A comparative study of different color spaces in computer-vision-based flame detection

2015 ◽  
Vol 75 (17) ◽  
pp. 10291-10310 ◽  
Author(s):  
Sheng-Yong Du ◽  
Zhao-Guang Liu
Keyword(s):  
Computer Vision ◽  
Comparative Study ◽  
Color Spaces ◽  
Flame Detection ◽  
Different Color

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.

Anisotropic Diffusion-Based Color Texture Analysis for Industrial Application

2020 ◽  
pp. 30-64
Author(s):  
Rohini A. Bhusnurmath ◽  
Prakash S. Hiremath
Keyword(s):  
Computer Vision ◽  
Texture Component ◽  
Industrial Application ◽  
Anisotropic Diffusion ◽  
Texture Classification ◽  
Industrial Applications ◽  
Color Spaces ◽  
Linear Discriminant ◽  
Benchmark Datasets ◽  
Different Color

This chapter proposes the framework for computer vision algorithm for industrial application. The proposed framework uses wavelet transform to obtain the multiresolution images. Anisotropic diffusion is employed to obtain the texture component. Various feature sets and their combinations are considered obtained from texture component. Linear discriminant analysis is employed to get the distinguished features. The k-NN classifier is used for classification. The proposed method is experimented on benchmark datasets for texture classification. Further, the method is extended to exploration of different color spaces for finding reference standard. The thrust area of industrial applications for machine intelligence in computer vision is considered. The industrial datasets, namely, MondialMarmi dataset for granite tiles and Parquet dataset for wood textures are experimented. It was observed that the combination of features performs better in YCbCr and HSV color spaces for MondialMarmi and Parquet datasets as compared to the other methods in literature.

scholarly journals Comparative study of vectorial morphological operations in different color spaces

2001 ◽  
Author(s):  
Francisco G. Ortiz Zamora ◽  
Fernando Torres-Medina ◽  
Jesus Lopez-Angulo ◽  
Santiago Puente Mendez
Keyword(s):  
Comparative Study ◽  
Morphological Operations ◽  
Color Spaces ◽  
Different Color

Fast color correction using principal regions mapping in different color spaces

2004 ◽  
Vol 10 (1) ◽  
pp. 23-30 ◽  
Author(s):  
Maojun Zhang ◽  
Nicolas D. Georganas
Keyword(s):  
Color Correction ◽  
Color Spaces ◽  
Different Color

Behavior of skin color under varying illumination seen by different cameras at different color spaces

2001 ◽  
Author(s):  
J. Birgitta Martinkauppi ◽  
Maricor N. Soriano ◽  
Mika V. Laaksonen
Keyword(s):  
Skin Color ◽  
Color Spaces ◽  
Different Color
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