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Featured researches published by Bing Tu.


International Journal of Pattern Recognition and Artificial Intelligence | 2017

Sub-Pixel Level Defect Detection Based on Notch Filter and Image Registration

Longyuan Guo; ShiNan Li; Wenjing Hu; Jianhui Wu; Bing Tu; Wei He; Xianfeng Ou; Guoyun Zhang

General machine vision algorithms are difficult to detect LCD sub-pixel level defects. By studying the LCD screen images, we found that the pixels in the LCD screen are regularly arranged. The spectrum distribution of LCD images, which is obtained by the Fourier transform, is relatively consistent. According to this feature, a method of sub-pixel defect detection based on notch filter and image registration is proposed. First, we take a defect-free template image to establish registration template and notch-filtering template; then we take the defect images for image registration with registration template, and solve the offset problem. After the notch-filter template filtering the background texture, the defect is more obvious; Finally the defects are obtained by the threshold segmentation method. The experiment results show that the proposed method can detect sub-pixel defects accurately and quickly.


International Journal of Pattern Recognition and Artificial Intelligence | 2017

Temporal–Spatial Symmetric Distributed Multi-View Video Coding Scheme

Guoyun Zhang; Canqun Xiang; Xianfeng Ou; Hong Yue; Longyuan Guo; Jianhui Wu; Bing Tu; Wei He

To improve the rate stability and make a balance for different viewpoints in distributed multi-view video coding (DMVC) system, a novel symmetric DMVC (SDMVC) scheme is proposed in this paper. In the proposed scheme, every frame from all views adopts the same encoding mode and stable output rates are achieved, which are significant to improve the transmission efficiency in the channel. Both temporal and spatial correlations are exploited, in addition, a novel side information (SI) generation algorithm aiming at better exploring the correlations of proposed scheme has been proposed to obtain better performance. The simulation results show that the proposed SDMVC scheme gets a much more stable rate than the asymmetric scheme, only with neglectable bit-rate increasing. Meanwhile, the proposed SI generation algorithm significantly improves the coding performance.


pacific rim conference on multimedia | 2018

Spectral-Spatial Hyperspectral Image Classification via Adaptive Total Variation Filtering

Bing Tu; Jinping Wang; Xiaofei Zhang; Siyuan Huang; Guoyun Zhang

It is unavoidable that existing noise interference in hyperspectral image (HSI). In order to reduce the noise in HSI and obtain a higher classification result, a spectral-spatial HSI classification via adaptive total variation filtering (ATVF) is proposed in this paper, which consists of the following steps: first, the principal component analysis (PCA) method is used for dimension reduction of HSI. Then, the adaptive total variation filtering is performed on the principal components so as to reduce the sensitiveness of noise and obtain a coarse contour feature. Next, the ensemble empirical mode decomposition is used to decompose each spectrum band into serial components, the characteristics of HSI can be further integrated in a transform domain. Finally, a pixel-level classifier (such as SVM) is used for classification of the processed image. The paper analyzes the effect of different parameters of ATVF method on the classification performance in detail, tests the proposed algorithm on the real hyperspectral data sets, and finally verifies the superiority of the proposed algorithm based on a contrastive analysis of different algorithms.


Multimedia Tools and Applications | 2018

Study of multiple moving targets’ detection in fisheye video based on the moving blob model

Jianhui Wu; Feng Huang; Wenjing Hu; Wei He; Bing Tu; Longyuan Guo; Xianfeng Ou; Guoyun Zhang

This paper discussed some improved algorithms for multiple moving targets detection and tracking in fisheye video sequences which based on the moving blob model. The view field of fisheye lens achieved 183 degree which used in our system, so it has more effective in the no blind surveillance system. However, the fisheye image has a big distortion that makes it difficult to achieve an intelligent function. In this paper we try to establish a moving blob model to detect and track multiple moving targets in the fisheye video sequences, in order to achieve the automation and intelligent ability for no blind surveillance system. It is divided into three steps. Firstly, the distortion model of fisheye lens was established, we are discussing the character of the imaging principle of fisheye lens, and calculate the distortion coefficient which can be used in the moving blob model. Secondly, the principle of the moving blob model was analyzed in detail which based on the fisheye distortion model. It was included four main algorithms, which the first is the traditional algorithm of background extraction; and the background updating algorithm; the algorithm of the fisheye video sequence with the background subtracted in order to get the moving blobs; the algorithm of removing the shadow of blobs in RGB space. Thirdly, we determined that every extracted blob is a real moving target by calculating the pixels with a threshold, which can discard the faulty moving targets. Lastly, we designed the algorithm for tracking the moving targets based on the moving blobs selected through calculating the geometry center. The experiment indicated that every algorithm has a better processing efficiency of multiple moving targets in fisheye video sequences. Compared the traditional algorithm, the improved algorithm can be detected the moving target in a circular fisheye image effectively and stably.


