Prof. Xiangyang Hao Profile

Prof. Xiangyang Hao

Professor at Zhengzhou Institute of Surveying and Mapping

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Publications (8)

Proceedings Article | 14 August 2019 Paper

Multi-target tracking of surveillance video with differential YOLO and DeepSort

Xu Zhang, Xiangyang Hao, Songlin Liu, Junqiang Wang, Jiwei Xu, Jun Hu

Proceedings Volume 11179, 111792L (2019) https://doi.org/10.1117/12.2540269

KEYWORDS: Video surveillance, Video, Detection and tracking algorithms, Target detection, Surveillance, Neural networks, Sensors

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Proceedings Article | 29 August 2016 Paper

Object cutout from multiview images using level set of probabilities

Mandan Zhao, Xiangyang Hao, Chuanqi Cheng, Zhenjie Zhang

Proceedings Volume 10033, 100331D (2016) https://doi.org/10.1117/12.2245144

KEYWORDS: Image segmentation, Sensors, Clouds, Cameras, Visualization, Video, Lutetium, 3D modeling, Data modeling, Visibility

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Proceedings Article | 26 January 2016 Paper

Design and implementation of interactive strap-down inertial navigation simulation system for UAV

Chuan-qi Cheng, Xiang Cheng, Xiang-yang Hao, Man-dan Zhao

Proceedings Volume 9796, 97962V (2016) https://doi.org/10.1117/12.2230479

KEYWORDS: Unmanned aerial vehicles, Inertial navigation systems, Computer simulations, Data modeling, Differential equations, Motion models, Gyroscopes, Control systems, Data processing

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Proceedings Article | 22 June 2015 Paper

Automatic detection system of shaft part surface defect based on machine vision

Lixing Jiang, Kuoyuan Sun, Fulai Zhao, Xiangyang Hao

Proceedings Volume 9530, 95300G (2015) https://doi.org/10.1117/12.2184728

KEYWORDS: Image segmentation, Machine vision, Image processing, Defect detection, Cameras, Inspection, Imaging systems, Telecommunications, Image processing algorithms and systems, Detection and tracking algorithms

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Proceedings Article | 22 June 2015 Paper

An approach to defect inspection for packing presswork with virtual orientation points and threshold template image

Xiangyang Hao, Songlin Liu, Fulai Zhao, Lixing Jiang

Proceedings Volume 9525, 95253B (2015) https://doi.org/10.1117/12.2184628

KEYWORDS: Inspection, Image registration, Defect inspection, Image restoration, Cameras, Image acquisition, Image enhancement, Line scan image sensors, Image resolution, Defect detection

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Proceedings Article | 29 May 2013 Paper

An approach to DSM refinement with fusion of airborne lidar point cloud data and optical imagery

Xiangyang Hao, Weiqiang Zhang, Lixing Jiang

Proceedings Volume 8756, 87560H (2013) https://doi.org/10.1117/12.2014816

KEYWORDS: LIDAR, Image fusion, Cameras, Clouds, Calibration, Edge detection, Data fusion, Associative arrays, 3D image processing, Image processing

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Proceedings Article | 20 May 2013 Paper

A study on the calibration of pitch-angle deviation for airborne lidar system

Lixing Jiang, Xiangyang Hao, Weiqiang Zhang

Proceedings Volume 8731, 87310T (2013) https://doi.org/10.1117/12.2014812

KEYWORDS: Calibration, LIDAR, Clouds, Airborne laser technology, 3D metrology, Mathematical modeling, Data modeling, Imaging systems, System integration, Global Positioning System

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Airborne Lidar measurement technology, as an efficient way of acquiring three-dimensional geographic information, plays an important role in building DSM and DEM rapidly. Because the airborne Lidar measurement system usually integrates multiple devices including GPS receiver, INS, laser rangefinder and CCD camera, the relative geometric position and attitude relationships among these devices must be accurately measured in order to get the points with high precision and thereby satisfy the accuracy requirements of produced DSM and DEM. It is proved that the misalignment of airborne Lidar system, which is represented by angle deviations of yaw, pitch and roll, is the most significant source of systematic error in airborne Lidar measurement. In this paper, the effect of pitch angle error on the 3D coordinates of measured point is firstly analyzed. On this basis, a calibration method of the pitch angle deviation for airborne Lidar system by using the geometric characteristics of spire houses is put forward. The proposed pitch angle deviation calibration method consists of four key steps: (1) Initial pitch angle calculation. In the light of the offset distance between the ridge lines of the same house acquired by airborne Lidar system flying in opposite directions, an initial pitch angle deviation can be calculated. After separating the effect of pitch angle deviation, the rectified laser point cloud data are obtained. (2) Roof plane equation determination. The plane equations of both roof slopes are determined by fitting algorithms with the 3D coordinates of points located in the same spire roof. (3) Distance standard error calculation. The distance of each point to the roof plane is computed and applied to the calculation of distance standard error. (4) Final pitch angle deviation calculation. Taking the distance standard error as the overlapping criterion, the pitch angle deviation correction is iteratively calculated according to the aforesaid procedure until the distance standard error is less than a given value. The final pitch angle deviation is the sum of all the pitch angle deviation corrections. Experiments show that the proposed calibration method is correct and effective.

Proceedings Article | 28 October 2006 Paper

A spectral-restored fusion method based on wavelet-packet transform

Haitao Shan, Jianxing Guo, Shuyu Ma, Xiangyang Hao

Proceedings Volume 6419, 64192F (2006) https://doi.org/10.1117/12.714221

KEYWORDS: Image fusion, Remote sensing, Wavelets, Synthetic aperture radar, Chemical elements, Image resolution, Image quality, Wavelet transforms, Image processing, Image information entropy

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Showing 5 of 8 publications

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