scholarly journals Enhancement Image Intensity of HSV Color Space

2019 ◽  
Vol 8 (2S11) ◽  
pp. 2583-2585
Keyword(s):  
Light Intensity ◽  
Face Detection ◽  
Color Image ◽  
Color Space ◽  
Detection Performance ◽  
Lighting Condition ◽  
Hsv Color Space ◽  
Number Of Faces ◽  
Intensity Normalization ◽  
Poor Lighting

One of factor that affects technology in face detecting or recognizing is illumination. Poor lighting can cause difficulty to the system to recognize face. Although it is over exposure or under exposure. By doing color image processing, it supports the system to detect face in a poor lighting condition. This research used lighting intensity normalization method to increase face detection performance. This method can normalize the light intensity especially on the face lighting. We normalize the light intensity through HSV color space. HSV color space has saturation which is amount of light in the image. The method proceed saturation in image to increase face detection performance. Total number of faces we had tested is 286 faces, the system detect 243 faces before intensity normalization proceed. Whereas, after normalization process, it detects more faces which is 279 faces. As we can see, the percentage improvement before to after intensity normalization is 84.97% to 97.55%. This is 12.58% improvement. We can say this method helps face detection to increase it performance.

scholarly journals The Impact of Color Space and Intensity Normalization to Face Detection Performance

2017 ◽  
Vol 15 (4) ◽  
pp. 1895
Author(s):  
I Nyoman Gede Arya Astawa ◽  
I Ketut Gede Darma Putra ◽  
I Made Sudarma ◽  
Rukmi Sari Hartati
Keyword(s):  
Face Detection ◽  
Color Space ◽  
Detection Performance ◽  
The Impact ◽  
Intensity Normalization

scholarly journals An Innovative Face Detection Based on YCgCr Color Space

2018 ◽  
Author(s):  
Solly Aryza
Keyword(s):  
Face Detection ◽  
Skin Color ◽  
Color Image ◽  
Color Space ◽  
Gaussian Model ◽  
Detection Rates ◽  
Detection Algorithms ◽  
Lighting Conditions ◽  
Wide Range ◽  
Human Faces

It is very challenging to recognize a face from an image due to the wide variety of face and the uncertain of face position. The research on detecting human faces in color image and in video sequence has been attracted with more and more people. In this paper, we propose a novel face detection method that achieves better detection rates. The new face detection algorithms based on skin color model in YCgCr chrominance space. Firstly, we build a skin Gaussian model in Cg-Cr color space. Secondly, a calculation of correlation coefficient is performed between the given template and the candidates. Experimental results demonstrate that our system has achieved high detection rates and low false positives over a wide range of facial variations in color, position and varying lighting conditions.

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 Multifocus Color Image Fusion Based on NSST and PCNN

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Xin Jin ◽  
Rencan Nie ◽  
Dongming Zhou ◽  
Quan Wang ◽  
Kangjian He
Keyword(s):  
Neural Networks ◽  
Image Fusion ◽  
Color Image ◽  
Color Space ◽  
Color Spaces ◽  
Fusion Algorithm ◽  
Hsv Color Space ◽  
Coupled Neural Networks ◽  
Different Color ◽  
Pulse Coupled Neural Networks

This paper proposed an effective multifocus color image fusion algorithm based on nonsubsampled shearlet transform (NSST) and pulse coupled neural networks (PCNN); the algorithm can be used in different color spaces. In this paper, we take HSV color space as an example, H component is clustered by adaptive simplified PCNN (S-PCNN), and then the H component is fused according to oscillation frequency graph (OFG) of S-PCNN; at the same time, S and V components are decomposed by NSST, and different fusion rules are utilized to fuse the obtained results. Finally, inverse HSV transform is performed to get the RGB color image. The experimental results indicate that the proposed color image fusion algorithm is more efficient than other common color image fusion algorithms.

View-Invariant Face Detection for Colorful Image

2014 ◽  
Vol 945-949 ◽  
pp. 1880-1884
Author(s):  
Hua Zhang ◽  
Li Jia Wang ◽  
Zhen Jie Wang ◽  
Wei Yi Yuan
Keyword(s):  
Face Detection ◽  
Detection Method ◽  
Color Space ◽  
Well Performance ◽  
Illumination Compensation ◽  
Face Model ◽  
Hsv Color Space ◽  
Ycbcr Color Space ◽  
Face Images ◽  
Illumination Changes

To overcome illumination changes and pose variations, a pose-invariant face detection method is presented. First, an illumination compensation method based on reference white is presented to overcome the lighting variations. The reference white is obtained according to the component Y from YCbCr color space. Then, a mixture face model is constructed by the Cb and Cr from YCbCr color space and H from the HSV color space to extract faces from colorful image. At last, an eyes model is designed to locate eyes in the obtained face images, which can distinguish face from neck and arms ultimately. The presented method is conducted on the CASIA face database. The experimental results have shown that our method is robust to pose changes and illumination variations, and it can achieve well performance.

Color image fusion based on simplified pulse coupled neural network and HSV color space

2016 ◽  
Author(s):  
Jin Xin ◽  
Dongming Zhou ◽  
Shaowen Yao ◽  
Rencan Nie ◽  
Chuanbo Yu ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Fusion ◽  
Color Image ◽  
Color Space ◽  
Hsv Color Space ◽  
Pulse Coupled Neural Network
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