Mr. Jintao Huang Profile

Jintao Huang

at IWHR

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Publications (3)

Proceedings Article | 30 June 2021 Paper

A non-contacted measurement method for the aggregate gradation

Jiabao Wang, Tianjie Lei, Jintao Huang, Cuiying Zheng

Proceedings Volume 11878, 118781U (2021) https://doi.org/10.1117/12.2601093

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Proceedings Article | 14 August 2019 Paper

Research on vehicle license plate data generation in complex environment based on generative adversarial network

Jintao Huang, Tianjie Lei, Xuemei Liu, Jing Qin, Jing Xu, Man Yuan, Jiabao Wang

Proceedings Volume 11179, 1117932 (2019) https://doi.org/10.1117/12.2539854

KEYWORDS: Gallium nitride, Data modeling, RGB color model, Image processing, Light sources and illumination, Distortion, Image quality, Detection and tracking algorithms, Convolutional neural networks, Neural networks

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Proceedings Article | 14 August 2019 Paper

Research on improved image registration algorithm based on PROSAC algorithm

Xiangyu Li, Tianjie Lei, Chun Zhao, Jintao Huang, Man Yuan, Jiabao Wang

Proceedings Volume 11179, 111792K (2019) https://doi.org/10.1117/12.2539877

KEYWORDS: Image registration, Image processing, Image quality, Data modeling

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In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (RANSAC) algorithm to optimize the matching algorithm. However, because of the randomness of the RANSAC algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional RANSAC improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.In order to solve the problems of poor stability and multiple mismatching points in image registration, most scholars have used Random Sample Consensus (RANSAC) algorithm to optimize the matching algorithm. However, because of the randomness of the RANSAC algorithm itself, the matching algorithm has poor stability, low registration efficiency and poor robustness. To solve this problem, an improved SIFT (Scale-invariant feature transform) image registration optimization algorithm based on PROSAC (Progressive Sampling Consensus) was proposed. The experimental results showed that the proposed image registration optimization algorithm could effectively solve the problems of error matching and low efficiency in the process of image matching. Using the same image to test, the average correct registration rate of the traditional RANSAC improved SIFT algorithm was 82%, and the average running time was 36 seconds. The average correct registration rate of the SIFT image registration algorithm based on PROSAC improved SIFT image registration algorithm was 86.67%, the average running time was 26.51 seconds, and the running efficiency was increased by 36%. Therefore, the improved SIFT image registration algorithm based on PROSAC has higher robustness, can meet the needs of fast image mosaic, and has broad application prospects.

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