Prof. Haibo Luo Profile

Prof. Haibo Luo

at Shenyang Institute of Automation

SPIE Involvement:

Author

Publications (22)

Proceedings Article | 13 June 2024 Paper

Ship detection based on the modified YOLOV5 for SAR imagery

Zhongbo Wang, Miao He, Qinghai Ding, Haibo Luo

Proceedings Volume 13180, 1318009 (2024) https://doi.org/10.1117/12.3034316

KEYWORDS: Detection and tracking algorithms, Synthetic aperture radar, Super resolution, Network architectures, Education and training, Algorithm development, Evolutionary algorithms, Convolutional neural networks, Feature extraction, Deep learning

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Proceedings Article | 20 October 2023 Paper

Sea scene classification from synthetic aperture radar images using a modified MobileNetV1

Zhongbo Wang, Miao He, Qinghai Ding, Haibo Luo

Proceedings Volume 12916, 129161I (2023) https://doi.org/10.1117/12.3004752

KEYWORDS: Convolution, Scene classification, Synthetic aperture radar, Education and training, Convolutional neural networks, Matrices, Environmental monitoring, Spatial learning, Semantics, Real-time computing

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Proceedings Article | 27 March 2022 Paper

LPM-GAN: lumbar paraspinal muscle segmentation using a generative adversarial network

Haixing Li, Haibo Luo, Huan Wang, Chongnan Yan, Lanbo Wang, Yueming Mu, Yunpeng Liu

Proceedings Volume 12169, 121694J (2022) https://doi.org/10.1117/12.2624128

KEYWORDS: Image segmentation, Spine, Magnetic resonance imaging, Medical imaging, Convolution, Visualization

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Proceedings Article | 15 February 2022 Paper

Ship detection in infrared images based on YOLOV4 and salient regions

Panpan Zhang, Haibo Luo, Zheng Xu, Miao He

Proceedings Volume 12166, 121663N (2022) https://doi.org/10.1117/12.2616841

KEYWORDS: Image fusion, Target detection, Infrared detectors, Convolutional neural networks

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SPIE Journal Paper | 25 February 2021

Reliable part-based long-term tracking using multiple correlation filters

Hongyu Chen, Haibo Luo, Bin Hui, Zheng Chang

OE, Vol. 60, Issue 02, 023107, (February 2021) https://doi.org/10.1117/12.10.1117/1.OE.60.2.023107

KEYWORDS: Image filtering, Electronic filtering, Optical tracking, Optical engineering, Californium, Target detection, Sensors, Switching, Detection and tracking algorithms, Reliability

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Proceedings Article | 5 November 2020 Paper

Multi-scale spatio-temporal context visual tracking algorithm based on target model adaptive update

Faling Chen, Qinghai Ding, Haibo Luo, Bin Hui, Zheng Chang, Yunpeng Liu

Proceedings Volume 11567, 115670I (2020) https://doi.org/10.1117/12.2574819

KEYWORDS: Visual process modeling, Particle filters, Performance modeling, Motion models, Convolution

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Proceedings Article | 17 April 2020 Paper

Part-based long-term tracking via multiple correlation filters

Hongyu Chen, Haibo Luo, Bin Hui, Zheng Chang, Miao He

Proceedings Volume 11455, 1145574 (2020) https://doi.org/10.1117/12.2565319

KEYWORDS: Computer vision technology, Monte Carlo methods, Detection and tracking algorithms, Optical tracking

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SPIE Journal Paper | 27 May 2019

Scale adaptive part-based tracking method using multiple correlation filters

Hongyu Chen, Haibo Luo, Faling Chen, Bin Hui, Zheng Chang

OE, Vol. 58, Issue 05, 053106, (May 2019) https://doi.org/10.1117/12.10.1117/1.OE.58.5.053106

KEYWORDS: Image filtering, Electronic filtering, Detection and tracking algorithms, Digital filtering, Particle filters, Optical engineering, Monte Carlo methods, Machine vision, Computer vision technology, Californium

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Proceedings Article | 12 December 2018 Paper