Journal of Visual Communication and Image Representation | 2018

Adaptive total variation-based spectral-spatial feature extraction of hyperspectral image

Guoyun Zhang; Jinping Wang; Xiaofei Zhang; Hongyan Fei; Bing Tu

Abstract In this paper, a simple yet quite useful hyperspectral images (HSI) classification method based on adaptive total variation filtering (ATVF) is proposed. The proposed method consists of the following steps: First, the spectral dimension of the HSI is reduced with principal component analysis (PCA). Then, ATVF is employed to extract image features which not only reduces the noise in the image, but also effectively exploits spatial–spectral information. Therefore, it can provide an improved representation. Finally, the efficient extreme learning machine (ELM) with a very simple structure is used for classification. This paper analyzes the influence of different parameters of the ATVF and ELM algorithm on the classification performance in detail. Experiments are performed on three hyperspectral urban data sets. By comparing with other HSI classification methods and other different feature extraction methods, the proposed method based on the ATVF algorithm shows outstanding performance in terms of classification accuracy and computational efficiency when compared with other hyperspectral classification methods.


Journal of Visual Communication and Image Representation | 2018

An overview of face-related technologies

Hongyan Fei; Bing Tu; Ququ Chen; Danbing He; Chengle Zhou; Yishu Peng

Abstract In recent years, information technology is developing continuously and set off a burst of artificial intelligence boom in the field of science. The development of advanced technologies such as unmanned driving and AI chips, is the extensive application of artificial intelligence. Face-related technologies have a wide range of applications because of intuitive results and good concealment. Since 3D face information can provide more comprehensive facial information than 2D face information, and it can solve many difficulties that cannot be solved in 2D face recognition. Therefore, more and more researchers have studied 3D face recognition in recent years. Under the new circumstances, the research on face are experiencing all kinds of challenges. With the tireless of many scientists, the new technology is also making a constant progress, and in the development of many technologies it still maintained its leading position. In this paper, we simply sort out the present development process of facial correlation technology, and the general evolution of this technology is outlined. Finally, the practical significance of this technology development is briefly discussed.


IEEE Geoscience and Remote Sensing Letters | 2018

Hyperspectral Imagery Noisy Label Detection by Spectral Angle Local Outlier Factor

Bing Tu; Chengle Zhou; Wenlan Kuang; Longyuan Guo; Xianfeng Ou


IEEE Geoscience and Remote Sensing Letters | 2018

Hyperspectral Image Classification via Fusing Correlation Coefficient and Joint Sparse Representation

Bing Tu; Xiaofei Zhang; Xudong Kang; Guoyun Zhang; Jinping Wang; Jianhui Wu


Sensing and Imaging | 2018

Spectral–Spatial Hyperspectral Image Classification via Non-local Means Filtering Feature Extraction

Bing Tu; Xiaofei Zhang; Jinping Wang; Guoyun Zhang; Xianfeng Ou


Journal of Computational Methods in Sciences and Engineering | 2018

Research of image inpainting algorithm based on image segmentation

Xianfeng Ou; Pengcheng Yan; Wenjing Hu; Jianhui Wu; Bing Tu; Longyuan Guo; Xin Peng; Guoyun Zhang; Peng Wang

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Guoyun Zhang

Hunan Institute of Science and Technology

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Xianfeng Ou

Hunan Institute of Science and Technology

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Jianhui Wu

Hunan Institute of Science and Technology

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Longyuan Guo

Hunan Institute of Science and Technology

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Jinping Wang

Hunan Institute of Science and Technology

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Xiaofei Zhang

Hunan Institute of Science and Technology

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Wei He

Hunan Institute of Science and Technology

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Wenjing Hu

Hunan Institute of Science and Technology

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Siyuan Huang

Hunan Institute of Science and Technology

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