Comparative analysis of function approximation methods applied to image processing

2007 ◽  
Vol 17 (2) ◽  
pp. 217-221 ◽  
Author(s):  
V. V. Sergeev ◽  
V. N. Kopenkov ◽  
A. V. Chernov
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Function Approximation ◽  
Approximation Methods ◽  
Analysis Of Function

scholarly journals COMPARATIVE ANALYSIS OF APPROXIMATION METHODS USING REGRESSION RELATIONSHIPS AND NEURAL NETWORKS FOR LINEAR MODELS

2021 ◽  
Vol 2021 (1) ◽  
pp. 31-35
Author(s):  
Svetlana Senotova
Keyword(s):  
Neural Networks ◽  
Comparative Analysis ◽  
Linear Models ◽  
Approximation Methods

The paper examines comparative analysis of approximation methods using regression dependencies and neural networks for linear models.

Comparative Analysis on Noise Processing of Image

2013 ◽  
Vol 718-720 ◽  
pp. 2159-2162
Author(s):  
Hua Jun Dong ◽  
Xue Mei Jiang ◽  
Chen Xu Niu
Keyword(s):  
Image Processing ◽  
Comparative Analysis ◽  
Digital Image Processing ◽  
Digital Image ◽  
Median Filter ◽  
Wiener Filter ◽  
Image Noise ◽  
Two Dimensional ◽  
Wavelet Filter ◽  
Average Filter

The existence of noises have great interference on image processing, so the elimination of image noise is of great importance. In this paper, based on the digital image processing, the methods of average filter, wiener filter, median filter, two-dimensional wavelet filter, maximum and minimum filter are used to eliminate the salt & pepper noise of image. Then we analysis and compare the results of the five methods to find the best way to eliminate the image noise.

scholarly journals ANN analysis in a vision approach for potato inspection

2008 ◽  
Vol 6 (02) ◽  
Author(s):  
R. Rios-Cabrera ◽  
I Lopez-Juarez ◽  
Hsieh Sheng-Jen
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Feature Extraction ◽  
Artificial Neural Networks ◽  
Comparative Analysis ◽  
Physical Properties ◽  
Real Time ◽  
The Other ◽  
Stability And Convergence

An image processing methodology for the extraction of potato properties is explained. The objective is to determine their quality evaluating physical properties and using Artificial Neural Networks (ANN’s) to find misshapen potatoes. A comparative analysis for three connectionist models (Backpropagation, Perceptron and FuzzyARTMAP), evaluating speed and stability for classifying extracted properties is presented. The methodology for image processing and pattern feature extraction is presented together with some results. These results showed that FuzzyARTMAP outperformed the other models due to its stability and convergence speed with times as low as 1 ms per pattern which demonstrates its suitability for real-time inspection. Several algorithms to determine potato defects such as greening, scab, cracks are proposed which can be affectively used for grading different quality of potatoes.

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