Adaptive Two-Pass Median Filter Based on Support Vector Machines for Image Restoration

2004 ◽  
Vol 16 (2) ◽  
pp. 333-354 ◽  
Author(s):  
Tzu-Chao Lin ◽  
Pao-Ta Yu
Keyword(s):  
Support Vector Machines ◽  
Image Restoration ◽  
Impulse Noise ◽  
Median Filter ◽  
Support Vector ◽  
New Approach ◽  
Median Filters ◽  
Vector Machines ◽  
Impulse Detector

In this letter, a novel adaptive filter, the adaptive two-pass median (ATM) filter based on support vector machines (SVMs), is proposed to preserve more image details while effectively suppressing impulse noise for image restoration. The proposed filter is composed of a noise decision maker and two-pass median filters. Our new approach basically uses an SVM impulse detector to judge whether the input pixel is noise. If a pixel is detected as a corrupted pixel, the noise-free reduction median filter will be triggered to replace it. Otherwise, it remains unchanged. Then, to improve the quality of the restored image, a decision impulse filter is put to work in the second-pass filtering procedure. As for the noise suppressing both fixed-valued and random-valued impulses without degrading the quality of the fine details, the results of our extensive experiments demonstrate that the proposed filter outperforms earlier median-based filters in the literature. Our new filter also provides excellent robustness at various percentages of impulse noise.

scholarly journals Adaptive Switching Median Filter for Impulse Noise Removal Based on Support Vector Machines

2011 ◽  
Vol 18 (6) ◽  
pp. 871-886
Author(s):  
Dae-Geun Lee ◽  
Min-Jae Park ◽  
Jeong-Ok Kim ◽  
Do-Yoon Kim ◽  
Dong-Wook Kim ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Impulse Noise ◽  
Median Filter ◽  
Noise Removal ◽  
Support Vector ◽  
Impulse Noise Removal ◽  
Switching Median Filter ◽  
Vector Machines

Persian Handwritten Number Recognition Using Adapted Framing Feature and Support Vector Machines

2016 ◽  
Vol 15 (01) ◽  
pp. 1650004 ◽  
Author(s):  
Hedieh Sajedi ◽  
Mehran Bahador
Keyword(s):  
Support Vector Machines ◽  
Recognition Rate ◽  
Nearest Neighbors ◽  
Polynomial Kernel ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
New Approach ◽  
Number Recognition ◽  
Vector Machines

In this paper, a new approach for segmentation and recognition of Persian handwritten numbers is presented. This method utilizes the framing feature technique in combination with outer profile feature that we named this the adapted framing feature. In our proposed approach, segmentation of the numbers into digits has been carried out automatically. In the classification stage of the proposed method, Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN) are used. Experimentations are conducted on the IFHCDB database consisting 17,740 numeral images and HODA database consisting 102,352 numeral images. In isolated digit level on IFHCDB, the recognition rate of 99.27%, is achieved by using SVM with polynomial kernel. Furthermore, in isolated digit level on HODA, the recognition rate of 99.07% is achieved by using SVM with polynomial kernel. The experiments illustrate that applying our proposed method resulted higher accuracy compared to previous researches.

scholarly journals Microbiological Quality Assessment of Chicken Thigh Fillets Using Spectroscopic Sensors and Multivariate Data Analysis

Foods ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 2723
Author(s):  
Evgenia D. Spyrelli ◽  
Christina Papachristou ◽  
George-John E. Nychas ◽  
Efstathios Z. Panagou
Keyword(s):  
Support Vector Machines ◽  
Discriminant Analysis ◽  
Microbiological Quality ◽  
Poultry Meat ◽  
Support Vector ◽  
Classification Models ◽  
Pseudomonas Spp ◽  
Vector Machines ◽  
Ft Ir

Fourier transform infrared spectroscopy (FT-IR) and multispectral imaging (MSI) were evaluated for the prediction of the microbiological quality of poultry meat via regression and classification models. Chicken thigh fillets (n = 402) were subjected to spoilage experiments at eight isothermal and two dynamic temperature profiles. Samples were analyzed microbiologically (total viable counts (TVCs) and Pseudomonas spp.), while simultaneously MSI and FT-IR spectra were acquired. The organoleptic quality of the samples was also evaluated by a sensory panel, establishing a TVC spoilage threshold at 6.99 log CFU/cm2. Partial least squares regression (PLS-R) models were employed in the assessment of TVCs and Pseudomonas spp. counts on chicken’s surface. Furthermore, classification models (linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), support vector machines (SVMs), and quadratic support vector machines (QSVMs)) were developed to discriminate the samples in two quality classes (fresh vs. spoiled). PLS-R models developed on MSI data predicted TVCs and Pseudomonas spp. counts satisfactorily, with root mean squared error (RMSE) values of 0.987 and 1.215 log CFU/cm2, respectively. SVM model coupled to MSI data exhibited the highest performance with an overall accuracy of 94.4%, while in the case of FT-IR, improved classification was obtained with the QDA model (overall accuracy 71.4%). These results confirm the efficacy of MSI and FT-IR as rapid methods to assess the quality in poultry products.

A new approach for ICD rhythm classification based on support vector machines

2009 ◽  
Author(s):  
B. Kamousi ◽  
A. Tewfik ◽  
B. Lin ◽  
A. Al-Ahmad ◽  
H. Hsia ◽  
...  
Keyword(s):  
Support Vector Machines ◽  
Support Vector ◽  
New Approach ◽  
Vector Machines

FEATURE SELECTION FOR SUPPORT VECTOR MACHINES USING GENETIC ALGORITHMS

2004 ◽  
Vol 13 (04) ◽  
pp. 791-800 ◽  
Author(s):  
HOLGER FRÖHLICH ◽  
OLIVIER CHAPELLE ◽  
BERNHARD SCHÖLKOPF
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Support Vector ◽  
Generalization Error ◽  
New Approach ◽  
Vector Machines ◽  
Selection For ◽  
Natural Way

The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new approach is compared to the traditional method of performing cross-validation and to other existing algorithms for feature selection.

A new approach to simulate characterization of particulate matter employing support vector machines

2011 ◽  
Vol 186 (2-3) ◽  
pp. 1254-1262 ◽  
Author(s):  
K. Mogireddy ◽  
V. Devabhaktuni ◽  
A. Kumar ◽  
P. Aggarwal ◽  
P. Bhattacharya
Keyword(s):  
Particulate Matter ◽  
Support Vector Machines ◽  
Support Vector ◽  
New Approach ◽  
Vector Machines
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