Single sperm tracking using Intersect Cortical Model-Mean Shift Method

2017 ◽  
Author(s):  
Weng Chun Tan ◽  
Nor Ashidi Mat Isa
Keyword(s):  
Mean Shift ◽  
Cortical Model ◽  
Shift Method

The mean shift method of chaotic sequences in the study of compressive sensing

2016 ◽  
Vol 8 (5) ◽  
pp. 1643-1654 ◽  
Author(s):  
Guoming Chen ◽  
Qiang Chen ◽  
Shun Long ◽  
Weiheng Zhu
Keyword(s):  
Compressive Sensing ◽  
Mean Shift ◽  
Shift Method ◽  
Chaotic Sequences ◽  
The Mean

Research of Remote Sensing Image Segmentation Based on Mean Shift and Region Merging

2011 ◽  
Vol 90-93 ◽  
pp. 2836-2839 ◽  
Author(s):  
Jian Cui ◽  
Dong Ling Ma ◽  
Ming Yang Yu ◽  
Ying Zhou
Keyword(s):  
Remote Sensing ◽  
Image Segmentation ◽  
Large Scale ◽  
Mean Shift ◽  
Image Features ◽  
Small Scale ◽  
Region Merging ◽  
Segmentation Method ◽  
Shift Method ◽  
Scale Parameters

In order to extract ground information more accurately, it is important to find an image segmentation method to make the segmented features match the ground objects. We proposed an image segmentation method based on mean shift and region merging. With this method, we first segmented the image by using mean shift method and small-scale parameters. According to the region merging homogeneity rule, image features were merged and large-scale image layers were generated. What’s more, Multi-level image object layers were created through scaling method. The test of segmenting remote sensing images showed that the method was effective and feasible, which laid a foundation for object-oriented information extraction.

scholarly journals Spatial Object Tracking Using an Enhanced Mean Shift Method Based on Perceptual Spatial-Space Generation Model

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Pengcheng Han ◽  
Junping Du ◽  
Ming Fang
Keyword(s):  
Object Tracking ◽  
Mean Shift ◽  
Contour Detection ◽  
Generation Model ◽  
Shift Method ◽  
Spatial Object ◽  
Intelligent Modeling ◽  
Spatial Shape ◽  
Local Filters ◽  
Spatial Video

Object tracking is one of the fundamental problems in computer vision, but existing efficient methods may not be suitable for spatial object tracking. Therefore, it is necessary to propose a more intelligent mathematical model. In this paper, we present an intelligent modeling method using an enhanced mean shift method based on a perceptual spatial-space generation model. We use a series of basic and composite graphic operators to complete signal perceptual transformation. The Monte Carlo contour detection method could overcome the dimensions problem of existing local filters. We also propose the enhanced mean shift method with estimation of spatial shape parameters. This method could adaptively adjust tracking areas and eliminate spatial background interference. Extensive experiments on a variety of spatial video sequences with comparison to several state-of-the-art methods demonstrate that our method could achieve reliable and accurate spatial object tracking.

DIFFERENTIAL SHAPE STATISTICAL ANALYSIS

2007 ◽  
Vol 05 (01) ◽  
pp. 145-157 ◽  
Author(s):  
YUFENG CHEN ◽  
MANDUN ZHANG ◽  
PENG LU ◽  
YANGSHENG WANG
Keyword(s):  
Statistical Analysis ◽  
Real Time ◽  
Structural Properties ◽  
Statistical Approach ◽  
Mean Shift ◽  
Scale Space ◽  
Contour Analysis ◽  
Shift Method ◽  
Contour Segment ◽  
Curvature Scale Space

A novel statistical approach that involves differential shape is proposed to analyze contour segments. First, a moment-based algorithm to represent the differential contour segment in an efficient way is introduced. Then, a curvature mean-shift method is adopted to search for the salient features. An optimized function is also developed to segment a contour into parts based on its structural properties. Compared with some other methods used in CSS (Curvature Scale Space) and shock graphs, our method is more powerful for shape contour analysis, especially for the incomplete or occluded contours. Experiments show that our method can track salient parts in real-time and give a judgment of the basic shape properties such as symmetry.

scholarly journals A Denoising Method for LiDAR Full-Waveform Data

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xudong Lai ◽  
Min Zheng
Keyword(s):  
Pulse Width ◽  
Point Cloud ◽  
Mean Shift ◽  
Peak Position ◽  
Denoising Method ◽  
Positioning Accuracy ◽  
Waveform Data ◽  
Shift Method ◽  
Filtering Algorithm ◽  
Full Waveform

Decomposition of LiDAR full-waveform data can not only enhance the density and positioning accuracy of a point cloud, but also provide other useful parameters, such as pulse width, peak amplitude, and peak position which are important information for subsequent processing. Full-waveform data usually contain some random noises. Traditional filtering algorithms always cause distortion in the waveform.λ/μfiltering algorithm is based on Mean Shift method. It can smooth the signal iteratively and will not cause any distortion in the waveform. In this paper, an improvedλ/μfiltering algorithm is proposed, and several experiments on both simulated waveform data and real waveform data are implemented to prove the effectiveness of the proposed algorithm.

A Multi-Feature Fusion Tracking Method Based on Mean Shift and Particle Filter

2014 ◽  
Vol 701-702 ◽  
pp. 257-260
Author(s):  
Ming Jie Zhang ◽  
Bao Sheng Kang
Keyword(s):  
Particle Filter ◽  
Feature Fusion ◽  
Mean Shift ◽  
Filter Method ◽  
Complex Environments ◽  
Shape Information ◽  
Shift Method ◽  
Tracking Method ◽  
Fusion Tracking ◽  
Stability And Accuracy

In order to improve the robustness of visual tracking in complex environments, a novel multi-feature fusion tracking method based on mean shift and particle filter is proposed. In the proposed method, the color and shape information are adaptively fused to represent the target observation, and incorporating mean shift method into particle filter method. The method can overcome the degeneracy problem of particle. Experimental results demonstrate that this method can improve stability and accuracy of tracking.

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