Image Description Via The Multiresolution Intensity Axis Of Symmetry

Continuous orthogonal moments, for which continuous functions are used as kernel functions, are invariant to rotation and scaling, and they have been greatly developed over the recent years. Among continuous orthogonal moments, polar harmonic Fourier moments (PHFMs) have superior performance and strong image description ability. In order to improve the performance of PHFMs in noise resistance and image reconstruction, PHFMs, which can only take integer numbers, are extended to fractional-order polar harmonic Fourier moments (FrPHFMs) in this paper. Firstly, the radial polynomials of integer-order PHFMs are modified to obtain fractional-order radial polynomials, and FrPHFMs are constructed based on the fractional-order radial polynomials; subsequently, the strong reconstruction ability, orthogonality, and geometric invariance of the proposed FrPHFMs are proven; and, finally, the performance of the proposed FrPHFMs is compared with that of integer-order PHFMs, fractional-order radial harmonic Fourier moments (FrRHFMs), fractional-order polar harmonic transforms (FrPHTs), and fractional-order Zernike moments (FrZMs). The experimental results show that the FrPHFMs constructed in this paper are superior to integer-order PHFMs and other fractional-order continuous orthogonal moments in terms of performance in image reconstruction and object recognition, as well as that the proposed FrPHFMs have strong image description ability and good stability.

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The Stationary Concentrated Vortex Model

Climate ◽

10.3390/cli9030039 ◽

2021 ◽

Vol 9 (3) ◽

pp. 39

Author(s):

Oleg Onishchenko ◽

Viktor Fedun ◽

Wendell Horton ◽

Oleg Pokhotelov ◽

Natalia Astafieva ◽

...

Keyword(s):

Vortex Structure ◽

Radial Direction ◽

Symmetry Axis ◽

Outer Part ◽

Vertical Direction ◽

Axially Symmetric ◽

New Model ◽

Vortex Model ◽

Axis Of Symmetry ◽

Good Agreement

A new model of an axially-symmetric stationary concentrated vortex for an inviscid incompressible flow is presented as an exact solution of the Euler equations. In this new model, the vortex is exponentially localised, not only in the radial direction, but also in height. This new model of stationary concentrated vortex arises when the radial flow, which concentrates vorticity in a narrow column around the axis of symmetry, is balanced by vortex advection along the symmetry axis. Unlike previous models, vortex velocity, vorticity and pressure are characterised not only by a characteristic vortex radius, but also by a characteristic vortex height. The vortex structure in the radial direction has two distinct regions defined by the internal and external parts: in the inner part the vortex flow is directed upward, and in the outer part it is downward. The vortex structure in the vertical direction can be divided into the bottom and top regions. At the bottom of the vortex the flow is centripetal and at the top it is centrifugal. Furthermore, at the top of the vortex the previously ascending fluid starts to descend. It is shown that this new model of a vortex is in good agreement with the results of field observations of dust vortices in the Earth’s atmosphere.

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Chinese Image Captioning via Fuzzy Attention-based DenseNet-BiLSTM

ACM Transactions on Multimedia Computing Communications and Applications ◽

10.1145/3422668 ◽

2021 ◽

Vol 17 (1s) ◽

pp. 1-18 ◽

Cited By ~ 1

Author(s):

Huimin Lu ◽

Rui Yang ◽

Zhenrong Deng ◽

Yonglin Zhang ◽

Guangwei Gao ◽

...

Keyword(s):

Feature Extraction ◽

Contextual Information ◽

Image Features ◽

Context Information ◽

Global Information ◽

Image Description ◽

Image Captioning ◽

Image Content ◽

Single Feature ◽

Proposed Model

Chinese image description generation tasks usually have some challenges, such as single-feature extraction, lack of global information, and lack of detailed description of the image content. To address these limitations, we propose a fuzzy attention-based DenseNet-BiLSTM Chinese image captioning method in this article. In the proposed method, we first improve the densely connected network to extract features of the image at different scales and to enhance the model’s ability to capture the weak features. At the same time, a bidirectional LSTM is used as the decoder to enhance the use of context information. The introduction of an improved fuzzy attention mechanism effectively improves the problem of correspondence between image features and contextual information. We conduct experiments on the AI Challenger dataset to evaluate the performance of the model. The results show that compared with other models, our proposed model achieves higher scores in objective quantitative evaluation indicators, including BLEU , BLEU , METEOR, ROUGEl, and CIDEr. The generated description sentence can accurately express the image content.

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