Dr. Xiangsuo Fan Profile

Dr. Xiangsuo Fan

at Institute of Optics and Electronics CAS

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Publications (4)

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Proceedings Article | 18 December 2019 Paper

Dim small targets detection based on statistical block low-rank background modeling

Biao Li, Zhiyong Xu, Jianlin Zhang, Xiangsuo Fan

Proceedings Volume 11338, 113382J (2019) https://doi.org/10.1117/12.2547630

KEYWORDS: Target detection, Detection and tracking algorithms, Digital filtering, Signal to noise ratio, Reconstruction algorithms, Statistical modeling, Image processing

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How to effectively detect weak targets from complex background is always a challenging problem and is a meaningful research subject with practical significance. In this paper, the complex video frame images are considered as a spatial random process, and the stationarity and low-rank characteristics of different components of the image are related to theirs statistical characteristics. According to this view, a statistical block low-rank background modeling algorithm (for short: SBLR) is proposed. This paper first analyzes the regional statistical characteristics of the image, and then uses k-mean statistical clustering algorithm to divide the image into statistical blocks to obtain the statistical block images. Then, the characteristics of each component of the statistical block image are analyzed to establish a model composed of statistical block low rank background and sparse components. Next, according to the characteristics of each component of the model, the solution scheme of principal component analysis is adopted, and the specific solution algorithm is given. Finally, the background reconstruction experiment according to SBLR algorithm and target detection experiment are carried out. Experiments show that the algorithm proposed in this paper achieves good accuracy in the background reconstruction of complex scenes, the background is significantly suppressed, the target is significantly enhanced, and the target detection rate is high.

Proceedings Article | 24 October 2017 Paper

Infrared dim and small target background suppression based on improved anisotropy filtering

Xiangsuo Fan, Zhiyong Xu, Jianlin Zhang, Yongmei Huang, Zhenming Peng

Proceedings Volume 10462, 104622M (2017) https://doi.org/10.1117/12.2284888

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Infrared small target is easy submerged in the complex background, to improve the ability of detecting target, which by inhibiting the background to enhance the target signal. Focusing on the shortcomings of the isotropic background prediction method, a kind of improved anisotropic infrared background prediction method (IABP) is proposed. According to differences of local gradient character among target region, smooth background region and undulate background region, the edge stopping function of anisotropic partial differential equation is improved. Then the mean of the two least values of the edge as the prediction value of the background. Finally in order to extract the candidate target and reduce the false alarm rate of the real target, which the difference between the background image and the original image is processed. Experimental results show that: 1) improved anisotropy of background prediction for different scenes can obtain good background prediction effect; 2) improved anisotropic background predication for the signal-noise ratio (SNR) was lower than 2.3db could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods.

Proceedings Article | 24 October 2017 Paper

Immune particle filter algorithm for target tracking based on histograms of color and oriented gradient

Linshun Luo, Xiangsuo Fan

Proceedings Volume 10462, 104622Q (2017) https://doi.org/10.1117/12.2284926

KEYWORDS: Particle filters, Detection and tracking algorithms

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Focusing on fails to track with standard particle filters when object and background have similar color and object is occluded, a new algorithm of immune particle filter object tracking based on histograms of color and oriented gradient is proposed. Color histogram is the global description of targets in color image, histogram of oriented gradient contains some construction information. The gradient feature is added, the poor performance of only one feature is improved when the environment changes. In addition, the proposed immune optimization algorithm improved the diversity of particles, especially the decline of the impact of sample impoverishment when occlusion occurs. Experimental results show that the proposed method is able to track the object stably, when one of the features loses discrimination ability for tracking. It is simple and suitable to be applied to deal with tracking problems in complex scene.

Proceedings Article | 1 November 2016 Paper

An enhancement algorithm for dim and small infrared target based on time and spatial correlation

Xiangsuo Fan, Zhiyong Xu, Jianlin Zhang

Proceedings Volume 10157, 101572S (2016) https://doi.org/10.1117/12.2247052

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For the single frame image enhancement, the enhancement of weak small targets has some limitation with only using partial information of target both in time domain and the spatial domain. The spatial domain processing methods focus on the gray feature of image target, its shortcoming is to ignore the gray continuity of the target in the time domain. However, the time domain processing methods take into account the gray continuity of the target in the time domain, but ignoring the gray intensity distribution, which will result in recognizing too many false targets with the interference of noise. The purpose of the spatial domain processing and time domain processing is to enhance target on the different basis. The spatial domain processing has a focus on the spatial characteristics of targets; another has a focus on the time movement characteristics of the target. Therefore, the energy enhancement method of multiple frames target combined with the time domain and spatial domain attract more and more attention. In this paper, according to the different feature of the target and the background and noise in space domain and time domain, the joint probability distribution is adopted to integrate gray image obtained by the two processing methods. Studies show that the average grey value and SNR gain of target enhance effectively after enhancement. At the same time, due to the time domain and spatial domain processing independent of each other, parallel processing method can be used in order to improve the speed of processing and greatly shorten the operation time.

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