Image segmentation using continuous cellular automata

2011 ◽  
Author(s):  
Djemame Safia ◽  
Djidel Oussama ◽  
Batouche Mohamed Chawki
Keyword(s):  
Image Segmentation ◽  
Cellular Automata

scholarly journals Land Use Land Cover map segmentation using Remote Sensing: A Case study of Ajoy river watershed, India

2020 ◽  
Vol 30 (1) ◽  
pp. 273-286
Author(s):  
Kalyan Mahata ◽  
Rajib Das ◽  
Subhasish Das ◽  
Anasua Sarkar
Keyword(s):  
Land Use ◽  
Image Segmentation ◽  
Cellular Automata ◽  
Land Cover ◽  
Satellite Imagery ◽  
Current Model ◽  
Discrete Dynamical System ◽  
Land Use Land Cover ◽  
Data Set ◽  
Validity Indices

Abstract Image segmentation in land cover regions which are overlapping in satellite imagery, is one crucial challenge. To detect true belonging of one pixel becomes a challenging problem while classifying mixed pixels in overlapping regions. In current work, we propose one new approach for image segmentation using a hybrid algorithm of K-Means and Cellular Automata algorithms. This newly implemented unsupervised model can detect cluster groups using hybrid 2-Dimensional Cellular-Automata model based on K-Means segmentation approach. This approach detects different land use land cover areas in satellite imagery by existing K-Means algorithm. Since it is a discrete dynamical system, cellular automaton realizes uniform interconnecting cells containing states. In the second stage of current model, we experiment with a 2-dimensional cellular automata to rank allocations of pixels among different land-cover regions. The method is experimented on the watershed area of Ajoy river (India) and Salinas (California) data set with true class labels using two internal and four external validity indices. The segmented areas are then compared with existing FCM, DBSCAN and K-Means methods and verified with the ground truth. The statistical analysis results also show the superiority of the new method.

An Improved Method for Edge Detection and Image Segmentation Using Fuzzy Cellular Automata

2016 ◽  
Vol 47 (3) ◽  
pp. 161-179 ◽  
Author(s):  
Reza Shahverdi ◽  
Madjid Tavana ◽  
Ali Ebrahimnejad ◽  
Khadijeh Zahedi ◽  
Hesam Omranpour
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Improved Method ◽  
Fuzzy Cellular Automata

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection.

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

scholarly journals Using Semi-Supervised Learning in Cellular Automata for Edge Detection of Image Segmentation

2018 ◽  
Vol 7 (02) ◽  
pp. 23613-23619
Author(s):  
Draiya A. Alaswad ◽  
Yasser F. Hassan
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Supervised Learning ◽  
Unlabeled Data ◽  
Machine Learning Techniques ◽  
Natural Image ◽  
Clustering Method ◽  
Learning Techniques ◽  
Better Than

Semi-Supervised Learning is an area of increasing importance in Machine Learning techniques that make use of both labeled and unlabeled data. The goal of using both labeled and unlabeled data is to build better learners instead of using each one alone. Semi-supervised learning investigates how to use the information of both labeled and unlabeled examples to perform better than supervised learning. In this paper we present a new method for edge detection of image segmentation using cellular automata with modification for game of life rules and K-means algorithm. We use the semi-supervised clustering method, which can jointly learn to fusion by making use of the unlabeled data. The learning aim consists in distinguishing between edge and no edge for each pixel in image. We have applied the semi-supervised method for finding edge detection in natural image and measured its performance using the Berkeley Segmentation Dataset and Benchmark dataset. The results and experiments showed the accuracy and efficiency of the proposed method.

Image Segmentation Based on Learning Cellular Automata Using Soft Computing Approach

2010 ◽  
Author(s):  
Debasis Das ◽  
Abhishek Ray ◽  
Swapan Paruya ◽  
Samarjit Kar ◽  
Suchismita Roy
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Soft Computing ◽  
Computing Approach

TUNNELING PHASE LOGIC CELLULAR NONLINEAR NETWORKS

2001 ◽  
Vol 11 (12) ◽  
pp. 2895-2911 ◽  
Author(s):  
TAO YANG ◽  
RICHARD A. KIEHL ◽  
LEON O. CHUA
Keyword(s):  
Image Segmentation ◽  
Cellular Automata ◽  
Edge Detection ◽  
Voltage Source ◽  
Sinusoidal Voltage ◽  
Simple Circuit ◽  
Dc Voltage ◽  
Nonlinear Networks ◽  
2D Arrays ◽  
Image Computation

Based on a simple circuit model of a tunneling phase logic (TPL) element that is driven by a sinusoidal voltage source and biased by a DC voltage source, we present simulations of operations in cellular nonlinear networks (CNN) that could potentially be used to perform general computations in 2D arrays of simple, locally connected nanoscale devices. Some examples are presented to demonstrate the image computation capability of TPL–CNN. In particular, we use a simple 2D TPL–CNN structure to perform edge detection, image enhancement and image segmentation. Some cellular automata (CA)-like behaviors of our 2D TPL-CNN are also presented.

scholarly journals Detection of Tumor Cells in Brain using Cellular Automata with Image Segmentation and Edge Detection

2019 ◽  
Vol 9 (4) ◽  
pp. 11-13
Author(s):  
Dr Kumaravel A. ◽  
◽  
Jasmeena Tariq ◽  
Keyword(s):  
Image Segmentation ◽  
Brain Tumor ◽  
Cellular Automata ◽  
Edge Detection ◽  
Human Life ◽  
Computation Time ◽  
Mri Scan ◽  
Magnetic Resonance Imaging Mri ◽  
Medical Examinations ◽  
Cancerous Cells

Tumor growth or, growth of cancerous cells is a big challenge in today’s medical word. When dealing with human life, the detection of tumors through computers has to be highly accurate. Thus we require the assistance of computer in medical examinations, so that we will get very low rate of false cases. Brain tumor, in today’s world, is seen as most threatening and life taking disease. In order to detect brain tumor more accurately in lesser time, many techniques have already been proposed using image segmentation and edge detection. In our paper we propose a technique which is more efficient to detect brain tumor where edge detection through cellular automata have been used from Magnetic Resonance Imaging (MRI) scan images. It processes these images, and determines the area affected by using segmentation and edge detection with cellular automata. Simulated work is completed with the help of Simulink in MATLAB. Regarding this particular topic there are many studies, however our proposal of combination of both segmentation and edge detection through cellular automata shows better results as compared to combining segmentation with classical edge detection in term of computation time and clarity. This will help in efficiency of detecting brain tumor and later in its removal.

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