Color Image Segmentation of Plume for Monitoring Disk Laser Welding Process

2012 ◽  
Vol 201-202 ◽  
pp. 166-169
Author(s):  
Teng Wang ◽  
Xiang Dong Gao
Keyword(s):  
Image Segmentation ◽  
Laser Welding ◽  
High Speed ◽  
Clustering Algorithm ◽  
Color Image ◽  
Color Space ◽  
Welding Process ◽  
Metal Vapor ◽  
Color Image Segmentation ◽  
Deep Penetration

During deep penetration laser welding, changes in the metal vapor plume contain information about the stability of welding process. A high-speed camera was used to online monitor the welding process in order to detect the laser welding defects. A color segmentation clustering algorithm based on HSI color space was proposed for processing the recorded welding sequences. The effectiveness of algorithms based on different model is discussed, welding experimental results showed that the proposed algorithm could achieve better image segmentation, and it highlighted the edge of the metal vapor details in the image.

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.

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.

scholarly journals Human Perception Based Color Image Segmentation

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Neeta Pradeep Gargote ◽  
Savitha Devaraj ◽  
Shravani Shahapure
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Color Image ◽  
Color Space ◽  
Human Perception ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Neural Network Architecture ◽  
Hsi Color Space

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.

Sign in / Sign up

Export Citation Format

Share Document