Sub-blocks segmentation based on multi-feature fusion

Hongyu Chen, Haibo Luo, Zheng Chang, Bin Hui, Anbo Jiao

Proceedings Volume 10846, 108460Q (2018) https://doi.org/10.1117/12.2503909

KEYWORDS: Image segmentation, Detection and tracking algorithms, Visualization, Volume rendering, Visual process modeling, Target detection, Computer vision technology, Evolutionary algorithms

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Proceedings Article | 12 December 2018 Paper

Multi-scale kernel correlation filter for visual tracking

Faling Chen, Qinghai Ding, Haibo luo, Bin Hui, Zheng Chang, Yunpeng Liu

Proceedings Volume 10846, 108461G (2018) https://doi.org/10.1117/12.2504742

KEYWORDS: Detection and tracking algorithms, Image filtering, Machine vision, Computer vision technology, Pattern recognition, Visual process modeling

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Proceedings Article | 31 August 2018 Paper

Contour detection using an improved holistically-nested edge detection network

Zheng Xu, Haibo Luo, Bin Hui, Zheng Chang

Proceedings Volume 10835, 1083503 (2018) https://doi.org/10.1117/12.2503573

KEYWORDS: Edge detection, Network architectures, Target detection, Video, Cameras, Detection and tracking algorithms, Convolutional neural networks, Convolution

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SPIE Journal Paper | 9 November 2016

Guided filter-based fusion method for multiexposure images

Xinglin Hou, Haibo Luo, Feng Qi, Peipei Zhou

OE, Vol. 55, Issue 11, 113101, (November 2016) https://doi.org/10.1117/12.10.1117/1.OE.55.11.113101

KEYWORDS: Image fusion, Image filtering, High dynamic range imaging, Optical filters, Image processing, Cameras, Optical engineering, Image quality, Raman spectroscopy, Volume rendering

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Proceedings Article | 1 November 2016 Paper

Modification method of numerical calculation of heat flux over dome based on turbulence models

Daijun Zhang, Haibo Luo, Junchao Zhang, Xiangyue Zhang

Proceedings Volume 10157, 101573D (2016) https://doi.org/10.1117/12.2247371

KEYWORDS: Heat flux, Turbulence, Thermal modeling

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Proceedings Article | 1 November 2016 Paper

Analysis of thermal shock resistance of CVD ZnS dome

Daijun Zhang, Haibo Luo, Peipei Zhou, Xinglin Hou

Proceedings Volume 10157, 101573G (2016) https://doi.org/10.1117/12.2247400

KEYWORDS: Resistance, Chemical vapor deposition, Domes

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Proceedings Article | 8 October 2015 Paper

Tone mapping infrared images using conditional filtering-based multi-scale retinex

Haibo Luo, Lingyun Xu, Bin Hui, Zheng Chang

Proceedings Volume 9675, 96752D (2015) https://doi.org/10.1117/12.2202187

KEYWORDS: Infrared imaging, Gaussian filters, Infrared radiation, Image filtering, Imaging systems, Convolution, Optical filters, Image enhancement, High dynamic range imaging, Associative arrays

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Proceedings Article | 24 November 2014 Paper

An improved multi-scale autoconvolution transform

Chunyan Shao, Qinghai Ding, Haibo Luo

Proceedings Volume 9301, 930120 (2014) https://doi.org/10.1117/12.2072429

KEYWORDS: Pattern recognition, Image segmentation, Image processing, Machine vision, Computer vision technology, Affine motion model, Optoelectronics, Image classification, Radon transform, Convolution

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Proceedings Article | 24 November 2014 Paper

A real-time multi-scale 2D Gaussian filter based on FPGA

Haibo Luo, Xingqin Gai, Zheng Chang, Bin Hui

Proceedings Volume 9301, 930104 (2014) https://doi.org/10.1117/12.2072284

KEYWORDS: Gaussian filters, Convolution, Field programmable gate arrays, Imaging systems, Linear filtering, Sensors, Digital imaging, Detection and tracking algorithms, Channel projecting optics, Matrices

