Mr. Jiabao Wang Profile

Jiabao Wang

at PLA Univ of Science and Technology

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Author

Publications (2)

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.

Proceedings Article | 10 April 2018 Paper

Deep classification hashing for person re-identification

Jiabao Wang, Yang Li, Xiancai Zhang, Zhuang Miao, Gang Tao

Proceedings Volume 10615, 106150L (2018) https://doi.org/10.1117/12.2302474

KEYWORDS: Binary data, Feature extraction, Network architectures, Image classification, Convolutional neural networks, Information technology, Surveillance

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