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首页> 外文期刊>Journal of Imaging Science and Technology >Image Identification Algorithm of Deep Compensation Transformation Matrix based on Main Component Feature Dimensionality Reduction
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Image Identification Algorithm of Deep Compensation Transformation Matrix based on Main Component Feature Dimensionality Reduction

机译：基于主组件特征维数维度减少的深度补偿变换矩阵的图像识别算法

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摘要

In order to reconstruct and identify three-dimensional (3D) images, an image identification algorithm based on a deep learning compensation transformation matrix of main component feature dimensionality reduction is proposed, including line matching with point matching as the base, 3D reconstruction of point and line integration, parallelization automatic differentiation applied to bundle adjustment, parallelization positive definite matrix system solution applied to bundle adjustment, and an improved classifier based on a deep compensation transformation matrix. Based on the INRIA database, the performance and reconstruction effect of the algorithm are verified. The accuracy rate and success rate are compared with L1APG, VTD, CT, MT, etc. The results show that random transformation and re-sampling of samples during training can improve the performance of the classifier prediction algorithm under the condition that the training time is short. The reconstructed image obtained by the algorithm described in this study has a low correlation with the original image, with high number of pixels change rate (NPCR) and unified average changing intensity (UACI) values and low peak signal to noise ratio (PSNR) values. Image reconstruction effect is better with image capacity advantage. Compared with other algorithms, the proposed algorithm has certain advantages in accuracy and success rate with stable performance and good robustness. Therefore, it can be concluded that image recognition based on the dimension reduction of principal component features provides good recognition effect, which is of guiding significance for research in the image recognition field. (C) 2020 Society for Imaging Science and Technology.
机译：为了重建和识别三维（3D）图像，提出了一种基于主要分量特征维度减小的深学习补偿变换矩阵的图像识别算法，包括与点匹配作为基础，3D重建点和点的线匹配线集成，并行化自动化应用于捆扎调整，并行化正定矩阵系统解决方案施加捆扎调整，以及基于深度补偿变换矩阵的改进分类器。基于Inria数据库，验证了算法的性能和重建效果。将精度和成功率与L1APG，VTD，CT，MT等进行比较。结果表明，在训练期间样品的随机转换和再采样可以在训练时间的条件下提高分类器预测算法的性能短的。通过本研究中描述的算法获得的重建图像具有与原始图像的低相关，具有大量像素变化率（NPCR）和统一的平均变化强度（UACI）值以及低峰值信号到噪声比（PSNR）值（PSNR）值。图像重建效果更好，图像容量优势更好。与其他算法相比，所提出的算法的精度和成功率具有稳定的性能和良好的稳健性。因此，可以得出结论，基于主成分特征的尺寸降低的图像识别提供了良好的识别效应，这对于图像识别场的研究是指导意义。 （c）2020年影像科技协会。

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