scholarly journals Identification of Virus in Microscopic Image Using Genetic Algorithm

2019 ◽  
Vol 8 (S2) ◽  
pp. 24-27
Author(s):  
N. Senthilkumaran ◽  
R. Preethi
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Edge Detection ◽  
Microscopic Image ◽  
Canny Edge Detection ◽  
Detection Techniques ◽  
Canny Edge ◽  
Sobel Edge Detection ◽  
The Many ◽  
Prewitt Edge Detection

In this paper describes a several techniques of effective edge detection by using image segmentation. The image segmentation provides various techniques to detect the edges on image. The paper mainly focused on edge detection using matlab parameters and solved the many problems. Edge detection techniques have a several type of techniques. We have taken microscopic image, which affects the human body by making diseases through viruses and bacteria’s. Now analyze only about the major techniques: a.) Roberts edge detection, b) sobel edge detection, c) prewitt edge detection, d) log (laplacian of gaussian) edge detection, e) genetic edge detection and f) canny edge detection. We have applied above five techniques which are used in edge detection and got a result on microscopic images. Hence, we scope this paper defines and compares the variety of techniques and demand assures the genetic algorithm provides a better performance on edge detection using microscopic image.

A Study on Parallel and Pipelining Simulation Techniques for Edge Detection and Their Performance Analysis

2019 ◽  
Vol 16 (2) ◽  
pp. 568-572
Author(s):  
Merlin L. M. Livingston ◽  
Senthil C. Singh ◽  
K. Manojkumar ◽  
Sathish S. Kumar
Keyword(s):  
Edge Detection ◽  
Prime Number ◽  
Parallel Simulation ◽  
Canny Edge Detection ◽  
Simulation Techniques ◽  
Image Edge Detection ◽  
Multi Stage ◽  
Canny Edge ◽  
Image Edge ◽  
Sobel Edge Detection

Real processing components along with component simulators are combined together to construct a new virtual prototyping system. The increase in component simulators result in degraded performance of the simulation in distributed systems. The speed of simulation can be increased by doing parallel simulation techniques. Prime number test and Image edge detection are chosen to implement the parallel simulation techniques and achieved the expected results while implementing in real time applications. The prime number test calculates the number of processors in a system and the image edge detection can be done in two stages by Canny Edge detection and Sobel Edge detection. The Canny Edge detection is used to detect the edges in the images by using a multi-stage algorithm. The smaller, separable and integer valued filter in images are combined in horizontal and vertical directions by using the Sobel edge detection resulting in reduction of implementation cost. The tool named OpenMP is used for implementing the parallel simulation techniques by combining both the canny edge and Sobel edge detection. An add-on named MPI is used along with the OpenMP to reduce the implementation time in parallel processing.

scholarly journals Research on Improved Canny Edge Detection Algorithm

2018 ◽  
Vol 232 ◽  
pp. 03053
Author(s):  
Ruiyuan Liu ◽  
Jian Mao
Keyword(s):  
Genetic Algorithm ◽  
Edge Detection ◽  
Detection Algorithm ◽  
Noise Robustness ◽  
Canny Edge Detection ◽  
Edge Detection Algorithm ◽  
Canny Edge ◽  
Image Edge ◽  
Canny Algorithm ◽  
Canny Edge Detection Algorithm

Aiming at the poor noise robustness of traditional Canny algorithm and the defect of false edge or edge loss, an edge detection algorithm using statistical algorithm for filtering denoising and using genetic algorithm to determine the optimal high and low threshold of image segmentation is proposed. Firstly, statistical filtering uses mean and variance to denoise, avoiding the problem of Gaussian denoising susceptible to interference in the traditional Canny algorithm, and ensuring the integrity of image edge information. Secondly, this article uses the genetic algorithm, design the crossover operator and genetic operator to modify the evolution of the population, and determine the optimal height threshold of the image edge connection to make the threshold more accurate. Finally, using MATLAB software to simulate, the results show that the improved Canny edge detection algorithm can further improve the anti-noise ability and robustness, and the edge location is more accurate.