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Proceedings Article | 24 November 2014 Paper

An improved infrared image enhancement algorithm based on multi-scale decomposition

Honghui Zhang, Haibo Luo, Xin-rong Yu, Qing-hai Ding

Proceedings Volume 9301, 93010P (2014) https://doi.org/10.1117/12.2070296

KEYWORDS: Image enhancement, Image processing, Infrared imaging, Infrared radiation, Image filtering, Image contrast enhancement, Spatial filters, Optical filters, Image restoration, Spatial frequencies

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Proceedings Article | 24 November 2014 Paper

A method of plane geometry primitive presentation

Anbo Jiao, Haibo Luo, Zheng Chang, Bin Hui

Proceedings Volume 9301, 930131 (2014) https://doi.org/10.1117/12.2073114

KEYWORDS: Image segmentation, Sensors, Optoelectronics, Machine vision, Computer vision technology, Object recognition, Remote sensing, Data processing, Image understanding, Feature extraction

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Proceedings Article | 8 September 2011 Paper

A field transition particle filter tracking algorithm

De-jiang Xu, Ze-lin Shi, Xin-rong Yu, Qing-hai Ding, Hai-bo Luo

Proceedings Volume 8193, 819309 (2011) https://doi.org/10.1117/12.896426

KEYWORDS: Detection and tracking algorithms, Particle filters, Particles, Optical tracking, Machine vision, Computer vision technology, Computed tomography, Motion analysis, Surveillance, Motion estimation

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Visual tracking is a critical task in many computer vision applications such as surveillance, vehicle tracking, and motion analysis. The challenges in designing a robust visual tracking algorithm are caused by the presence of background clutter, occlusion, and illumination changes. In this paper, we propose a visual tracking algorithm in a particle filter framework to overcome these three challenging issues. Particle filter is an inference technique for estimating the unknown motion state from a noisy collection of observations, so we employ particle filter to learn the trajectory of a target. The proposed algorithm depends on the learned trajectory to predict the position of a target at a new frame, and corrects the predication by a process that can be entitled field transition. At the beginning of the tracking stage, a set of disturbance templates around the target template are accurately selected and defined as particles. During tracking, a position of the tracked target is firstly predicted based on the learned motion state, and then we take the normalized cross-correlation coefficient as a level to select the most suitable field transition parameters of the predicted position from the corresponding parameters of the particles. After judging the target is not occluded, we apply the named field transition with the selected parameters to compensate the predicted position to the accurate location of the target, meanwhile, we make use of the calculated cross-correlation coefficient as a posterior knowledge to update the weights of all the particles for the next prediction. In order to evaluate the performance of the proposed tracking algorithm, we test the approach on challenging sequences involving heavy background clutter, severe occlusions, and drastic illumination changes. Comparative experiments have demonstrated that this method makes a more significant improvement in efficiency and accuracy than two previously proposed algorithms: the mean shift tracking algorithm (MS) and the covariance tracking algorithm (CT).

Proceedings Article | 27 August 2011 Paper

A method for real-time implementation of HOG feature extraction

Hai-bo Luo, Xin-rong Yu, Hong-mei Liu, Qing-hai Ding

Proceedings Volume 8193, 819302 (2011) https://doi.org/10.1117/12.901081

KEYWORDS: Feature extraction, Target detection, Target recognition, Automatic target recognition, Machine vision, Computer vision technology, Field programmable gate arrays, Structural design, Image processing, Logic

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Proceedings Article | 15 November 2007 Paper

Difference-templates based target tracking method

Haibo Luo, Zelin Shi, Deqiang Li, Zhande Yan

Proceedings Volume 6786, 67860W (2007) https://doi.org/10.1117/12.747087

KEYWORDS: Image processing, Detection and tracking algorithms, Berkelium, Lithium, Target recognition, Pattern recognition, Machine vision, Computer vision technology, Cameras, Automatic target recognition

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Showing 5 of 22 publications

Conference Committee Involvement (1)

Conference on AI in Optics and Photonics

30 November 2020 | Beijing, China

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