Flood fill mean shift: A robust segmentation algorithm

2010 ◽  
Vol 8 (6) ◽  
pp. 1313-1319 ◽  
Author(s):  
Jayong Lee ◽  
Hoon Kang
Keyword(s):  
Mean Shift ◽  
Segmentation Algorithm ◽  
Robust Segmentation ◽  
Flood Fill

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

A robust segmentation algorithm for branch structure and its implementation

2011 ◽  
Author(s):  
Yanan Wang ◽  
Wanggen Wan ◽  
Zhi Wang ◽  
Shuiling Mao ◽  
Rui Wang ◽  
...  
Keyword(s):  
Segmentation Algorithm ◽  
Robust Segmentation

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

Multiscale image segmentation algorithm based on mean shift

2002 ◽  
Author(s):  
Fei Liu ◽  
Xiaodan Song ◽  
Yupin Luo ◽  
Dongcheng Hu
Keyword(s):  
Image Segmentation ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Multiscale Image Segmentation ◽  
Image Segmentation Algorithm

scholarly journals Superpixel Segmentation of Polarimetric Synthetic Aperture Radar (SAR) Images Based on Generalized Mean Shift

2018 ◽  
Vol 10 (10) ◽  
pp. 1592 ◽  
Author(s):  
Fengkai Lang ◽  
Jie Yang ◽  
Shiyong Yan ◽  
Fachao Qin
Keyword(s):  
Image Segmentation ◽  
Synthetic Aperture Radar ◽  
Mean Shift ◽  
Segmentation Algorithm ◽  
Synthetic Aperture ◽  
Sar Images ◽  
Mean Shift Algorithm ◽  
Superpixel Segmentation ◽  
Generalized Mean ◽  
Aperture Radar

The mean shift algorithm has been shown to perform well in optical image segmentation. However, the conventional mean shift algorithm performs poorly if it is directly used with Synthetic Aperture Radar (SAR) images due to the large dynamic range and strong speckle noise. Recently, the Generalized Mean Shift (GMS) algorithm with an adaptive variable asymmetric bandwidth has been proposed for Polarimetric SAR (PolSAR) image filtering. In this paper, the GMS algorithm is further developed for PolSAR image segmentation. A new merging predicate that is defined in the joint spatial-range domain is derived based on the GMS algorithm. A pre-sorting strategy and a post-processing step are also introduced into the GMS segmentation algorithm. The proposed algorithm can be directly used for PolSAR image superpixel segmentation without any pre-processing steps. Experiments using Airborne SAR (AirSAR) and Experimental SAR (ESAR) L-band PolSAR data demonstrate the effectiveness of the proposed superpixel segmentation algorithm. The parameter settings, stability, quality, and efficiency of the GMS algorithm are also discussed at the end of this paper.

scholarly journals Multimodal MR Brain Segmentation Using Bayesian-based Adaptive Mean-Shift (BAMS)

2013 ◽  
Author(s):  
Qaiser Mahmood ◽  
Mohammad Alipoor ◽  
Artur Chodorowski ◽  
Andrew Mehnert1 ◽  
Mikael Persson
Keyword(s):  
Mean Shift ◽  
Ground Truth ◽  
Segmentation Algorithm ◽  
Brain Segmentation ◽  
Mr Images ◽  
Unsupervised Segmentation ◽  
Fully Automatic ◽  
The Brain ◽  
Mr Brain ◽  
Dice Index

In this paper, we validate our proposed segmentation algorithm called Bayesian-based adaptive mean-shift (BAMS) on real mul-timodal MR images provided by the MRBrainS challenge. BAMS is a fully automatic unsupervised segmentation algorithm. It is based on the adaptive mean shift wherein the adaptive bandwidth of the kernel for each feature point is estimated using our proposed Bayesian approach [1]. BAMS is designed to segment the brain into three tissues; white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF). The performance of the algorithm is evaluated relative to the manual segmentation (ground truth). The results of our proposed algorithm show the average Dice index 0.8377±0.036 for the WM, 0.7637±0.038 for the GM and 0.6835 ±0.023 for the CSF.

Remotely Sensed Image Based on Robust Segmentation and GIS System

2021 ◽  
Vol 34 (1) ◽  
pp. 64-68
Author(s):  
Swati Jain ◽  
Somesh Kumar Dewangan
Keyword(s):  
Mean Shift ◽  
Police Work ◽  
Geographical Information ◽  
Data Framework ◽  
Robust Segmentation ◽  
Remotely Sensed Image ◽  
Gis System ◽  
Salt And Pepper ◽  
Information Framework ◽  
Image Division

The continuous rising abstraction resolution of distant police work sensors sets new interest for applications victimization this information. For mining valuable information from far flung police work data, various classifiers hooked in to the supernatural examination of individual pixels are projected and big advancement has been accomplished. Even so, these methodologies have their restrictions, for the foremost half they manufacture "salt and pepper" boisterous outcomes. to beat such problems, object-arranged image examination strategy hooked in to multi-resolution division methodology was advanced and it's been used for various application functions effectively. During this examination, a productive remotely detected image smart understanding technique hooked in to image division and geographical information framework (GIS) was projected, within the 1st place, division hooked in to mean shift was utilized to amass the underlying parts from distant police work footage. At that time, apply vectorization (Raster to Vector Convertor) strategy to supply polygons from the divided image and highlight attributions, as an example, ghostly, shape, surface then on square measure removed by zonal investigation hooked in to distinctive formation and polygons. At last, creating getting ready take a look at and administered characterization square measure dispensed. just about all means that square measure accomplished in geo-data framework with the exception of image division. supported the investigation, we have a tendency to engineered up a product arrangement of remotely detected image examination. Contrasted and also the understanding methodology of a business programming eCognition, the projected one was gettable and practiced once applied to the Quick bird remotely detected footage.

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