Accurate and reliable image classification by using conformal predictors in the TJ-II Thomson scattering

2010 ◽  
Vol 81 (10) ◽  
pp. 10E118 ◽  
Author(s):  
J. Vega ◽  
A. Murari ◽  
A. Pereira ◽  
S. González ◽  
I. Pastor
Keyword(s):  
Image Classification ◽  
Thomson Scattering ◽  
Conformal Predictors

Image classification by using a reduced set of features in the TJ-II Thomson Scattering diagnostic

2018 ◽  
Vol 129 ◽  
pp. 99-103
Author(s):  
Gonzalo Farias ◽  
Sebastián Dormido-Canto ◽  
Jesús Vega ◽  
Ismael Martínez ◽  
Gabriel Hermosilla ◽  
...  
Keyword(s):  
Image Classification ◽  
Thomson Scattering ◽  
Thomson Scattering Diagnostic

Active Learning Using Conformal Predictors: Application to Image Classification

2012 ◽  
Vol 62 (2) ◽  
pp. 347-355 ◽  
Author(s):  
Lázaro Emílio Makili ◽  
Jesús A. Vega Sánchez ◽  
Sebastián Dormido-Canto
Keyword(s):  
Active Learning ◽  
Image Classification ◽  
Conformal Predictors

THOMSON SCATTERING WITH A HIGH BACKGROUND LEVEL OF PLASMA RADIATION

1979 ◽  
Vol 40 (C7) ◽  
pp. C7-851-C7-852
Author(s):  
B. Van der Sijde ◽  
T. Poorter ◽  
S. Adema ◽  
B. F.M. Pots ◽  
D. C. Schram
Keyword(s):  
Background Level ◽  
Thomson Scattering ◽  
Plasma Radiation ◽  
High Background

MODELS BASED ON CONFORMAL PREDICTORS FOR DIAGNOSTIC SYSTEMS IN MEDICINE

2019 ◽  
Vol 2 (2) ◽  
pp. 127-137
Author(s):  
Igor Shevchuk ◽  
Nikolai Michaluk ◽  
Victoria Ruvinskaya
Keyword(s):  
Diagnostic Systems ◽  
Conformal Predictors

USING FUZZY CLUSTERING IN STRUCTURAL METHODS OF IMAGE CLASSIFICATION

2020 ◽  
Vol 79 (9) ◽  
pp. 781-791
Author(s):  
V. О. Gorokhovatskyi ◽  
I. S. Tvoroshenko ◽  
N. V. Vlasenko
Keyword(s):  
Image Classification ◽  
Fuzzy Clustering ◽  
Structural Methods

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

Sign in / Sign up

Export Citation Format

Share Document