School of Electronics Engineering, Vellore Institute of Technology (VIT), Chennai 600127, Tamilnadu, India.
Sensors (Basel). 2023 Jan 17;23(3):1095. doi: 10.3390/s23031095.
Digital holographically sensed 3D data processing, which is useful for AI-based vision, is demonstrated. Three prominent methods of learning from datasets such as sensed holograms, computationally retrieved intensity and phase from holograms forming concatenated intensity-phase (whole information) images, and phase-only images (depth information) were utilized for the proposed multi-class classification and multi-output regression tasks of the chosen 3D objects in supervised learning. Each dataset comprised 2268 images obtained from the chosen eighteen 3D objects. The efficacy of our approaches was validated on experimentally generated digital holographic data then further quantified and compared using specific evaluation matrices. The machine learning classifiers had better AUC values for different classes on the holograms and whole information datasets compared to the CNN, whereas the CNN had a better performance on the phase-only image dataset compared to these classifiers. The MLP regressor was found to have a stable prediction in the test and validation sets with a fixed EV regression score of 0.00 compared to the CNN, the other regressors for holograms, and the phase-only image datasets, whereas the RF regressor showed a better performance in the validation set for the whole information dataset with a fixed EV regression score of 0.01 compared to the CNN and other regressors.
演示了基于数字全息感知的 3D 数据处理，这对于基于人工智能的视觉很有用。利用从感测全息图、从全息图计算获取的强度和相位形成串联强度-相位（全信息）图像以及相息图（深度信息）等数据集学习的三种突出方法，对所选择的 3D 对象进行了监督学习的多类分类和多输出回归任务。每个数据集由从十八个选定的 3D 对象获得的 2268 张图像组成。我们的方法在实验生成的数字全息数据上进行了验证，然后使用特定的评估矩阵进行了量化和比较。与 CNN 相比，机器学习分类器在全息图和全信息数据集上对不同类别的 AUC 值更好，而 CNN 在相息图数据集上的性能优于这些分类器。与 CNN、全息图和相息图数据集的其他回归器相比，MLP 回归器在测试集和验证集上具有稳定的预测，其固定 EV 回归分数为 0.00，而 RF 回归器在全信息数据集的验证集上具有更好的性能，其固定 EV 回归分数为 0.01。
