Shape Based Image Classification and Retrieval with Multiresolution Enhanced Orthogonal Polynomial Model

2013 ◽  
pp. 163-170
Author(s):  
S. Sathiya Devi ◽  
R. Krishnamoorthi
Keyword(s):  
Orthogonal Polynomial ◽  
Image Classification ◽  
Polynomial Model

SAGE-based estimation of doubly selective channel with an orthogonal polynomial model

2008 ◽  
Vol 88 (4) ◽  
pp. 1061-1068 ◽  
Author(s):  
Feng Yang ◽  
Jian Song ◽  
Yu Zhang ◽  
Changyong Pan ◽  
Zhixing Yang
Keyword(s):  
Orthogonal Polynomial ◽  
Polynomial Model ◽  
Doubly Selective Channel ◽  
Selective Channel

METHOD OF ASSESSMENT OF THE COMMUNICATION CHANNEL BASED ON THE POLYNOMIAL MODEL OF PULSE CHARACTERISTICS

2019 ◽  
Vol 41 ◽  
pp. 53-62
Author(s):  
I. A. Batyrev ◽  
◽  
S. M. Dobrovolskiy ◽  
A. M. Semenov ◽  
Yu. F. Strugov ◽  
...  
Keyword(s):  
Communication Channel ◽  
Polynomial Model ◽  
Pulse Characteristics ◽  
Method Of Assessment

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.

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