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.

Research on Multi-Threshold Color Image Segmentation Based on Rough Set

2011 ◽  
Vol 474-476 ◽  
pp. 771-776
Author(s):  
Guo Quan Zhang ◽  
Zhan Ming Li
Keyword(s):  
Image Segmentation ◽  
Rough Set ◽  
Color Image ◽  
Experimental Results ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Illumination Variation ◽  
Threshold Segmentation ◽  
Segmentation Accuracy ◽  
Novel Method

Aims at the problem that the threshold number and value are difficulty to determine automatically existing in multi-threshold color image segmentation method, a novel method of multi-threshold segmentation in HSV is proposed. First of all, the image is pre-processed in HSV, component H and V is projected to S and be quantified at the same time. Secondly, histogram and advanced Histon histogram (AHH) are constructed. According to concept of roughness in the theory of Rough Set, the histogram of roughness (RSH) is constructed. Finally, according to requirement of segmentation accuracy, set a threshold Hn on RSH to determine the number and scope of multi-threshold and the image is segmented with above thresholds. The experimental results show that this method can determine the threshold quantity automatically, segment image efficiently and robust against illumination variation.

Clustering-Based Color Image Segmentation Using Local Maxima

2018 ◽  
Vol 14 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Kalaivani Anbarasan ◽  
S. Chitrakala
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Automatic Segmentation ◽  
Input Image ◽  
Color Image Segmentation ◽  
Local Maxima ◽  
User Input ◽  
Texture Property ◽  
Multiple Regions

Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, with pixels spatially clustered into identical regions. A novel Clustering-based Image Segmentation using Local Maxima (CBIS-LM) method is presented. Our proposed approach generates reliable, accurate and non-overlapping multiple regions for the given user input image. The segmented regions can be automatically annotated with distinct labels which, in turn, help retrieve relevant images based on image semantics.

Assessment of automatic segmentation accuracy with various point cloud density

2020 ◽  
Vol 961 (7) ◽  
pp. 47-55
Author(s):  
A.G. Yunusov ◽  
A.J. Jdeed ◽  
N.S. Begliarov ◽  
M.A. Elshewy
Keyword(s):  
Point Cloud ◽  
Laser Scanning ◽  
Reference Data ◽  
Automatic Segmentation ◽  
Geometric Accuracy ◽  
Segmentation Method ◽  
Segmentation Methods ◽  
Segmentation Accuracy ◽  
Number Of Segments ◽  
Cloud Density

Laser scanning is considered as one of the most useful and fast technologies for modelling. On the other hand, the size of scan results can vary from hundreds to several million points. As a result, the large volume of the obtained clouds leads to complication at processing the results and increases the time costs. One way to reduce the volume of a point cloud is segmentation, which reduces the amount of data from several million points to a limited number of segments. In this article, we evaluated effect on the performance, the accuracy of various segmentation methods and the geometric accuracy of the obtained models at density changes taking into account the processing time. The results of our experiment were compared with reference data in a form of comparative analysis. As a conclusion, some recommendations for choosing the best segmentation method were proposed.

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.

scholarly journals Color Image Segmentation Based on Different Color Space Models Using Automatic GrabCut

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Dina Khattab ◽  
Hala Mousher Ebied ◽  
Ashraf Saad Hussein ◽  
Mohamed Fahmy Tolba
Keyword(s):  
Image Segmentation ◽  
Comparative Study ◽  
Color Image ◽  
Color Space ◽  
User Interaction ◽  
Color Image Segmentation ◽  
Space Representation ◽  
Color Spaces ◽  
Different Color ◽  
The Comparative Study

This paper presents a comparative study using different color spaces to evaluate the performance of color image segmentation using the automatic GrabCut technique. GrabCut is considered as one of the semiautomatic image segmentation techniques, since it requires user interaction for the initialization of the segmentation process. The automation of the GrabCut technique is proposed as a modification of the original semiautomatic one in order to eliminate the user interaction. The automatic GrabCut utilizes the unsupervised Orchard and Bouman clustering technique for the initialization phase. Comparisons with the original GrabCut show the efficiency of the proposed automatic technique in terms of segmentation, quality, and accuracy. As no explicit color space is recommended for every segmentation problem, automatic GrabCut is applied withRGB,HSV,CMY,XYZ, andYUVcolor spaces. The comparative study and experimental results using different color images show thatRGBcolor space is the best color space representation for the set of the images used.

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.

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