Mr. Pengcheng Wang Profile

Pengcheng Wang

at Nanjing Univ of Science and Technology

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Publications (6)

SPIE Journal Paper | 10 March 2017

Salient motion detection using proximal robust principal component analysis method

Pengcheng Wang, Qian Chen, Weixian Qian, Kan Ren, Zheyi Yao, Fuyuan Xu

JEI, Vol. 26, Issue 02, 023004, (March 2017) https://doi.org/10.1117/12.10.1117/1.JEI.26.2.023004

KEYWORDS: Motion detection, Principal component analysis, Image segmentation, Bromine, Motion models, Video, Machine vision, Signal to noise ratio, Detection and tracking algorithms, Computer vision technology

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Proceedings Article | 28 September 2016 Paper

Moving object detection via low-rank total variation regularization

Pengcheng Wang, Qian Chen, Na Shao

Proceedings Volume 9971, 997132 (2016) https://doi.org/10.1117/12.2236146

KEYWORDS: Video surveillance, Principal component analysis, Motion models, Cameras, MATLAB, Motion detection, Video, Optical tracking, Machine vision, Computer vision technology

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Proceedings Article | 15 October 2015 Paper

The optimal extraction of feature algorithm based on KAZE

Zheyi Yao, Guohua Gu, Weixian Qian, Pengcheng Wang

Proceedings Volume 9674, 967438 (2015) https://doi.org/10.1117/12.2204977

KEYWORDS: Feature extraction, Diffusion, Image processing, Principal component analysis, Detection and tracking algorithms, Nonlinear filtering, Adaptive optics, Wavelets, Wavelet transforms, Image compression

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Proceedings Article | 8 October 2015 Paper

Constant false alarm rate algorithm for the dim-small target detection based on the distribution characteristics of target coordinates

Xiao-Liang Fei, Kan Ren, Wei-xian Qian, Peng-cheng Wang

Proceedings Volume 9675, 96750R (2015) https://doi.org/10.1117/12.2197891

KEYWORDS: Linear filtering, Target detection, Detection and tracking algorithms, Image segmentation, Infrared radiation, Infrared imaging, Infrared detectors, Infrared technology, Image processing algorithms and systems, Infrared search and track

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CFAR (Constant False Alarm Rate) is a key technology in Infrared dim-small target detection system. Because the traditional constant false alarm rate detection algorithm gets the probability density distribution which is based on the pixel information of each area in the whole image and calculates the target segmentation threshold of each area by formula of Constant false alarm rate, the problems including the difficulty of probability distribution statistics and large amount of algorithm calculation and long delay time are existing. In order to solve the above problems effectively, a formula of Constant false alarm rate based on target coordinates distribution is presented. Firstly, this paper proposes a new formula of Constant false alarm rate by improving the traditional formula of Constant false alarm rate based on the single grayscale distribution which objective statistical distribution features are introduced. So the control of false alarm according to the target distribution information is implemented more accurately and the problem of high false alarm that is caused of the complex background in local area as the cloud reflection and the ground clutter interference is solved. At the same time, in order to reduce the amount of algorithm calculation and improve the real-time characteristics of algorithm, this paper divides the constant false-alarm statistical area through two-dimensional probability density distribution of target number adaptively which is different from the general identifying methods of constant false-alarm statistical area. Finally, the target segmentation threshold of next frame is calculated by iteration based on the function of target distribution probability density in image sequence which can achieve the purpose of controlling the false alarm until the false alarm is down to the upper limit. The experiment results show that the proposed method can significantly improve the operation time and meet the real-time requirements on condition of keeping the target detection performance.

Proceedings Article | 8 October 2015 Paper

Fast randomized Hough transformation track initiation algorithm based on multi-scale clustering

Minjie Wan, Guohua Gu, Qian Chen, Weixian Qian, Pengcheng Wang

Proceedings Volume 9675, 96750S (2015) https://doi.org/10.1117/12.2197916

KEYWORDS: 3D image processing, Detection and tracking algorithms, Automatic tracking, 3D modeling, Image processing, Motion models, Distortion, Signal to noise ratio, Infrared search and track, Associative arrays

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A fast randomized Hough transformation track initiation algorithm based on multi-scale clustering is proposed to overcome existing problems in traditional infrared search and track system(IRST) which cannot provide movement information of the initial target and select the threshold value of correlation automatically by a two-dimensional track association algorithm based on bearing-only information . Movements of all the targets are presumed to be uniform rectilinear motion throughout this new algorithm. Concepts of space random sampling, parameter space dynamic linking table and convergent mapping of image to parameter space are developed on the basis of fast randomized Hough transformation. Considering the phenomenon of peak value clustering due to shortcomings of peak detection itself which is built on threshold value method, accuracy can only be ensured on condition that parameter space has an obvious peak value. A multi-scale idea is added to the above-mentioned algorithm. Firstly, a primary association is conducted to select several alternative tracks by a low-threshold .Then, alternative tracks are processed by multi-scale clustering methods , through which accurate numbers and parameters of tracks are figured out automatically by means of transforming scale parameters. The first three frames are processed by this algorithm in order to get the first three targets of the track , and then two slightly different gate radius are worked out , mean value of which is used to be the global threshold value of correlation. Moreover, a new model for curvilinear equation correction is applied to the above-mentioned track initiation algorithm for purpose of solving the problem of shape distortion when a space three-dimensional curve is mapped to a two-dimensional bearing-only space. Using sideways-flying, launch and landing as examples to build models and simulate, the application of the proposed approach in simulation proves its effectiveness , accuracy , and adaptivity of correlation threshold selection.

Proceedings Article | 11 September 2013 Paper

A new affine-invariant image matching method based on SIFT

Peng-cheng Wang, Qian Chen, Hai-xin Chen, Hong-chang Cheng, Zhen-fei Gong

Proceedings Volume 8907, 89072D (2013) https://doi.org/10.1117/12.2033014

KEYWORDS: Feature extraction, Principal component analysis, Detection and tracking algorithms, Anisotropy, Machine vision, Computer vision technology, Cameras, Image registration, Image filtering, Target recognition

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Local invariant feature extraction, as one of the main problems in the field of computer vision, has been widely applied to image matching, splicing and target recognition etc. Lowe’s scale invariant feature transform (known as SIFT) algorithm has attracted much attention due to its invariance to scale, rotation and illumination. However, SIFT is not robust to affine deformations, because it is based on the DoG detector which extracts keypoints in a circle region. Besides, the feature descriptor is represented by a 128-dimensional vector, which means that the algorithm complexity is extremely large especially when there is a great quantity of keypoints in the image. In this paper, a new feature descriptor, which is robust to affine deformations, is proposed. Considering that circles turn to be ellipses after affine deformations, some improvements have been made. Firstly, the Gaussian image pyramids are constructed by convoluting the source image and the elliptical Gaussian kernel with two volatile parameters, orientation and eccentricity. In addition, the two parameters are discretely selected in order to imitate the possibilities of the affine deformation, which can make sure that anisotropic regions are transformed into isotropic ones. Next, all extreme points can be extracted as the candidates for the affine-invariant keypoints in the image pyramids. After accurate keypoints localization is performed, the secondary moment of the keypoints’ neighborhood is calculated to identify the elliptical region which is affineinvariant, the same as SIFT, the main orientation of the keypoints can be determined and the feature descriptor is generated based on the histogram constructed in this region. At last, the PCA method for the 128-dimensional descriptor’s reduction is used to improve the computer calculating efficiency. The experiments show that this new algorithm inherits all SIFT’s original advantages, and has a good resistance to affine deformations; what’s more, it is more effective in calculation and storage requirement.

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