Histograms of oriented mosaic gradients for snapshot spectral image description

A Fast Non-destructive Algorithm for Image Description Based on Improved Wavelet Moment Features

ACTA AUTOMATICA SINICA ◽

10.3724/sp.j.1004.2009.01278 ◽

2009 ◽

Vol 35 (10) ◽

pp. 1278-1282

Author(s):

Jia-Min LIU ◽

Hai-Jun XIE ◽

Qiang LIU ◽

Sheng-Jun ZHU ◽

Wei ZHANG

Keyword(s):

Image Description ◽

Moment Features ◽

Non Destructive

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Image Inpainting of Multi-Spectral Image with Laser Lines Based on Generative Adversarial Network

Journal of Physics Conference Series ◽

10.1088/1742-6596/1880/1/012011 ◽

2021 ◽

Vol 1880 (1) ◽

pp. 012011

Author(s):

L Lu ◽

G Zhong ◽

J Dong

Keyword(s):

Image Inpainting ◽

Spectral Image ◽

Generative Adversarial Network ◽

Adversarial Network

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A Cross-Spectral Image Fusion Based Visual Odometry

2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) ◽

10.1109/yac51587.2020.9337596 ◽

2020 ◽

Author(s):

Yunfei Zhou ◽

Kang Li

Keyword(s):

Image Fusion ◽

Visual Odometry ◽

Spectral Image

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Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments

Sensors ◽

10.3390/s21041544 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1544

Author(s):

Chunpeng Wang ◽

Hongling Gao ◽

Meihong Yang ◽

Jian Li ◽

Bin Ma ◽

...

Keyword(s):

Image Reconstruction ◽

Fractional Order ◽

Continuous Functions ◽

Kernel Functions ◽

Superior Performance ◽

Image Description ◽

Orthogonal Moments ◽

Integer Order ◽

Geometric Invariance ◽

Order Continuous

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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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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