scholarly journals Comparative Study of HSV Color Model and Ycbcr Color Model to Detect Nucleus of White Cells

2016 ◽  
Vol 150 (8) ◽  
pp. 38-42
Author(s):  
Himali Vaghela ◽  
Hardik Modi ◽  
Manoj Pandya ◽  
M. B.
Keyword(s):  
Comparative Study ◽  
Color Model ◽  
Hsv Color Model

Shadow Removal Using Inpainting and Interpolation Technique

2014 ◽  
Vol 626 ◽  
pp. 32-37 ◽  
Author(s):  
Ajayan Lekshmi ◽  
C. Christopher Seldev
Keyword(s):  
Image Inpainting ◽  
Smooth Transition ◽  
Color Model ◽  
Shadow Removal ◽  
Bilinear Interpolation ◽  
False Color ◽  
Novel Approach ◽  
Reconstruction Quality ◽  
Hsv Color Model ◽  
Interpolation Technique

Shadows are viewed as undesired information that strongly affects images. Shadows may cause a high risk to present false color tones, to distort the shape of objects, to merge, or to lose objects. This paper proposes a novel approach for the detection and removal of shadows in an image. Firstly the shadow and non shadow region of the original image is identified by HSV color model. The shadow removal is based on exemplar based image inpainting. Finally, the border between the reconstructed shadow and the non shadow areas undergoes bilinear interpolation to yield a smooth transition between them. They would lead to a better fitting of the shadow and non shadow classes, thus resulting in a potentially better reconstruction quality.

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.  

COLOR FACE SEGMENTATION USING A FUZZY MIN-MAX NEURAL NETWORK

2002 ◽  
Vol 02 (04) ◽  
pp. 587-601
Author(s):  
JUAN WACHS ◽  
HELMAN STERN ◽  
MARK LAST
Keyword(s):  
Neural Network ◽  
Color Model ◽  
Test Cases ◽  
Difficult Case ◽  
Automated Method ◽  
Hsv Color Model ◽  
Face Segmentation ◽  
The Face ◽  
Feature Information ◽  
Large Background

This work presents an automated method of segmentation of faces in color images with complex backgrounds. Segmentation of the face from the background in an image is performed by using face color feature information. Skin regions are determined by sampling the skin colors of the face in a Hue Saturation Value (HSV) color model, and then training a fuzzy min-max neural network (FMMNN) to automatically segment these skin colors. This work appears to be the first application of Simpson's FMMNN algorithm to the problem of face segmentation. Results on several test cases showed recognition rates of both face and background pixels to be above 93%, except for the case of a small face embedded in a large background. Suggestions for dealing with this difficult case are proffered. The image pixel classifier is linear of order O(Nh) where N is the number of pixels in the image and h is the number of fuzzy hyperbox sets determined by training the FMMNN.

scholarly journals Speed-limit Signs Detection and Recognition Based on HSV Color Model

2016 ◽  
Vol 9 (4) ◽  
pp. 219-222
Author(s):  
L. Almazaydeh ◽  
M. Salah ◽  
M. Misto ◽  
E. Nairok ◽  
S. Alsayed ◽  
...  
Keyword(s):  
Color Model ◽  
Speed Limit ◽  
Hsv Color Model ◽  
Detection And Recognition

scholarly journals Improved Unsupervised Color Segmentation Using a Modified HSV Color Model and a Bagging Procedure in K-Means++ Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-23 ◽  
Author(s):  
Edgar Chavolla ◽  
Arturo Valdivia ◽  
Primitivo Diaz ◽  
Daniel Zaldivar ◽  
Erik Cuevas ◽  
...  
Keyword(s):  
Machine Learning ◽  
Scientific Community ◽  
Color Image ◽  
The Other ◽  
Color Image Segmentation ◽  
Color Segmentation ◽  
Color Model ◽  
Wide Range ◽  
Hsv Color Model ◽  
Relevant Topic

Accurate color image segmentation has stayed as a relevant topic between the researches/scientific community due to the wide range of application areas such as medicine and agriculture. A major issue is the presence of illumination variations that obstruct precise segmentation. On the other hand, the machine learning unsupervised techniques have become attractive principally for the easy implementations. However, there is not an easy way to verify or ensure the accuracy of the unsupervised techniques; so these techniques could lead to an unknown result. This paper proposes an algorithm and a modification to the HSV color model in order to improve the accuracy of the results obtained from the color segmentation using the K-means++ algorithm. The proposal gives better segmentation and less erroneous color detections due to illumination conditions. This is achieved shifting the hue and rearranging the H equation in order to avoid undefined conditions and increase robustness in the color model.

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