Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
8959885 | Journal of Food Engineering | 2019 | 37 Pages |
Abstract
A data-driven model that predicatively generates photorealistic RGB images of dough surface browning is proposed. This model was validated in a practical application using a CO2 laser dough browning pipeline, thus confirming that it can be employed to characterize visual appearance of browned samples, such as surface color and patterns. A supervised deep generative network takes laser speed, laser energy flux, and dough moisture as an input and outputs an image (of 64Ã64 pixel size) of laser-browned dough. Image generation is achieved by nonlinearly interpolating high-dimensional training data. The proposed prediction framework contributes to the development of computer-aided design (CAD) software for food processing techniques by creating more accurate photorealistic models.
Related Topics
Physical Sciences and Engineering
Chemical Engineering
Chemical Engineering (General)
Authors
Peter Yichen Chen, Jonathan David Blutinger, Yorán Meijers, Changxi Zheng, Eitan Grinspun, Hod Lipson,