The conventional two-dimensional (2D) digital image correlation (DIC) method often fails to work when there is a large rotation between the reference and deformed images. The objective of this study is to propose a 2D DIC method to tackle this issue and calculate the full-field rotation angle. In our method, the deformed subset is chosen along the direction of gradient orientation angle error (GOAE) between the pseudo point of interest and the searching point. In addition, the initial deformation parameters for the inverse compositional Gaussian–Newton (IC-GN) method are set based on the GOAE to guarantee its convergence. Furthermore, a best fit method is presented to calculate the rotation angle of each target deformed subset using the converged deformation parameters from IC-GN method. Moreover, due to the high computation intensity of the proposed method, a multi-thread parallel computation technique is used to speed up its computation. Simulation and bending experiment results show that the proposed method can measure both the full-field in-plane displacement and rotation angle with relatively high accuracy and efficiency in the case of a large rotation, in comparison with the conventional DIC method and a ring template-based method. The proposed method is also robust to the strained deformation and image noise to some degree.
Chinese herbal oral liquid can leach a variety of effective ingredients from herbs and has become a major drug for clinical application. However, it is easy to produce or introduce foreign matters that are very faint in the automatic filling production process. To solve the challenge of low accuracy of faint foreign matter detection, in this paper, we proposed a salient-based anomaly detection method which is fuses visual saliency with dual-spectral saliency (VDS) for the hyperspectral herbal oral liquid. Specifically, we first select the most discriminative bands via the band selection method to generate the pseudo-color map. Subsequently, the histogram-based contrast method is introduced to select the saliency feature map with the largest variance of color features, while fusing the multi-scale gradient features to obtain the preliminary vision-based anomaly detection map. After that, the spectral angles and spectral Euclidean distances are calculated separately based on the oral liquid hyperspectral images to fused into dual-spectral saliency maps. Finally, the dual-spectral saliency map is employed to suppress the background information of the preliminary anomaly detection map. The experimental results show that our proposed method outperforms the state-of-the-art anomaly detection methods, which accurately and quickly achieve the detection of faint foreign matter in the hyperspectral herbal oral liquid. It will accelerate the process of automated filling production lines for oral liquid in the pharmaceutical industry.
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