Neural network based SOM for multispectral image segmentation in RGB and HSV color space

2015 ◽  
Author(s):  
Ganesan ◽  
B. S. Sathish ◽  
Khamar Basha Shaik ◽  
V. Kalist
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Space ◽  
Multispectral Image ◽  
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 Human Perception Based Color Image Segmentation

2013 ◽  
Vol 2 (3) ◽  
Author(s):  
Neeta Pradeep Gargote ◽  
Savitha Devaraj ◽  
Shravani Shahapure
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Color Image ◽  
Color Space ◽  
Human Perception ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Neural Network Architecture ◽  
Hsi Color Space

Color image segmentation is probably the most important task in image analysis and understanding. A novel Human Perception Based Color Image Segmentation System is presented in this paper. This system uses a neural network architecture. The neurons here uses a multisigmoid activation function. The multisigmoid activation function is the key for segmentation. The number of steps ie. thresholds in the multisigmoid function are dependent on the number of clusters in the image. The threshold values for detecting the clusters and their labels are found automatically from the first order derivative of histograms of saturation and intensity in the HSI color space. Here the main use of neural network is to detect the number of objects automatically from an image. It labels the objects with their mean colors. The algorithm is found to be reliable and works satisfactorily on different kinds of color images.

scholarly journals Smoke Image Segmentation Based on Color Model

2015 ◽  
Vol 6 (2) ◽  
pp. 130 ◽  
Author(s):  
Deng Xing ◽  
Yu Zhongming ◽  
Wang Lin ◽  
Li Jinlan
Keyword(s):  
Image Segmentation ◽  
False Alarm ◽  
False Alarm Rate ◽  
Color Space ◽  
Significant Feature ◽  
Color Model ◽  
Smoke Detection ◽  
Image Detection ◽  
Hsv Color Space ◽  
Lab Color Space

Smoke is the most significant feature in the process of fire, so it’s possible to rely on smoke detection to detect fire. While the smoke image segmentation is the most difficult and also indispensable step in the analysis of smoke image detection. In order to improve its accuracy and effectively exclude the disturbances of non-smoke image, and lower the false alarm rate, it puts forward a kind of smoke image segmentation based on color model. It uses K-means clustering in Lab color space and threshold segmentation in HSV color space, then merges the two results. Finally, it uses the method of shen filter and regional mark to denoise, Experimental results on segmentation of smoke image show that the proposed method is able to segment smoke from the background.

A Target Recognition Algorithm Based on the Object’s Color and Size Features

2014 ◽  
Vol 1039 ◽  
pp. 286-293
Author(s):  
Shuai Guo ◽  
Hua Wei Li ◽  
Chun Sheng Xie ◽  
Wen Yi Li
Keyword(s):  
Image Segmentation ◽  
Object Recognition ◽  
Target Recognition ◽  
Color Space ◽  
Target Object ◽  
Recognition Algorithm ◽  
Threshold Method ◽  
Hsv Color Space ◽  
Seed Growth ◽  
Growth Method

The problem being faced is that the current target recognition method based on color feature can’t filter objects that have the same color as the target object. In this paper, a new target recognition algorithm based on the object’s color and size is introduced. To achieve the goal of object recognition, the HSV color space conversion, the threshold method and seed growth method are used together to implement image segmentation. The size feature has been used to filter the image regions that have been extracted by image segmentation. The method is proved by experimentation to be effective in regular shape object recognition.

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.

Segmentation-Based Automatic White Balance Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 954-957
Author(s):  
Yong Hu
Keyword(s):  
Image Segmentation ◽  
Computational Complexity ◽  
Color Space ◽  
Color Images ◽  
Experimental Results ◽  
White Balance ◽  
Hsv Color Space ◽  
Low Computational Complexity ◽  
Automatic Adjustment ◽  
Automatic White Balance

Classic automatic white balance algorithm always been invalidity when there are large color-blocks or less of highlights points occurred in cast images. In this literature, an improved automatic adjustment algorithm based on image segmentation is proposed to resolve the problem mentioned above. First, color images were transformed to HSV color space and low saturation area was segmented from S channel. And then, adjustment parameters were calculated by selected points. Experimental results show that the algorithm can effectively correct varied cast image with low computational complexity, and are suitable for various scenarios.

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