Stylization and abstraction of photographs

By D. DeCarlo, Anthony Santella. ACM Transactions on Graphics, 2002

606 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/8da5d20ad02700a764019ebf1c8ccf10191e3243

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In Computer Vision and Pattern Recognition

Citations in the corpus

Quantitative Analysis of Human-Model Agreement in Visual Saliency Modeling: A Comparative Study (2013, background): “, image rendering [9], image thumb-nailing [10], automatic collage creation [11], and dynamic lighting [12]); robotics (e.”
Image smoothing via L0 gradient minimization (2011, methodology): “Two main steps are involved in traditional methods – that is, image smoothing by mean-shift filtering [DeCarlo and Santella 2002] or bilateral filtering [Winnemöller et al. 2006], and line extraction by difference-of-Gaussian (DoG) filtering.”
XDoG: An eXtended difference-of-Gaussians compendium including advanced image stylization (2012, neutral): “Therefore, techniques employing it for artistic purpose typically perform additional processing, such as scalespace analysis [13] or curve fitting [14].”
Gaze-based interaction for semi-automatic photo cropping (2006, background): “A number of gaze-based applications and evaluation techniques use eye tracking to identify what the user thinks is important [9, 10, 30].”
Real-time video abstraction (2006, methodology): “Canny [1986] devised a more sophisticated edge detection algorithm , which found use in several related works [DeCarlo and Santella 2002; Fischer et al. 2005].”
Understanding and Predicting Image Memorability at a Large Scale (2015, background): “The last three columns show the same image modified using [5] based on the predicted memorability map: high image − regions of high memorability are emphasized while those of low memorability are de-emphasized e.”
Automatic thumbnail cropping and its effectiveness (2003, background): “In quite a different context, DeCarlo and Santella [8] tracked a user’s eye movements to determine interesting portions of images, and generated non-photorealistic, painterly images that enhanced the most salient parts of the image.”
Learning to predict where humans look (2009, methodology): “As a demonstration that our model can be used for graphics applications, we show the DeCarlo and Santella [4] abstracted nonphotoreal-istic rendering technique adapted to use our saliency model instead of eye tracking input.”
Global contrast based salient region detection (2011, positive): “Inspired by this observation, a number of non-photorealistic rendering (NPR) efforts use saliency maps to generate interesting effects [7].”
What Do Different Evaluation Metrics Tell Us About Saliency Models? (2016, background): “[54], [87], thumbnailing [55], [76], content-aware image re-targeting and cropping [3], [4], [69], [72], [85], rendering and visualization [42], [52], collage [31], [86] and artistic rendering [20], [41] require ranking (by importance or saliency) different image regions.”