Automatic foreground extraction from imperfect backgrounds using multi-agent consensus equilibrium

Wang, Xiran; Juang, Jason; Chan, Stanley H.

doi:10.1016/j.jvcir.2020.102907

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Generation Of the Training Dataset1

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2022

2024

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Cited by 3 publications

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“…The selected three styles are marked in red rectangles. [41][42][43]. In these schemes, objects in a foreground of a scene are separated from their backgrounds through a series of convolutional neural nets.…”

Section: Generation Of the Training Datasetmentioning

confidence: 99%

Syntheses of Dual-Artistic Media Effects Using a Generative Model with Spatial Control

Yang

Min

2022

Electronics

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We present a generative model with spatial control to synthesize dual-artistic media effects. It generates different artistic media effects on the foreground and background of an image. In order to apply a distinct artistic media effect to a photograph, deep learning-based models require a training dataset composed of pairs of a photograph and its corresponding artwork images. To build the dataset, we apply some existing techniques that generate an artwork image including colored pencil, watercolor and abstraction from a photograph. In order to produce a dual artistic effect, we apply a semantic segmentation technique to separate the foreground and background of a photograph. Our model applies different artistic media effects on the foreground and background using space control module such as SPADE block.

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Section: Generation Of the Training Datasetmentioning

confidence: 99%