A new neural network model for medical color image segmentation

2006 ◽  
Author(s):  
Chen Tang ◽  
Tengliang Luo ◽  
Guimin Zhang ◽  
Fang Zhang
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Model ◽  
Neural Network Model ◽  
Color Image ◽  
Color Image Segmentation

Research on Underwater Polarization Image Segmentation Inspired by Biological Optic Nerve

2013 ◽  
Vol 347-350 ◽  
pp. 2178-2184
Author(s):  
Hui Bin Wang ◽  
Yu Rong Wu ◽  
Jie Shen ◽  
Zhe Chen
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Model ◽  
Neural Network Model ◽  
Underwater Image ◽  
The Neural Network ◽  
Intensity Information ◽  
Segmentation Methods ◽  
Polarization Image ◽  
Feedback Neural Network

Due to effects of the light by water and other particles, the quality of underwater image will degrade. The traditional underwater image segmentation methods based on intensity and spectrum have difficulty in determining boundary. Inspired by the visual system of mantis shrimps, this paper constructed a feedback neural network model, in which the parameters were optimized using machine learning method. Based on this model, we combine the polarization and intensity information to achieve the underwater polarization image segmentation. The results of experiment prove that the neural network model designed in this paper can improve the accuracy of underwater image segmentation.

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.

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