the potential tree crown boundaries are identified to estimate average crown width as the kernel bandwidth. Afterwards, we use a mean shift with the automatically estimated kernel bandwidth to extract individual tree points. The method is iteratively implemented within a given area until all trees are segmented. The proposed method was tested on the 7 plots acquired by a Velodyne 16E LiDAR system, including 3 simple plots and 4 complex plots, and 95% and 80% of trees were correctly segmented, respectively. Comparative experiments show that our method contributes to the improvement of both segmentation accuracy and computational efficiency.

Implementation of the parallel mean shift-based image segmentation algorithm on a GPU cluster

2018 ◽  
Vol 12 (3) ◽  
pp. 328-353 ◽  
Author(s):  
Fang Huang ◽  
Yinjie Chen ◽  
Li Li ◽  
Ji Zhou ◽  
Jian Tao ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Gpu Cluster ◽  
Image Segmentation Algorithm

scholarly journals An Intelligent Automatic Adaptive Maximum Power Point Tracker for Photovoltaic Module Arrays

Energies ◽  
2020 ◽  
Vol 13 (18) ◽  
pp. 4775
Author(s):  
Kuei-Hsiang Chao ◽  
Yu-Ju Lai
Keyword(s):  
Digital Signal Processor ◽  
Local Maximum ◽  
Maximum Power ◽  
Photovoltaic System ◽  
Global Maximum ◽  
Photovoltaic Module ◽  
Maximum Power Point ◽  
Maximum Power Point Tracker ◽  
Tlbo Algorithm ◽  
Power Point

In this study, a maximum power point tracker was developed for photovoltaic module arrays by using a teacher-learning-based optimization (TLBO) algorithm to control the photovoltaic system. When a photovoltaic module array is shaded, a conventional maximum power point tracker may obtain the local maximum power point rather than the global maximum power point. The tracker developed in this study was aimed at solving this problem. To prove the viability of the proposed method, a SANYO HIP 2717 photovoltaic module with diverse connection patterns and shading ratios was used. Thus, single-peak, double-peak, triple-peak, and multi-peak power–voltage characteristic curves of the photovoltaic module array were obtained. A simulation of maximum power point tracking (MPPT) was then performed with MATLAB software. With regard to practical testing, a boost converter was used as the hardware structure of the maximum power point tracker and a TMS320F2808 digital signal processor was selected to execute the rules for MPPT. The results of the practical tests verified that the proposed improved TLBO algorithm had a superior accuracy to existing TLBO algorithms. In addition, the proposed improved TLBO algorithm can shorten the tracking time to 1/2 or 1/4, so it can improve the efficiency of power generation by two to three percentage.

Fast and accurate mean-shift vector based wavelength extraction for chromatic confocal microscopy

2019 ◽  
Vol 30 (11) ◽  
pp. 115104
Author(s):  
Wenlong Lu ◽  
Cheng Chen ◽  
Hong Zhu ◽  
Jian Wang ◽  
Richard Leach ◽  
...  
Keyword(s):  
Confocal Microscopy ◽  
Mean Shift ◽  
Shift Vector

scholarly journals Feature-vector for the MeanShift

2021 ◽  
Vol 24 (2) ◽  
pp. 34-38
Author(s):  
A. I. Shaposhnikov ◽  
Keyword(s):  
Object Tracking ◽  
Feature Vector ◽  
Displacement Vector ◽  
Dimensional Space ◽  
Mean Shift ◽  
Similarity Function ◽  
Color Information ◽  
Shift Vector ◽  
The Mean ◽  
Elliptical Area

The article gives the description of the feature vector, which is suitable for the MeanShift procedure, uses all the color information of the RGB24 format and has a dimension exceeding only 1.5 times the dimension of the smallest 512-dimensional vector used for the Kernel Based Object Tracking procedure. For the described feature vector, a function of similarity of two elliptical areas of the frame is built. For the similarity function, formulas are found for the gradient vector - the mean shift vector, which indicates the direction of the growth of similarity in four-dimensional space of all elliptical regions covering the object in the frame. Knowing the greatest value of the similarity function of two elliptical regions, the length of the displacement vector in the four dimensional space of all elliptical regions was found. To this vector the previous point in space must be moved at the current moment, i.e. the values of the coordinates of the center and the dimensions of the ellipse, in order to obtain the best similarity of the current elliptical area from the previous one. Finally, so as to implement Kernel Based Object Tracking, an algorithm of successive iterations (Newton's method) has been developed, which allows finding the parameters of the ellipse that really has the best similarity. The experiments were carried out and their results were presented and discussed

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