Prof. Pengchang Zhang Profile

Prof. Pengchang Zhang

at XIOPM CAS

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Publications (3)

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Proceedings Article | 4 April 2023 Paper

Small sample learning of calligraphy and painting identification based on hyperspectral image

Xingjia Tang, Pengchang Zhang, Zongben Xu, Bingliang Hu, Siyuan Li

Proceedings Volume 12617, 126170Y (2023) https://doi.org/10.1117/12.2663778

KEYWORDS: Data modeling, Convolution, Hyperspectral imaging, Image enhancement, Statistical modeling, Convolutional neural networks, Image classification, Neural networks

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The existence of forgeries has seriously affected the fair trading, protection and inheritance of calligraphy and painting, while it has been unable to identify high-level counterfeiting means by traditional expert eye identification method. Combining the advantages of material attribute recognition and imaging analysis of hyperspectral imaging technology with the powerful feature expression and classification ability of convolutional neural network, the identification level of calligraphy and painting could be improved. However, there are still some practical problems in the application, like the small sample learning problem caused by the difficulty in obtaining the real hyperspectral sample data of calligraphy and painting. In this paper, a 10-hidden layers 2D-CNN convolutional neural network transfer learning method for calligraphy and painting identification with data enhancement is proposed by using a large number of relevant picture data and a small amount of MNF dimensionality reduced hyperspectral data. The experimental test shows that on the test set of this paper, for the identification of calligraphy and painting authors and authenticity, the accuracy of migration learning with data enhancement under the original sample are separately 97.5% and 94.8%, the accuracy of migration learning with data enhancement under half of the original sample are separately 94.3% and 92.8%, which shows the migration learning and data enhancement is helpful, and the identification accuracy of half of the original sample basically reaches the identification accuracy of the original sample without data enhancement and transfer learning, whose accuracy are 92.1% and 92.5%.

Proceedings Article | 24 November 2021 Paper

Three-dimensional reconstruction technique based on triangular phase shift and complementary gray code

Ruixue Gao, Pengchang Zhang, Pengchong Wang, Xi Ling

Proceedings Volume 12065, 120652E (2021) https://doi.org/10.1117/12.2606547

KEYWORDS: Phase shifts, 3D metrology, Structured light, 3D modeling, Binary data, Raster graphics, Cameras

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Structured light 3D reconstruction technology has the advantages of non-contact, low cost and high reliability, and is widely used in industrial parts defect detection, cultural relics 3D digitization and other fields. As the core part of structured light, coding method is the key element to obtain 3D scene information, which directly affects the accuracy of 3D reconstruction. The combination of gray coding and phase shifting is a typical method for 3D shape measurement. However, due to the complexity of sinusoidal fringe calculation, and the fringe pattern boundary cannot be strictly aligned, there is a step change problem, which limits the application of this method in the field of real-time high-speed three-dimensional measurement. In this paper, the phase shift method of triangular wave and gray coding are combined to measure the 3D morphology. The triangular wave function only uses two raster images and the phase information can be obtained by simple intensity ratio calculation. The binary defocus technique can shorten the projection time, but it blurs the edge boundary of gray code and aggravates the step problem of reconstruction of object surface. In order to solve this problem, the complementary gray code whose fringe width is half of the sinusoidal fringe period is used to correct the period deviation. The experimental results show that the proposed method can reduce the number of fringe projection, simplify the calculation steps and shorten the time of data processing, so it is feasible.

Proceedings Article | 9 August 2018 Paper

A study on classification of mineral pigments based on spectral angle mapper and decision tree

Cheng Fan, Pengchang Zhang, Shuang Wang, Bingliang Hu

Proceedings Volume 10806, 108065Z (2018) https://doi.org/10.1117/12.2503088

KEYWORDS: Minerals, Library classification systems, Hyperspectral imaging, Machine learning, Image classification, Spectroscopy, Distance measurement

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Mineral pigments are widely used in the ancient Chinese painting. Classification and identification of mineral pigments are important for cultural heritages conservation. As a non-destructive method, hyperspectral classification is based on the knowledge that different mineral pigments have distinct reflection spectra. This study acquired the hyperspectral images of 38 mineral pigments and established a reflection spectral library. Then Spectral Angle Mapper (SAM) was used to classify the test data and 0.20 was selected as the optimal threshold. For pigments having similar color and spectra, SAM was unable to classify them correctly. Therefore, decision tree, a machine learning method, was applied to the classification of the pigments misclassified by SAM. For each pigment, 7500 samples were randomly selected as training data and 2500 samples were selected as test data. Though the ID3 algorithm, a decision tree for pigment classification was learned. Then test data was classified by the decision tree. Compared with SAM, the accuracy of classification observed from decision tree was obviously improved. For most pigments, the accuracy of decision tree reached 94%. The results revealed that the SAM combined with decision tree could effectively achieve a discrimination of all the 38 mineral pigments in the experiment, thus providing a new approach for mineral pigments classification.

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