From:  Multimodal feature extraction and fusion for determining RGP lens specification base-curve through Pentacam images

 The result of the proposed CAE model.

NumberFeaturesMLPSVR
Kernel = polyKernel = rbfKernel = linear
MSER2MSER2MSER2MSER2
1CAE (img)0.0110.5210.0200.1830.0150.3810.0170.308
2digit--0.0100.5840.0100.5540.0130.447
3area + digit0.0100.5610.0110.5320.0150.3730.0150.359
4area + img0.0110.5190.0110.5350.0140.4280.0160.320
5digit + img0.0120.5040.0170.2900.0140.4120.0120.510
6area + digit + img0.0080.6480.0090.6170.0140.4080.0130.467
7BFS/BFTE + area + digit + img0.0050.7850.0080.6410.0120.4770.0090.606

The size of 88 × 88 × 12, and applying the preprocessing module on maps of CAE and color ratio calculations, and also the use of unlabeled data in unsupervised CAE training, using sphere and ellipse radius as feature vector 4. The best results are presented in bold. BFS: best-fit sphere; BFTE: best-fit toric ellipsoid; CAE: convolutional autoencoder; MLP: multi-layered perceptron; MSE: mean squared error; SVR: support vector regression.