Research on Edge Detection Technology

2013 ◽  
Vol 860-863 ◽  
pp. 2884-2887 ◽  
Author(s):  
Yu Bing Dong ◽  
Ming Jing Li ◽  
Hai Yan Wang
Keyword(s):  
Image Processing ◽  
Edge Detection ◽  
Canny Edge Detection ◽  
Detection Techniques ◽  
Image Edge Detection ◽  
Detection Technology ◽  
Canny Edge ◽  
Important Field ◽  
Image Edge ◽  
Better Than

Edge detection is an important field in image processing. Edge detection is in the forefront of image processing for object detection, it is crucial to have a good understanding of edge detection techniques. Various image edge detection techniques are introduced. These techniques are compared by using MATLAB7.0. The qualities of these techniques are elaborated. The results show that Canny edge detection techniques is better than others.

scholarly journals Performance comparison of segmentation algorithms for hand gesture recognition

2018 ◽  
Vol 7 (3) ◽  
pp. 1227
Author(s):  
Priyanka Parvathy D ◽  
Dr Kamalraj Subramaniam
Keyword(s):  
Image Segmentation ◽  
Edge Detection ◽  
Gesture Recognition ◽  
Noise Removal ◽  
Hand Gesture Recognition ◽  
Segmentation Algorithm ◽  
Hand Gesture ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Image Segmentation Algorithm

The gestures presented in diverse backgrounds have to be accurately processed and segmented, for it to be classified precisely by the hand gesture recognition system. This study compares performance of the proposed Image Segmentation Algorithm with a standard Canny Edge Detection Algorithm by comparing the statistical values of the features obtained from the feature extraction stage, thus validating the importance of having a robust preprocessing stage for the hand gestures. The proposed algorithm uses Non-local Mean filter for noise removal and then an improved Global Swarm Optimization based Canny edge detection for extracting the edges. Features are extracted using two dimensional Multi-resolution Discrete Wavelet Transform (2D-DWT) combined with Gray-level Co-occurrence Matrix. The efficiency of the proposed Image Segmentation Algorithm is evaluated using Radial Basis Function Neural Network as the classifier.  

scholarly journals PENGENALAN POLA HURUF HIJAIYAH KHAT KUFI DENGAN METODE DETEKSI TEPI SOBEL BERBASIS JARINGAN SYARAF TIRUAN BACKPROPAGATION

2018 ◽  
Vol 11 (1) ◽  
pp. 37-46
Author(s):  
Irvan Faturrahman
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Artificial Neural Network ◽  
Edge Detection ◽  
Learning Rate ◽  
Canny Edge Detection ◽  
Canny Edge ◽  
Rate Test ◽  
Artificial Neural ◽  
Sobel Edge Detection

ABSTRAK Khat kufi memiliki bentuk huruf hijaiyah yang unik berbentuk kotak. Banyak penelitian yang membahas pengenalan huruf hijaiyah namun untuk spesifik khat belum ada. Pada penelitian ini penulis melakukan simulasi pengenalan pola huruf hijaiyah khat kufi menggunakan deteksi tepi sobel dan jaringan syaraf tiruan backpropagation dengan menggunakan parameter uji learning rate dan epoch. Simulasi dilakukan 28 target huruf hijaiyah dengan learning rate 0.01, 0.05, 0.1, 0.5, dan epoch 25, 1000, 3000, 5000, 10000. Akurasi terbaik didapatkan pada learning rate 0.01 dan epoch 10000 yaitu 100%. Penelitian ini dapat dikembangkan menggunakan deteksi tepi canny, prewitt, atau robert serta JST LVQ, ADALINE, atau RBF.   ABSTRACT Khat kufi has a unique hijaiyah shape that is square in shape. Much of the research that discusses the introduction of the hijaiyah letters but for the specifics khat does not yet exist. In this study, the author performs a simulation of hijaiyah khat kufi pattern recognition using sobel edge detection and artificial neural network backpropagation using learning rate test and epoch parameters. The simulation has been done on 28 target letters hijaiyah with learning rate 0.01, 0.05, 0.1, 0.5, and epoch 25, 1000, 3000, 5000, 10000. The best accuracy obtained at learning rate 0.01 and epoch 10000 is 100%. This research can be developed using canny edge detection, prewitt, or robert and also JST LVQ, ADALINE, or RBF. How To Cite : Faturrahman, I. Arini. Mintarsih, F. (2018). PENGENALAN POLA HURUF HIJAIYAH KHAT KUFI DENGAN METODE DETEKSI TEPI SOBEL BERBASIS JARINGAN SYARAF TIRUAN BACKPROPAGATION. Jurnal Teknik Informatika, 11(1), 37-46.  doi 10.15408/jti.v11i1.6262 Permalink/DOI: http://dx.doi.org/10.15408/jti.v11i1.6262  

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