Research on Color Image Classification Based on HSV Color Space

2012 ◽  
Author(s):  
Chen Junhua ◽  
Lei Jing
Keyword(s):  
Image Classification ◽  
Color Image ◽  
Color Space ◽  
Hsv Color Space

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.

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

scholarly journals Interactive Color Image Segmentation using HSV Color Space

2019 ◽  
Vol 7 (1) ◽  
pp. 37-41
Author(s):  
D. Hema ◽  
◽  
Dr. S. Kannan ◽  
Keyword(s):  
Image Segmentation ◽  
Object Detection ◽  
Color Image ◽  
Research Work ◽  
Color Space ◽  
Color Image Segmentation ◽  
Hsv Color Space ◽  
Space Model ◽  
Detection Algorithms ◽  
Rgb Image

The primary goal of this research work is to extract only the essential foreground fragments of a color image through segmentation. This technique serves as the foundation for implementing object detection algorithms. The color image can be segmented better in HSV color space model than other color models. An interactive GUI tool is developed in Python and implemented to extract only the foreground from an image by adjusting the values for H (Hue), S (Saturation) and V (Value). The input is an RGB image and the output will be a segmented color image.

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.

ROBUST IMAGE RETRIEVAL APPROACH BASED ON CLUSTERING ANALYSIS OF RE-QUANTIZED COLOR IMAGE

2007 ◽  
Vol 05 (01) ◽  
pp. 213-225 ◽  
Author(s):  
YA-LI JI ◽  
XIAO-PING CHENG ◽  
NAI-QIN FENG
Keyword(s):  
Image Retrieval ◽  
Color Image ◽  
Color Space ◽  
Feature Representation ◽  
Hsv Color Space ◽  
Invariant Moments ◽  
Robust Image ◽  
Color Clustering ◽  
Object Features ◽  
Large Image Databases

In this paper, we propose a robust approach about color image retrieval. It can realize fast matching in CBIR (Content-Based Image Retrieval) when we search in large image databases. Indexes root in object features of Z image which is the result of re-quantization in HSV color space, matching with a non-geometrical distance is based on objects, so time consumption pixel by pixel can be avoided. Because Z image is made up of many color clustering regions and invariant moments are used for feature representation, our approach is robust to translation, scale and rotation.

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