scholarly journals A smoke image segmentation algorithm based on rough set and region growing

2019 ◽  
Vol 65 (No. 8) ◽  
pp. 321-329
Author(s):  
Haitao Wang ◽  
Yanli Chen
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Region Growing ◽  
Recall Rate ◽  
Segmentation Algorithm ◽  
Colour Space ◽  
Seed Point ◽  
Black Smoke ◽  
Growth Method ◽  
Image Segmentation Algorithm

Because the image fire smoke segmentation algorithm can not extract white, gray and black smoke at the same time, a smoke image segmentation algorithm is proposed by combining rough set and region growth method. The R component of the image is extracted in the RGB colour space, the roughness histogram is constructed according to the statistical histogram of the R component, and the appropriate valley value in the roughness histogram is selected as the segmentation threshold, the image is roughly segmented. Relative to the background image, the smoke belongs to the motion information, and the motion region is extracted by the interframe difference method to eliminate static interference. Smoke has a unique colour feature, a smoke colour model is created in the RGB colour space, the motion disturbances of similar colour are removed and the suspected smoke areas are obtained. The seed point is selected in the region, and the region is grown on the result of rough segmentation, the smoke region is extracted. The experimental results show that the algorithm can segment white, gray and black smoke at the same time, and the irregular information of smoke edges is relatively complete. Compared with the existing algorithms, the average segmentation accuracy, recall rate and F-value are increased by 19%, 21.5% and 20%, respectively.<br /><br />

Image Segmentation Based on Facet Model Fitting

2012 ◽  
Vol 459 ◽  
pp. 35-39
Author(s):  
Wen Guo Li ◽  
Shao Jun Duan ◽  
Zhi Hong Yin
Keyword(s):  
Image Segmentation ◽  
Model Fitting ◽  
Region Growing ◽  
Error Distribution ◽  
Segmentation Algorithm ◽  
Seed Region ◽  
Region Merging ◽  
Fitting Error ◽  
Image Segmentation Algorithm ◽  
Facet Model

The image segmentation algorithm based on facet model fitting is proposed, we firstly employ the facet model to fit the image intensity, and then calculate the fitting error. After acquiring seed segmentation region from the fitting error distribution, the region growing algorithm is implemented to enlarge the seed region to some region boundary. Finally, a new region merging algorithm is implemented to merge adjacent regipons into some large regions. Experiment results intestify the correctness of our proposed segmentation algorithm

scholarly journals Selective Feature Fusion Based Adaptive Image Segmentation Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Qianwen Li ◽  
Zhihua Wei ◽  
Wen Shen
Keyword(s):  
Image Segmentation ◽  
Feature Fusion ◽  
Region Growing ◽  
Adaptive Threshold ◽  
Segmentation Algorithm ◽  
Image Feature ◽  
Fusion Algorithm ◽  
Region Growing Segmentation ◽  
Image Segmentation Algorithm ◽  
Change Threshold

Image segmentation is an essential task in computer vision and pattern recognition. There are two key challenges for image segmentation. One is to find the most discriminative image feature set to get high-quality segments. The other is to achieve good performance among various images. In this paper, we firstly propose a selective feature fusion algorithm to choose the best feature set by evaluating the results of presegmentation. Specifically, the proposed method fuses selected features and applies the fused features to region growing segmentation algorithm. To get better segments on different images, we further develop an algorithm to change threshold adaptively for each image by measuring the size of the region. The adaptive threshold can achieve better performance on each image than fixed threshold. Experimental results demonstrate that our method improves the performance of traditional region growing by selective feature fusion and adaptive threshold. Moreover, our proposed algorithm obtains promising results and outperforms some popular approaches.

Dynamic Image Segmentation for Sport Graphics Based on Wavelet Transform

2014 ◽  
Vol 513-517 ◽  
pp. 3715-3718
Author(s):  
Wen Ge Zhao ◽  
Li Nan Wang
Keyword(s):  
Image Segmentation ◽  
Wavelet Transform ◽  
Experimental Evaluation ◽  
Real Life ◽  
Feature Space ◽  
Segmentation Algorithm ◽  
Dynamic Image ◽  
Segmentation Quality ◽  
Speed Up ◽  
Image Segmentation Algorithm

In this paper, a image segmentation algorithm based on wavelet transform are presented. The proposed image segmentation algorithm performs the segmentation in the combined intensity-texture-position feature space in order to produce connected regions that correspond to the real-life objects shown in the image. This segmentation algorithm is applied to reduced versions of the original images in order to speed-up the completion of the segmentation. As shown by experimental evaluation, this novel scheme provides fast segmentation with high perceptual segmentation quality.

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