scholarly journals A Fast Segmentation Method for Fire Forest Images Based on Multiscale Transform and PCA

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6429
Author(s):  
Lotfi Tlig ◽  
Moez Bouchouicha ◽  
Mohamed Tlig ◽  
Mounir Sayadi ◽  
Eric Moreau
Keyword(s):  
Forest Fire ◽  
Color Image ◽  
Gabor Filter ◽  
Human Life ◽  
Principal Component ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Full Account ◽  
Native Flora ◽  
Novel Approach

Forests provide various important things to human life. Fire is one of the main disasters in the world. Nowadays, the forest fire incidences endanger the ecosystem and destroy the native flora and fauna. This affects individual life, community and wildlife. Thus, it is essential to monitor and protect the forests and their assets. Nowadays, image processing outputs a lot of required information and measures for the implementation of advanced forest fire-fighting strategies. This work addresses a new color image segmentation method based on principal component analysis (PCA) and Gabor filter responses. Our method introduces a new superpixels extraction strategy that takes full account of two objectives: regional consistency and robustness to added noises. The novel approach is tested on various color images. Extensive experiments show that our method obviously outperforms existing segmentation variants on real and synthetic images of fire forest scenes, and also achieves outstanding performance on other popular benchmarked images (e.g., BSDS, MRSC). The merits of our proposed approach are that it is not sensitive to added noises and that the segmentation performance is higher with images of nonhomogeneous regions.

Color Image Segmentation Method Based on Parameter-Controlled MSPCNN and SLIC

2021 ◽  
Vol 58 (2) ◽  
pp. 0210023
Author(s):  
李新颖 Li Xinying ◽  
冉思园 Ran Siyuan ◽  
廉敬 Lian Jing
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Segmentation Method

Algorithm for Petro-Graphic Color Image Segmentation Used in Oil Exploration

2014 ◽  
pp. 187-195 ◽  
Keyword(s):  
Image Segmentation ◽  
Thin Section ◽  
Heuristic Algorithm ◽  
Color Image ◽  
Color Image Segmentation ◽  
Oil Exploration ◽  
Segmentation Method ◽  
Graphic Images ◽  
Oil Gas ◽  
Automatic Algorithm

A new heuristic algorithm for porosity segmentation for the colored petro-graphic images is proposed. The proposed algorithm automatically detects the porosities that represent the presence of oil, gas, or even water in the analyzed thin section rock segment based on the colour of the porosity area filled with dies in the analyzed sample. For the purpose of the oil exploration, the thin section fragments are died in order to emphasize the porosities that are analyzed under the microscope. The percentage of the porosity is directly proportional to the probability of the oil, gas, or even water presence in the area where the drilling is performed (i.e. the increased porosity indicates the higher probability of oil existence in the region). The proposed automatic algorithm shows better results than the existing K-means segmentation method.

Color Image Segmentation Based on Secondary Watershed and GrowCut Algorithm

2014 ◽  
Vol 989-994 ◽  
pp. 4032-4037
Author(s):  
Jian Mei Chen ◽  
Hai Ying Lu
Keyword(s):  
Color Image ◽  
Automatic Segmentation ◽  
User Interaction ◽  
Input Image ◽  
Color Image Segmentation ◽  
Superior Performance ◽  
Segmentation Method ◽  
Interactive Algorithm ◽  
Segmentation Accuracy ◽  
Label Algorithm

GrowCut algorithm is not only an interactive algorithm on the basis of cell automata, but also a multi-label algorithm based on seeds point. Aiming at the GrowCut algorithm usually asks users to partition foreground and background manually and mark a lot more initial seeds. This paper presents an automatic object segmentation method which combining secondary watershed and GrowCut algorithm, here in the following paper refers it to as SWGC algorithm. It firstly using the twice used watershed algorithm to partition the input image, the segmented regions are labeled using Mahalanobis distance, and merged according to the image color and space information, thereafter applying the GrowCut algorithm to perform globally optimized segmentation. The main contribution focuses on performing automatic segmentation which consist of obtain the foreground and background region and generate the seed template of GrowCut algorithm automatically. Thus not only leave out the constraints of user interaction operation, but also avoid the subjectivity and uncertainty. The proposed method reduces the runtime significantly as well as improves the segmentation accuracy and robustness of GrowCut algorithm. Experimental results show SWGC algorithm has superior performance compared to the other related methods.

The Study of Forest Fire Color Image Segmentation

2011 ◽  
Vol 474-476 ◽  
pp. 2140-2145
Author(s):  
Si Li ◽  
Hong E Ren
Keyword(s):  
Image Segmentation ◽  
Forest Fire ◽  
Color Image ◽  
Color Space ◽  
Color Images ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Segmentation Methods ◽  
Time Periods ◽  
Night And Day

Combined with the composition characteristics of forest fire image background when the forest fire occurred during different time periods of night and day, different image segmentation methods were applied to the forest fire color images of different time periods respectively, which could improve the efficiency of image processing. Meanwhile, application of H and S components from HSV color space, the strategy on color image segmentation which processed the segmentation processing to forest fire color images with complicated background was proposed combined with Otsu algorithm. The results of simulation experiment showed that the above-mentioned segmentation methods were obtained satisfactory segmentation effects when the segmentation on forest fire color images during different time periods of night and day were processed respectively. And also application of Otsu algorithm based on HSV color model, the forest fire image segmentation occurred in the daytime was processed, which overcame the interference factors of light and smoke, as well as the shortage of noise sensibility due to Otsu algorithm.

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