XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization
By Holger Winnemöller, Jan Eric Kyprianidis, Sven C. Olsen. 2012
256 citations recorded by the source corpus.
Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition, Generative Models
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Graphics and Computer-Aided Design
In Computer Vision and Pattern Recognition
- Ming-Hsuan Yang
- Luc Van Gool
- Trevor Darrell
- Jitendra Malik
- Dacheng Tao
- Xiaogang Wang
- Alexei A. Efros
- Alan L. Yuille
In Generative Models
Citations in the corpus
- Scribbler: Controlling Deep Image Synthesis with Sketch and Color (2016, methodology): “For each image category – face, car, or bedroom – we apply the boundary detection filter XDoG [48] on 200k photos to generate the corresponding synthetic sketches.”
- TextureGAN: Controlling Deep Image Synthesis with Texture Patches (2017, methodology): “We also apply xDoG [41] on the clothes image to obtain more variation in the training sketches.”
- Cali-Sketch: Stroke Calibration and Completion for High-Quality Face Image Generation from Poorly-Drawn Sketches (2019, methodology): “We use the XDoG edge detector [48], Photocopy effect [49] in Photoshop to generate two styles.”
- SuperCNN: A Superpixelwise Convolutional Neural Network for Salient Object Detection (2015, methodology): “Here, we compare the proposed method with the state-of-the-art method GMR (Yang et al. 2013), using XDoG (Winnemoller et al. 2012) for portrait stylization.”