Quantitative evaluation of color image segmentation results using fuzzy neural network

2008 ◽  
Author(s):  
Didier Coquin
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Quantitative Evaluation ◽  
Fuzzy Neural Network ◽  
Color Image ◽  
Color Image Segmentation ◽  
Fuzzy Neural

Quantitative evaluation of color image segmentation results

1998 ◽  
Vol 19 (8) ◽  
pp. 741-747 ◽  
Author(s):  
M. Borsotti ◽  
P. Campadelli ◽  
R. Schettini
Keyword(s):  
Image Segmentation ◽  
Quantitative Evaluation ◽  
Color Image ◽  
Color 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.

Multilevel and Color Image Segmentation by NSGA II Based OptiMUSIG Activation Function

2016 ◽  
pp. 321-348 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Image ◽  
Activation Function ◽  
Color Image Segmentation ◽  
Nsga Ii ◽  
Segmented Images ◽  
Single Objective ◽  
Self Organizing

A self-supervised image segmentation method by a non-dominated sorting genetic algorithm-II (NSGA-II) based optimized MUSIG (OptiMUSIG) activation function with a multilayer self-organizing neural network (MLSONN) architecture is proposed to segment multilevel gray scale images. In the same way, another NSGA-II based parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with a parallel self-organizing neural network (PSONN) architecture is purported to segment the color images in this article. These methods are intended to overcome the drawback of their single objective based counterparts. Three standard objective functions are employed as the multiple objective criteria of the NSGA-II algorithm to measure the quality of the segmented images.

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