scholarly journals A Method of Meteorological Observation Field Color Image Segmentation Using Mean Shift Combined with Gabor Texture Feature

2016 ◽  
Vol 06 (04) ◽  
pp. 216-222
Author(s):  
瑾 王
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Mean Shift ◽  
Texture Feature ◽  
Color Image Segmentation ◽  
Meteorological Observation

Color image segmentation based on mean shift and multi-feature fusion

2009 ◽  
Vol 29 (8) ◽  
pp. 2074-2076
Author(s):  
Hua LI ◽  
Ming-xin ZHANG ◽  
Jing-long ZHENG
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Color Image ◽  
Mean Shift ◽  
Color Image Segmentation

FAST INTERACTIVE REGIONAL PATTERN MERGING FOR GENERIC TISSUE SEGMENTATION IN HISTOPATHOLOGY IMAGES

2021 ◽  
pp. 2150012
Author(s):  
Kuo-Lung Lor ◽  
Chung-Ming Chen
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Linear Time ◽  
Mean Shift ◽  
Texture Features ◽  
Color Image Segmentation ◽  
Image Size ◽  
Region Merging ◽  
Tissue Segmentation ◽  
Regional Pattern

The image segmentation of histopathological tissue images has always been a challenge due to the overlapping of tissue color distributions, the complexity of extracellular texture and the large image size. In this paper, we introduce a new region-merging algorithm, namely, the Regional Pattern Merging (RPM) for interactive color image segmentation and annotation, by efficiently retrieving and applying the user’s prior knowledge of stroke-based interaction. Low-level color/texture features of each region are used to compose a regional pattern adapted to differentiating a foreground object from the background scene. This iterative region-merging is based on a modified Region Adjacency Graph (RAG) model built from initial segmented results of the mean shift to speed up the merging process. The foreground region of interest (ROI) is segmented by the reduction of the background region and discrimination of uncertain regions. We then compare our method against state-of-the-art interactive image segmentation algorithms in both natural images and histological images. Taking into account the homogeneity of both color and texture, the resulting semi-supervised classification and interactive segmentation capture histological structures more completely than other intensity or color-based methods. Experimental results show that the merging of the RAG model runs in a linear time according to the number of graph edges, which is essentially faster than both traditional graph-based and region-based methods.

A BRANCH AND BOUND ALGORITHM FOR FINDING THE MODES IN KERNEL DENSITY ESTIMATES

2009 ◽  
Vol 08 (01) ◽  
pp. 17-35
Author(s):  
OLIVER WIRJADI ◽  
THOMAS BREUEL
Keyword(s):  
Image Segmentation ◽  
Branch And Bound ◽  
Color Image ◽  
Mean Shift ◽  
Simulated Data ◽  
Kernel Density ◽  
Ease Of Use ◽  
Branch And Bound Algorithm ◽  
Color Image Segmentation ◽  
The Mean

Kernel density estimators are established tools in nonparametric statistics. Due to their flexibility and ease of use, these methods are popular in computer vision and pattern recognition for tasks such as object tracking in video or image segmentation. The most frequently used algorithm for finding the modes in such densities (the mean shift) is a gradient ascent rule, which converges to local maxima. We propose a novel, globally optimal branch and bound algorithm for finding the modes in kernel densities. We show in experiments on datasets up to dimension five that the branch and bound method is faster than local optimization and observe linear runtime scaling of our method with sample size. Quantitative experiments on simulated data show that our method gives statistically significantly more accurate solutions than the mean shift. The mode localization accuracy is about five times more precise than that of the mean shift for all tested parameters. Applications to color image segmentation on an established benchmark test set also show measurably improved results when using global optimization.

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