High annotation effort and the limitation to a vocabulary of known markers limit the power of such approaches. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection. Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. We study GAN performances to retrieve geometrical parameterization of surfaces. The DMD is a geometrical parameterization technique using a modal space projection to geometrically describe surfaces. Moreover, we introduce the innovative use of Discrete Modal Decomposition (DMD) to analyze network predictions. We firstly study prediction performances using different image similarity comparison algorithms. Our dataset is really small and the GAN learns to translate thermography to geometry. In this paper, we use recent success of Generative Adversarial Networks (GAN) with the pix2pix network architecture to predict the final part geometry, using only hot parts thermographic images, measured right after production.
Thus, the final part geometry must be predicted from measurements on hot parts. To guarantee the product’s quality, it is necessary to adjust the process settings in a closed loop.Those adjustments cannot rely on the final quality because a part takes days to be geometrically stable. Geometrical and appearance quality requirements set the limits of the current industrial performance in injection molding.
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