2012 ◽  
Vol 500 ◽  
pp. 709-715
Author(s):  
Yan Wang ◽  
Yan Ma

This paper presents an improved image segmentation method based on multi-resolution analysis of wavelet transform and watershed transformation. In the marked-controlled watershed segmentation, we not only enhance the contours of low-resolution input image to acquire segmentation function image, but also use minima imposition technology to apply filters to input image to acquire marked function image. In order to improve segmentation accuracy, we use regional fusion and coarse-fine segmentation in wavelet inverse transform. The experimental results show that the proposed image segmentation method can efficiently reduce over-segmentation, as well as improve the effect of image segmentation. In addition, the proposed method is robust.


2006 ◽  
Vol 06 (04) ◽  
pp. 569-582 ◽  
Author(s):  
EMMA REGENTOVA ◽  
DONGSHENG YAO ◽  
SHAHRAM LATIFI ◽  
JUN ZHENG

A new image segmentation method is developed that combines the advantage of the normalized cuts (Ncut) algorithm to solve the perceptual grouping problem by means of graph partitioning, and the ability of wavelet transform to capture image features by decomposing signal both in time and frequency. We derive image features from orientation histograms defined on the detail subbands of the discrete wavelet transform. The segmentation is implemented by partitioning a graph representing an image at the coarsest transform level, while the weights of the graph are calculated from all the scales. Due to the reduced dimensionality of the dataset, the speed of Ncut is increased. Even though segmentation is carried out at a coarsest level of transform, the results are accurate for images of different structural contents, including textures.


2020 ◽  
Vol 40 (21) ◽  
pp. 2110003
Author(s):  
王珏 Wang Jue ◽  
张秀英 Zhang Xiuying ◽  
蔡玉芳 Cai Yufang ◽  
卢艳平 Lu Yanping

Optik ◽  
2020 ◽  
Vol 208 ◽  
pp. 164123 ◽  
Author(s):  
Jianqiang Gao ◽  
Binbin Wang ◽  
Ziyi Wang ◽  
Yufeng Wang ◽  
Fanzhi Kong

2019 ◽  
Vol 8 (4) ◽  
pp. 9548-9551

Fuzzy c-means clustering is a popular image segmentation technique, in which a single pixel belongs to multiple clusters, with varying degree of membership. The main drawback of this method is it sensitive to noise. This method can be improved by incorporating multiresolution stationary wavelet analysis. In this paper we develop a robust image segmentation method using Fuzzy c-means clustering and wavelet transform. The experimental result shows that the proposed method is more accurate than the Fuzzy c-means clustering.


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