scholarly journals Fuzzy U-Net Neural Network Architecture Optimization for Image Segmentation

2021 ◽  
Vol 1031 (1) ◽  
pp. 012077
Author(s):  
M M Kirichev ◽  
T S Slavov ◽  
G D Momcheva
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Neural Network Architecture ◽  
Architecture Optimization

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.

Range image segmentation by dynamic neural network architecture

1996 ◽  
Vol 29 (2) ◽  
pp. 315-329 ◽  
Author(s):  
V. Chandrasekaran ◽  
M. Palaniswami ◽  
Terry M. Caelli
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Range Image ◽  
Neural Network Architecture ◽  
Dynamic Neural Network

Grayscale Image Segmentation With Quantum-Inspired Multilayer Self-Organizing Neural Network Architecture Endorsed by Context Sensitive Thresholding

2021 ◽  
pp. 197-227
Author(s):  
Pankaj Pal ◽  
Siddhartha Bhattacharyya ◽  
Nishtha Agrawal
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Network Architecture ◽  
Activation Function ◽  
Grayscale Image ◽  
Threshold Values ◽  
Neural Network Architecture ◽  
Context Sensitive ◽  
Segmentation Process ◽  
Sigmoidal Activation Function

A method for grayscale image segmentation is presented using a quantum-inspired self-organizing neural network architecture by proper selection of the threshold values of the multilevel sigmoidal activation function (MUSIG). The context-sensitive threshold values in the different positions of the image are measured based on the homogeneity of the image content and used to extract the object by means of effective thresholding of the multilevel sigmoidal activation function guided by the quantum superposition principle. The neural network architecture uses fuzzy theoretic concepts to assist in the segmentation process. The authors propose a grayscale image segmentation method endorsed by context-sensitive thresholding technique. This quantum-inspired multilayer neural network is adapted with self-organization. The architecture ensures the segmentation process for the real-life images as well as synthetic images by selecting intensity parameter as the threshold value.

Multilevel Image Segmentation by a Multiobjective Genetic Algorithm Based OptiMUSIG Activation Function

2013 ◽  
pp. 122-162 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Image Segmentation ◽  
Network Architecture ◽  
Activation Function ◽  
Color Images ◽  
Neural Network Architecture ◽  
Gray Scale ◽  
Multiobjective Genetic Algorithm ◽  
True Color

The proposed chapter is intended to propose a self supervised image segmentation method by a multi-objective genetic algorithm based optimized MUSIG (OptiMUSIG) activation function with a multilayer self organizing neural network architecture to segment multilevel gray scale intensity images. The multiobjective genetic algorithm based parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with a parallel self organizing neural network architecture is also discussed to segment true color images. These methods are quite efficient enough to overcome the drawbacks of the single objective based OptiMUSIG and ParaOptiMUSIG activation functions to segment gray scale and true color images, respectively. The proposed multiobjective genetic algorithm based optimization methods are applied on three standard objective functions to measure the quality of the segmented images. These functions form the multiple objective criteria of the multiobjective genetic algorithm based image segmentation method.

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