Segmentation of Arecanut Bunches: a Comparative Study of Different Color Models

2021 ◽  
Author(s):  
R. Dhanesha ◽  
D. K. Umesha ◽  
C. L. Shrinivasa Naika ◽  
G. N. Girish
Keyword(s):  
Comparative Study ◽  
Color Models ◽  
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.

scholarly journals Silhouette index for determining optimal k-means clustering on images in different color models

2018 ◽  
Vol 7 (2.14) ◽  
pp. 105 ◽  
Author(s):  
Abd Rasid Mamat ◽  
Fatma Susilawati Mohamed ◽  
Mohamad Afendee Mohamed ◽  
Norkhairani Mohd Rawi ◽  
Mohd Isa Awang
Keyword(s):  
Image Processing ◽  
Color Model ◽  
Cluster Validation ◽  
Validation Process ◽  
Color Models ◽  
Silhouette Index ◽  
Hsv Color Model ◽  
Different Color ◽  
Lab Color Model ◽  
Cie Lab

Clustering process is an essential part of the image processing. Its aim to group the data according to having the same attributes or similarities of the images. Consequently, determining the number of the optimum clusters or the best (well-clustered) for the image in different color models is very crucial. This is because the cluster validation is fundamental in the process of clustering and it reflects the split between clusters. In this study, the k-means algorithm was used on three colors model: CIE Lab, RGB and HSV and the clustering process made up to k clusters. Next, the Silhouette Index (SI) is used to the cluster validation process, and this value is range between 0 to 1 and the greater value of SI illustrates the best of cluster separation. The results from several experiments show that the best cluster separation occurs when k=2 and the value of average SI is inversely proportional to the number of k cluster for all color model. The result shows in HSV color model the average SI decreased 14.11% from k = 2 to k = 8, 11.1% in HSV color model and 16.7% in CIE Lab color model. Comparisons are also made for the three color models and generally the best cluster separation is found within HSV, followed by the RGB and CIE Lab color models.  

scholarly journals Biomarker Detection in Whole Slide Imaging based on Statistical Color Models

2010 ◽  
Author(s):  
Jie Shu ◽  
Guoping Qiu ◽  
Mohammad Ilyas ◽  
Philip Kaye
Keyword(s):  
Statistical Models ◽  
Color Image ◽  
Biomarker Detection ◽  
Color Spaces ◽  
Whole Slide Imaging ◽  
Analysis Problem ◽  
Color Image Analysis ◽  
Color Models ◽  
Colorectal Biopsies ◽  
Different Color

This paper presents a technique for immunostaining biomarker detection in digital slides. We treat immunostaining detection as a color image analysis problem and build statistical color models using a large number of labeled positive and negative immunostaining pixels. We have implemented the statistical models in different color spaces and show that the opponent chromaticity signals effectively characterize the color distributions of the immunostaining biomarkers and that the luminance is an unreliable and distractive signal. We have applied the technique to the detection of positive P53 immunostaining in digital slides of oesophagitis and colorectal biopsies. We present experimental results and show that the technique can achieve a biomarker detection rate of over 98% with 5% false positives.

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