A new design methodology for optimal interpolative neural networks with application to the localization and classification of acoustic transients

Purpose The purpose of this paper is to use convolutional neural networks in order to solve the problem of the difficulty in the classification of cashmere and wool. To do the research, it proposes a low-dimensional strategy of using part-level features to enhance object-level features. The study aims to use computer version method to find out the most effective and robust method to manage the difficult task of cashmere and wool identification. Design/methodology/approach The authors try to get a coarse classification result and the initial weights of the model in the first step. The authors use the results of the first step and a Fast-RCNN method to extract part-level features in step 2. Finally, the authors mix the part-level features to enhance object-level features and classify the cashmere and wool images. Findings The paper finds that not only the texture is the key element of the cashmere and wool identification but also the image colors. Originality/value Most importantly, the paper finds that the part-level features can enhance object-level features in the fiber identification task. However, it does not work in contrast, and the strategy can be used in the similar fibers identifications.

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Detection and classification of underwater acoustic transients using neural networks

IEEE Transactions on Neural Networks ◽

10.1109/72.317723 ◽

1994 ◽

Vol 5 (5) ◽

pp. 712-718 ◽

Cited By ~ 27

Author(s):

T.L. Hemminger ◽

Yoh-Han Pao

Keyword(s):

Neural Networks ◽

Underwater Acoustic ◽

Acoustic Transients

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Time Delay Neural Networks for the Classification of Flow Regimes

Computers & Chemical Engineering ◽

10.1016/s0098-1354(97)00076-8 ◽

1997 ◽

Vol 21 (1-2) ◽

pp. S367-S371

Author(s):

Y Yamashita

Keyword(s):

Neural Networks ◽

Time Delay ◽

Flow Regimes

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CNN-based Classification of Degraded Images

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.10.ipas-028 ◽

2020 ◽

Vol 2020 (10) ◽

pp. 28-1-28-7 ◽

Cited By ~ 1

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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