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Vehicle Image Data Augmentation by GAN-based Viewpoint Transformation
Hangyel Sun, Myeonghee Lee, Charmgil Hong, Injung Kim
http://doi.org/10.5626/JOK.2021.48.8.885
We introduce a novel GAN-based image synthesis method that transforms vehicle images captured from arbitrary viewpoints into images taken from a specific viewpoint. Training a vehicle image recognizer requires a large number of vehicle images taken from a specific viewpoint. However, in practice, it is difficult to collect such training data, especially for newly released vehicles. Therefore, we propose a method of augmenting vehicle image data by converting a vehicle image from an arbitrary viewpoint into an image from a specific viewpoint. The proposed method first transforms a vehicle image from an arbitrary viewpoint to an image taken from the top-front view using DRGAN, then enhances the image quality with DeblurGAN, and finally, improves the resolution using SRGAN. The experimental results demonstrated that the proposed method successfully converted an image taken within 45 degrees left and right into an image from the top-frontal view and was effective in improving the image quality and resolution.
ChannelAug: A New Approach to Data Augmentation for Improving Image Classification Performance in Uncertain Environments
Hyeok Yoon, Soohan Kang, Ji-Hyeong Han
http://doi.org/10.5626/JOK.2020.47.6.568
We propose a new data augmentation method that works by separating the RGB channels of an image to improve image classification ability in uncertain environments. Many data augmentation methods, using technique such as flipping and cropping, have been used to improve the image classification ability of models. while these data augmentation methods have been effective in improving image classification, they have unperformed in uncertain conditions. To solve this problem, we propose the ChannelSplit that separates and reassembles the RGB channels of an image, along with the Mix ChannelSplit, that adopts the concept of MixUp[1,2] to express more diversity. In this paper, the proposed ChannelSplit and Mix ChannelSplit are called ChannelAug because they only utilize channels and do not perform any other image operations. Also, we compare ChannelAug to other image augmentation methods to prove it enhances robustness and uncertainty measures on image classification.
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