Data description & pre-processing :
Label preserving transformations on images have been shown to be effective in training deep neural networks in \citealt{krizhevsky2012imagenet} by creating a larger transformed training dataset. We use this approach to generate rotated images as described below :
- Each image is first augmented by introducing a rotation angle between[ -60, 60] in increments of 5 degrees.
- A total of 25 (1 original + 24 augmented) training images were created from each image resulting in a training sample size of 2100 images.
- Each of the images were normalized to the range [0,1] and subject to local contrast enhancement using adaptive histogram equalization\citealt{Pizer:1987:AHE:29040.29046} as implemented in scikit-image\citealt{van_der_Walt_2014} Python library.
- Accordingly, the ground truth value for each of the images was updated to include the rotation angle applied.
- Transformed version of an ideal sample image is shown in Figure-\ref{852490} .