True Color Image Segmentation by MUSIG Activation Function Using Self-Supervised QMLSONN Architecture With Context-Sensitive Thresholding

2018 ◽  
pp. 213-261
Author(s):  
Pankaj Pal ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Real Life ◽  
Activation Function ◽  
Input Image ◽  
Color Image Segmentation ◽  
Context Sensitive ◽  
True Color Image ◽  
Fuzziness Measure ◽  
True Color

In this chapter, the authors propose the true color image segmentation in real-life images as well as synthetic images by means of thresholded MUSIG function, which is learnt by quantum-formulated self-supervised neural network according to change of phase. In the initial phase, the true color image is segregated in the source module to fragment three different components—red, green, and blue colors—for three parallel layers of QMLSONN architecture. This information is fused in the sink module of QPSONN to get the preferred output. Each pixel of the input image is converted to the corresponding qubit neurons according to the phase manner. The interconnection weights between the layers are represented by qubit rotation gates. The quantum measurement at the output layer destroys the quantum states and gets the output for the processed information by means of quantum backpropagation algorithm using fuzziness measure.

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

2021 ◽  
pp. 164-196
Author(s):  
Sunanda Das ◽  
Sourav De ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Execution Time ◽  
Color Image ◽  
Computational Cost ◽  
Cost Effective ◽  
Color Image Segmentation ◽  
Modified Genetic Algorithm ◽  
True Color Image ◽  
True Color

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

True Color Image Segmentation Using Quantum-Induced Modified-Genetic-Algorithm-Based FCM Algorithm

2018 ◽  
pp. 55-94
Author(s):  
Sunanda Das ◽  
Sourav De ◽  
Siddhartha Bhattacharyya
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Execution Time ◽  
Color Image ◽  
Computational Cost ◽  
Cost Effective ◽  
Color Image Segmentation ◽  
Modified Genetic Algorithm ◽  
True Color Image ◽  
True Color

In this chapter, a quantum-induced modified-genetic-algorithm-based FCM clustering approach is proposed for true color image segmentation. This approach brings down the early convergence problem of FCM to local minima point, increases efficacy of conventional genetic algorithm, and decreases the computational cost and execution time. Effectiveness of genetic algorithm is tumid by modifying some features in population initialization and crossover section. To speed up the execution time as well as make it cost effective and also to get more optimized class levels some quantum computing phenomena like qubit, superposition, entanglement, quantum rotation gate are induced to modified genetic algorithm. Class levels which are yield now fed to FCM as initial input class levels; thus, the ultimate segmented results are formed. Efficiency of proposed method are compared with classical modified-genetic-algorithm-based FCM and conventional FCM based on some standard statistical measures.

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.

scholarly journals Optimized Graph cut Color Image Segmentation using Genetic Algorithm with Weighted Constraints (OGcut)

2019 ◽  
Vol 8 (4S2) ◽  
pp. 583-587
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Color Image ◽  
Graph Cut ◽  
Input Image ◽  
Color Image Segmentation ◽  
Weighted Constraints ◽  
Mutation Operators ◽  
Cut Method ◽  
Crossover And Mutation

This paper proposes a Novel color image segmentation using Graph cut method by minimizing the weighted energy function. This method is applying a pair of optimal constraints namely: color constraint and gradient constraint. In the state-of-the-art methods, the background and foreground details are manually initialized and used for verifying the smoothness of the region. But in this proposed method, they are dynamically calculated from the input image. This feature of the proposed method can be used in color image segmentation where more number of unique segments exists in a single image. The genetic algorithm is applied to the graph obtained from the graph cut method. The crossover and mutation operators are applied on various subgraphs to populate the different segments.

Clustering-Based Color Image Segmentation Using Local Maxima

2018 ◽  
Vol 14 (1) ◽  
pp. 28-47 ◽  
Author(s):  
Kalaivani Anbarasan ◽  
S. Chitrakala
Keyword(s):  
Image Analysis ◽  
Image Segmentation ◽  
Color Image ◽  
Automatic Segmentation ◽  
Input Image ◽  
Color Image Segmentation ◽  
Local Maxima ◽  
User Input ◽  
Texture Property ◽  
Multiple Regions

Color image segmentation has contributed significantly to image analysis and retrieval of relevant images. Color image segmentation helps the end user subdivide user input images into unique homogenous regions of similar pixels, based on pixel property. The success of image analysis is largely owing to the reliability of segmentation. The automatic segmentation of a color image into accurate regions without over-segmentation is a tedious task. Our paper focuses on segmenting color images automatically into multiple regions accurately, based on the local maxima of the GLCM texture property, with pixels spatially clustered into identical regions. A novel Clustering-based Image Segmentation using Local Maxima (CBIS-LM) method is presented. Our proposed approach generates reliable, accurate and non-overlapping multiple regions for the given user input image. The segmented regions can be automatically annotated with distinct labels which, in turn, help retrieve relevant images based on image semantics.

Color Image Segmentation of Live Grouper Fish with Complex Background in Seawater

2015 ◽  
Vol 743 ◽  
pp. 293-302 ◽  
Author(s):  
G.Q. Ma ◽  
Y.C. Tian ◽  
X.L. Li ◽  
K.Z. Xing ◽  
Su Xu
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Color Image ◽  
Color Images ◽  
Color Image Segmentation ◽  
Segmentation Method ◽  
Complex Background ◽  
Segmented Images ◽  
Rgb Images ◽  
True Color

The color live fish image segmentation is a important procedure of the understanding fish behavior. We have introduced an simple segmentation method of live Grouper Fish color images with seawater background and presented a segmentation framework to extract the whole fish image from the complex background of seawater. Firstly, we took true color pictures of live Grouper fish in seawater using waterproof camera and save these pictures files as RGB format files, called True-color Images. Secondly, we extracted R,G and B planes of a true color Grouper fish image, painted and compared their histograms of R,G and B planes. Thirdly, we segmented these RGB images and the R,G and B planes of a true color Grouper fish image with the k-means clustering algorithm, using the kmeans () function which is packaged by the Clustering Analysis ToolBox of Matlab 2012(a). Finally, we analyzed the relationships between these histograms and segmented images, and then got a conclusion is that : using the B plane of these RGB images as Input-matrix to do clustering segmentation algorithm by the kmeans () function of Matlab Clustering ToolBox, can got a fulfilling segmentation results.

Color image segmentation using parallel OptiMUSIG activation function

2012 ◽  
Vol 12 (10) ◽  
pp. 3228-3236 ◽  
Author(s):  
Sourav De ◽  
Siddhartha Bhattacharyya ◽  
Susanta Chakraborty
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Activation Function ◽  
Color Image Segmentation
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