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.

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

2018 ◽  
pp. 141-177
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.

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 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.

SCORING MODELING BASED ON NEURAL NETWORKS FOR DETERMINING A BANK BORROWER'S RATING

2020 ◽  
Vol 2020 (10) ◽  
pp. 54-62
Author(s):  
Oleksii VASYLIEV ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Network Architecture ◽  
Statistical Data ◽  
Activation Function ◽  
Decision Making Process ◽  
Neural Network Architecture ◽  
Acceptable Accuracy ◽  
The Neural Network ◽  
Sigmoid Activation Function

The problem of applying neural networks to calculate ratings used in banking in the decision-making process on granting or not granting loans to borrowers is considered. The task is to determine the rating function of the borrower based on a set of statistical data on the effectiveness of loans provided by the bank. When constructing a regression model to calculate the rating function, it is necessary to know its general form. If so, the task is to calculate the parameters that are included in the expression for the rating function. In contrast to this approach, in the case of using neural networks, there is no need to specify the general form for the rating function. Instead, certain neural network architecture is chosen and parameters are calculated for it on the basis of statistical data. Importantly, the same neural network architecture can be used to process different sets of statistical data. The disadvantages of using neural networks include the need to calculate a large number of parameters. There is also no universal algorithm that would determine the optimal neural network architecture. As an example of the use of neural networks to determine the borrower's rating, a model system is considered, in which the borrower's rating is determined by a known non-analytical rating function. A neural network with two inner layers, which contain, respectively, three and two neurons and have a sigmoid activation function, is used for modeling. It is shown that the use of the neural network allows restoring the borrower's rating function with quite acceptable accuracy.

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

Efficient Color Image Segmentation by a Parallel Optimized (ParaOptiMUSIG) Activation Function

2014 ◽  
pp. 19-50 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Neural Network ◽  
Network Architecture ◽  
Optimization Technique ◽  
Activation Function ◽  
Color Images ◽  
Color Image Segmentation ◽  
Neural Network Architecture ◽  
Segmented Images ◽  
True Color ◽  
Self Organizing

The optimized class responses from the image content has been applied to generate the optimized version of MUSIG (OptiMUSIG) activation function for a multilayer self organizing neural network architecture to effectively segment multilevel gray level intensity images. This chapter depicts the parallel version of the OptiMUSIG (ParaOptiMUSIG) activation function with the optimized class responses for the individual features with a parallel self-organizing neural network architecture to segment true color images. A genetic algorithm-based optimization technique has been employed to yield the optimized class responses in parallel. Comparison of the proposed method with the existing non-optimized method is applied on two real life true color images and is demonstrated with the help of three standard objective functions as they are employed to measure the quality of the segmented images. Results evolved by the ParaOptiMUSIG activation function are superior enough in comparison with the conventional nonoptimized MUSIG activation applied separately on the color gamut.

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