Mr. Zaifeng Shi Profile

Zaifeng Shi

at Tianjin Univ

SPIE Involvement:

Author

Publications (8)

SPIE Journal Paper | 16 October 2024

Object detection model with efficient feature extraction and asymptotic feature fusion for unmanned aerial vehicle image

Xiangyang Zhao, Zaifeng Shi, Yunfeng Wang, Xiaowei Niu, Tao Luo

JEI, Vol. 33, Issue 05, 053044, (October 2024) https://doi.org/10.1117/12.10.1117/1.JEI.33.5.053044

KEYWORDS: Object detection, Feature fusion, Feature extraction, Convolution, Unmanned aerial vehicles, Data modeling, Target detection, Radiofrequency catheter ablation surgery, Image fusion, Education and training

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SPIE Journal Paper | 7 November 2023

Boundary refinement network with semantic embedding connections for UAV aerial image semantic segmentation

Runzeng Li, Zaifeng Shi, Fanning Kong, Xiangyang Zhao, Tao Luo

JEI, Vol. 32, Issue 06, 063003, (November 2023) https://doi.org/10.1117/12.10.1117/1.JEI.32.6.063003

KEYWORDS: Image segmentation, Semantics, Education and training, Convolution, Feature extraction, Unmanned aerial vehicles, Image processing, Feature fusion, Image analysis, Ablation

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SPIE Journal Paper | 30 October 2020

Data association framework based on biconnected gated recurrent unit network for multiple object tracking

Zaifeng Shi, Huizheng Ren, Qingjie Cao, Boyu Fan, Qiangqiang Fan

JEI, Vol. 29, Issue 05, 053017, (October 2020) https://doi.org/10.1117/12.10.1117/1.JEI.29.5.053017

KEYWORDS: Video, Data processing, Target detection, Video surveillance, Data modeling, Convolutional neural networks, Switches, Sensors, Motion models, Machine vision

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Proceedings Article | 28 May 2019 Paper

A spatial information incorporation method for irregular sampling CT based on deep learning

Zaifeng Shi, Zhongqi Wang, Huilong Li, Jinzhuo Li, Qingjie Cao

Proceedings Volume 11072, 1107232 (2019) https://doi.org/10.1117/12.2534920

KEYWORDS: X-ray computed tomography, Image restoration, X-rays, Neural networks, CT reconstruction, Medical imaging

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Proceedings Article | 5 January 2017 Paper

Segmented-spectrum detection mechanism for medical x-ray in CdTe

Zaifeng Shi, Qingzhen Meng, Qingjie Cao, Suying Yao

Proceedings Volume 10244, 102440Z (2017) https://doi.org/10.1117/12.2264512

KEYWORDS: X-rays, X-ray detectors, Sensors, Absorption

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SPIE Journal Paper | 16 September 2016

Seam-based variable-step Bresenham blending method for real-time video mosaicking

Hongru Zhang, Zaifeng Shi, Ke Pang, Qingjie Cao, Tao Luo, Suying Yao

JEI, Vol. 25, Issue 05, 053008, (September 2016) https://doi.org/10.1117/12.10.1117/1.JEI.25.5.053008

KEYWORDS: Video, Vestigial sideband modulation, Clocks, Image quality, Commercial off the shelf technology, Field programmable gate arrays, Video surveillance, Image blending, Image resolution, Visualization

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Proceedings Article | 11 September 2013 Paper

The research of auto-focusing method for the image mosaic and fusion system with multi-sensor

Ke Pang, Suying Yao, Zaifeng Shi, Jiangtao Xu, Jiangming Liu

Proceedings Volume 8907, 890716 (2013) https://doi.org/10.1117/12.2032234

KEYWORDS: Sensors, Image fusion, Image sensors, Image processing, Digital image processing, Image analysis, Computing systems, Digital signal processing, Imaging systems, Infrared sensors

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In modern image processing, due to the development of digital image processing, the focus of the sensor can be automatically set by the digital processing system through computation. In the other hand, the auto-focusing synchronously and consistently is one of the most important factors for image mosaic and fusion processing, especially for the system with multi-sensor which are put on one line in order to gain the wide angle video information. Different images sampled by the sensors with different focal length values will always increase the complexity of the affine matrix of the image mosaic and fusion in next, which potentially reducing the efficiency of the system and consuming more power. Here, a new fast evaluation method based on the gray value variance of the image pixel is proposed to find the common focal length value for all sensors to achieve the better image sharpness. For the multi-frame pictures that are sampled from different sensors that have been adjusted and been regarded as time synchronization, the gray value variances of the adjacent pixels are determined to generate one curve. This curve is the focus measure function which describes the relationship between the image sharpness and the focal length value of the sensor. On the basis of all focus measure functions of all sensors in the image processing system, this paper uses least square method to carry out the data fitting to imitate the disperse curves and give one objective function for the multi-sensor system, and then find the optimal solution corresponding to the extreme value of the image sharpness according to the evaluation of the objective function. This optimal focal length value is the common parameter for all sensors in this system. By setting the common focal length value, in the premise of ensuring the image sharpness, the computing of the affine matrix which is the core processing of the image mosaic and fusion which stitching all those pictures into one wide angle image will be greatly simplified and the efficiency of the image processing system is significantly improved.

Proceedings Article | 6 August 2009 Paper

A real-time enhanced technique for CMOS image sensor based on dynamic area threshold

Zaifeng Shi, Chao Shi, Xiaodong Xie, Suying Yao, Qingjie Cao

Proceedings Volume 7384, 73842N (2009) https://doi.org/10.1117/12.836534

KEYWORDS: Image enhancement, Image processing, CMOS sensors, Signal processing, Image sensors, RGB color model, Curium, Evolutionary algorithms, Field programmable gate arrays, Charge-coupled devices

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Showing 5 of 8 publications

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