Mr. Xiaozheng Liu Profile

Xiaozheng Liu

at Beijing Institute of Technology

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Author

Publications (4)

Proceedings Article | 12 March 2020 Paper

An improved kernelized-correlation-filter spatial target tracking method using variable regularization and spatio-temporal context model

Yuxuan Mao, Zhijia Yang, Xiaozheng Liu, Tinghua Zhang, Kun Gao

Proceedings Volume 11438, 1143811 (2020) https://doi.org/10.1117/12.2550288

KEYWORDS: Detection and tracking algorithms, Stars, Image filtering, Signal to noise ratio, Electronic filtering, Optical tracking, Target detection, Image processing, Video, Statistical modeling

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Proceedings Article | 12 March 2020 Paper

A mosaic method for multichannel sequence starry images via multiscale edge-preserving spatio-temporal context filtering

Zhijia Yang, Xiaozheng Liu, Yuxuan Mao, Tinghua Zhang, Kun Gao

Proceedings Volume 11438, 114380Z (2020) https://doi.org/10.1117/12.2550260

KEYWORDS: Stars, Image fusion, Image filtering, Signal to noise ratio, Image processing, Image registration, Electronic filtering, Image segmentation, Image enhancement, Linear filtering

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Proceedings Article | 12 March 2020 Paper

Starry image matching method based on the description of multi-scale geometric invariant features

Xiaozheng Liu, Yuxuan Mao, Zhijia Yang, Kun Gao, Tinghua Zhang

Proceedings Volume 11438, 1143812 (2020) https://doi.org/10.1117/12.2550290

KEYWORDS: Image processing, Stars, Gaussian filters, Signal to noise ratio, Image registration, Image filtering, Image sensors, Convolution, Target acquisition, Astronomy

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In the spatial target surveillance and astronomical observation applications, image matching processing is the key procedure for the multi-temporal starry images or the multi-channel starry images acquired by different imaging sensors. However, the starry images obtained often have low Signal-to-Noise Ratios (SNR), the light intensities of the target stars or spacecrafts in them are vulnerable to background interferences, such as the atmospheric turbulence and the night clouds, etc., and become dim and instable. With the weak texture information of the target stars, all the influences make the feature point extraction quite difficult. In this paper, a new type of image matching method based on the description of Multi-scale Geometric Invariant Features (MGIF) is proposed, which uses the Rolling Guidance Filter (RGF) to perform preprocessing for the input images. By virtue of the excellent edge-preserving performance of the Joint Bilateral Filter in RGF, the integrities of contour profile of the star points are guaranteed effectively while the interference and other noise in the background are suppressed. Then the segmented and morphology methods are applied to extract star points and get the centroid of star points to form the feature point constellation. Considering the cross ratio of two lines in projection transformation model of image matching is a geometric invariant, a multi-scale geometric invariants based function, which uses the scaling of RGF as a reference to describe the relative spatial positions of matching points more accurately, is constructed to evaluate the level of similarity between star points according to the relative position of each points in the constellation. Subsequently, Random Sample Consensus（RANSAC）method is adopted to remove the mismatching star points and calculate the rigid transform matrix and other registration parameters. Digital simulation and practical processing results demonstrate that the proposed method can achieve higher matching accuracy and robustness for the starry images with low SNR and complex backgrounds.

Proceedings Article | 12 March 2020 Paper

A method of posture monitoring and falling detection based on physiological and behavioral characteristics of the elderly

Shiyun Zhou, Zhijia Yang, Yuxuan Mao, Haisong Tang, Yanchen Liu, Xiaozheng Liu, Liquan Dong

Proceedings Volume 11434, 114341I (2020) https://doi.org/10.1117/12.2550263

KEYWORDS: Detection and tracking algorithms, Sensors, Data modeling, Data acquisition, Digital filtering, Video surveillance, Image processing, Gesture recognition, Astatine, Feature extraction

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