scholarly journals Simulated Annealing: A Novel Application of Image Processing in the Wood Area

10.5772/50635 ◽  
2012 ◽  
Author(s):  
Cristhian A. ◽  
Mario A. ◽  
Angel D.
Keyword(s):  
Image Processing ◽  
Simulated Annealing

Computing the Medical Image Registration Using Meta-Heuristics

2014 ◽  
Vol 643 ◽  
pp. 237-242 ◽  
Author(s):  
Tahari Abdou El Karim ◽  
Bendakmousse Abdeslam ◽  
Ait Aoudia Samy
Keyword(s):  
Image Processing ◽  
Genetic Algorithms ◽  
Simulated Annealing ◽  
Image Registration ◽  
Cost Function ◽  
Population Genetic ◽  
Medical Image ◽  
Medical Image Registration ◽  
Anatomical Structures

The image registration is a very important task in image processing. In the field of medical imaging, it is used to compare the anatomical structures of two or more images taken at different time to track for example the evolution of a disease. Intensity-based techniques are widely used in the multi-modal registration. To have the best registration, a cost function expressing the similarity between these images is maximized. The registration problem is reduced to the optimization of a cost function. We propose to use neighborhood meta-heuristics (tabu search, simulated annealing) and a meta-heuristic population (genetic algorithms). An evaluation step is necessary to estimate the quality of registration obtained. In this paper we present some results of medical image registration

Image processing by simulated annealing

1987 ◽  
pp. 551-561
Author(s):  
P. Carnevali ◽  
L. Coletti ◽  
S. Patarnello
Keyword(s):  
Image Processing ◽  
Simulated Annealing

scholarly journals Detection of Heart Abnormalities Based On ECG Signal Characteristics using Multilayer Perceptron with Firefly Algorithm-Simulated Annealing

2021 ◽  
Vol 3 (1) ◽  
pp. 45
Author(s):  
Sofiah Ishlakhul Abda ◽  
Auli Damayanti ◽  
Edi Winarko
Keyword(s):  
Image Processing ◽  
Heart Disease ◽  
Simulated Annealing ◽  
Firefly Algorithm ◽  
Training Data ◽  
Ecg Signal ◽  
Multi Layer Perceptron ◽  
Validation Test ◽  
Signal Characteristics ◽  
Heart Abnormalities

Heart disease is one of the causes of death worldwide. Therefore, detecting heart disease is very important to reduce the increased mortality rate. One of the methods used to detect the abnormalities or disorders of the heart is to use computer assistance to determine the characteristics of an electrocardiogram. Electrocardiogram (ECG) is a test that detects and records the activity of the heart through small metal electrodes attached to the skin of one's chest, arms and legs. This test shows how fast the heart beats and whether the rhythm is stable or not. The purpose of this thesis is to apply a multi-layer perceptron model with firefly algorithm and simulated annealing in detecting cardiac abnormalities based on the ECG signal characteristics. The initial step of this research is image processing. The stages of ECG image processing are grayscale, thresholding, edge detection, segmentation and normalization processes. The results of this image processing are used as input matrices in the perceptron multilayer network training using firefly algorithm and simulated annealing. In the training process, we will get optimal weights and biases for validation tests on test data. The training data in this thesis uses 20 ECG images and in the validation test process uses 10 ECG images. The validation results in the validation test show that the accuracy in detecting heart abnormalities based on the characteristics of ECG signals using multi- layer perceptron with firefly algorithm and simulated annealing is 100%.

scholarly journals Designing a metaheuristic mining algorithm to separate two-color points in a two-dimensional environment

2019 ◽  
Vol 7 (2) ◽  
Author(s):  
Parisa Aghazade ◽  
Alireza Bagheri ◽  
Mohamadmansoor Riahi Kashani
Keyword(s):  
Image Processing ◽  
Simulated Annealing ◽  
Computational Geometry ◽  
Metaheuristic Algorithm ◽  
Two Dimensional ◽  
Optimal Response ◽  
Mining Algorithm ◽  
Optimum Algorithm ◽  
Recommended Algorithm ◽  
Annealing Method

The separation of color points is one of the important issues in computational geometry, which is used in various parts of science; it can be used in facility locating, image processing and clustering. Among these, one of the most widely used computational geometry in the real-world is the problem of covering and separating points with rectangles. In this paper, we intend to consider the problemof separating the two-color points sets, using three rectangles. In fact, our goal is to separate desired blue points from undesired red points by three rectangles, in such a way that these three rectangles contain the most desire points. For this purpose, we provide a metaheuristic algorithm based on the simulated annealing method that could separates blue points from input points, , in time order O (n) with the help of three rectangles. The algorithm is executed with C# and also it has been compared and evaluated with the optimum algorithm results. The results show that our recommended algorithm responses is so close to optimal responses, and also in some cases we obtains the exact optimal response.

Curve Representation Using Principal Component Analysis for Shape Optimization of Path Generating Mechanisms

2005 ◽  
Author(s):  
Nassim Khaled ◽  
Ahmad Smaili
Keyword(s):  
Image Processing ◽  
Principal Component Analysis ◽  
Simulated Annealing ◽  
Shape Optimization ◽  
Principal Component ◽  
Component Analysis ◽  
Path Generation ◽  
Starting Point ◽  
Curve Representation ◽  
Generation Mechanisms

The focus of this paper is on the synthesis of path generation mechanisms based on shape optimization. The principle component analysis (PCA) technique used in image processing is employed to represent the desired coupler curve of the mechanism and simulated annealing is used as the optimization tool. PCA representation is invariant under rotation, translation, scaling, and starting point. Once a shape-optimized mechanism is found, it is translated, rotated, and scaled to its final form. An illustrative example is introduced to demonstrate the proposed method.

Image processing by simulated annealing

1985 ◽  
Vol 29 (6) ◽  
pp. 569-579 ◽  
Author(s):  
P. Carnevali ◽  
L. Coletti ◽  
S. Patarnello
Keyword(s):  
Image Processing ◽  
Simulated Annealing
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