WHU-IIP dataset samples of face images in thermal domain (top) and

Download scientific diagram | WHU-IIP dataset samples of face images in thermal domain (top) and visible domain (bottom) and existing GAN based methods. For WHU-IIP for thermal to real visual transformation, 552 training image pairs, and 240 testing image pairs are considered in the experiments. We use 403 images for training and 156 images for testing in paired manner for Tufts Face Thermal2RGB dataset. Tufts Face thermal2RGB dataset contains more diverse data than WHU-IIP to judge the generalization capability of the proposed model. It includes images of people having various races with different facial attributes, including some people who have sunglasses and spectacles. from publication: TVA-GAN: Attention Guided Generative Adversarial Network For Thermal To Visible Face Transformations | In the recent advancement of machine learning methods for realistic image generation and image translation, Generative Adversarial Networks (GANs) play a vital role. GAN generates novel samples that look indistinguishable from the real images. The image translation using a | Visibility, Transformation and Face | ResearchGate, the professional network for scientists.

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Mean and standard error of defect detection rate.

Inspection object and location of defect.

WHU-IIP dataset samples of face images in thermal domain (top) and

WHU-IIP dataset samples of face images in thermal domain (top) and

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This figure taken from Burggraf (2015) [12] shows a lightcurve spanning

WHU-IIP dataset samples of face images in thermal domain (top) and

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This figure taken from Burggraf (2015) [12] shows a lightcurve spanning