Defective point data reconstruction based on improved process of morphological operations

Yin, Zhouping; Kun, M.; Xiong, Yan

doi:10.1007/s11431-011-4604-9

Cited by 4 publications

(2 citation statements)

References 40 publications

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“…Remove the shadow of the moving vehicle based on the detected shadows area and obtain accurate vehicle detection. en, use morphology operation [45] to remove the road area. Finally, obtain complete and accurate detection area of the moving vehicle by filling the "cavity" via close operation [46].…”

Section: Shadow Removalmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Sun

Zhang

et al. 2020

Complexity

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Video-based moving vehicle detection and tracking is an important prerequisite for vehicle counting under complex transportation environments. However, in the complex natural scene, the conventional optical flow method cannot accurately detect the boundary of the moving vehicle due to the generation of the shadow. In addition, traditional vehicle tracking algorithms are often occluded by trees, buildings, etc., and particle filters are also susceptible to particle degradation. To solve this problem, this paper proposes a kind of moving vehicle detection and tracking based on the optical flow method and immune particle filter algorithm. The proposed method firstly uses the optical flow method to roughly detect the moving vehicle and then uses the shadow detection algorithm based on the HSV color space to mark the shadow position after threshold segmentation and further combines the region-labeling algorithm to realize the shadow removal and accurately detect the moving vehicle. Improved affinity calculation and mutation function of antibody are proposed to make the particle filter algorithm have certain adaptivity and robustness to scene interference. Experiments are carried out in complex traffic scenes with shadow and occlusion interference. The experimental results show that the proposed algorithm can well solve the interference of shadow and occlusion and realize accurate detection and robust tracking of moving vehicles under complex transportation environments, which has the potentiality to be processed on a cloud computing platform.

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Section: Shadow Removalmentioning

confidence: 99%

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Sun

Zhang

et al. 2020

Complexity

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show abstract

“…Under operation, the components' faults degrade the machining accuracy [6][7][8][9][10][11]. It is difficult to detect fault feature, especially weak fault feature, which is usually submerged in the noise because the structures of combined machining centers are complex.…”

Section: Introductionmentioning

confidence: 99%

Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance

2011

Sci. China Technol. Sci.

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The difficulty to select the best system parameters restricts the engineering application of stochastic resonance (SR). An adaptive cascade stochastic resonance (ACSR) is proposed in the present study. The proposed method introduces correlation theory into SR, and uses correlation coefficient of the input signals and noise as a weight to construct the weighted signal-to-noise ratio (WSNR) index. The influence of high frequency noise is alleviated and the signal-to-noise ratio index used in traditional SR is improved accordingly. The ACSR with WSNR can obtain optimal parameters adaptively. And it is not necessary to predict the exact frequency of the target signal. In addition, through the secondary utilization of noise, ACSR makes the signal output waveform smoother and the fluctuation period more obvious. Simulation example and engineering application of gearbox fault diagnosis demonstrate the effectiveness and feasibility of the proposed method. stochastic resonance, adaptive, weighted signal-to-noise ratio, feature enhancement, combined machining center Citation:Li B, Li J M, He Z J. Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance.

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Moving Vehicle Detection Based on Optical Flow Method and Shadow Removal

Sun

Zhang

et al. 2020

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Defective point data reconstruction based on improved process of morphological operations

Cited by 4 publications

References 40 publications

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Moving Vehicle Detection and Tracking Based on Optical Flow Method and Immune Particle Filter under Complex Transportation Environments

Fault feature enhancement of gearbox in combined machining center by using adaptive cascade stochastic resonance

Moving Vehicle Detection Based on Optical Flow Method and Shadow Removal

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