It has often been conjectured that the effectiveness of line drawings can be explained by the similarity of edge images to line drawings. This article presents several problems with explaining line drawing perception in terms of edges, and how the recently proposed Realism Hypothesis resolves these problems. There is nonetheless existing evidence that edges are often the best features for predicting where people draw lines; this article describes how the Realism Hypothesis can explain this evidence.
ColeF.GolovinskiyA.LimpaecherA.BarrosH. S.FinkelsteinA.FunkhouserT.RusinkiewiczS. (2008).
Where do people draw lines?ACM Transactions on Graphics,
27, 88-1–88-11. https://doi.org/10.1145/1360612.1360687
3.
ColeF.SanikK.DeCarloD.FinkelsteinA.FunkhouserT.RusinkiewiczS.SinghM. (2009).
How well do line drawings depict shape?Transactions on Graphics,
28, 28:1–28:9. https://doi.org/10.1145/1531326.1531334
4.
DeCarloD. (2012). Depicting 3D shape using lines. In Proceedings in SPIE human vision and electronic imaging XVII, Vol. 8291. https://doi.org/10.1117/12.916463
5.
DeCarloD.FinkelsteinA.RusinkiewiczS.SantellaA. (2003).
Suggestive contours for conveying shape. ACM Transactions on Graphics,
22, 848–855. https://doi.org/10.1145/882262.882354
6.
DeCarloD.RusinkiewiczS. (2007). Highlight lines for conveying shape. In Proceedings of the 5th international symposium on non-photorealistic animation and rendering, NPAR’07, pp. 63–70. ACM. https://doi.org/10.1145/1274871.1274881
7.
DeregowskiJ. B. (1989).
Real space and represented space: Cross-cultural perspectives. Behavioral and Brain Sciences,
12, 51--119.
8.
HertzmannA. (2003).
A survey of stroke-based rendering. IEEE Computer Graphics & Applications,
23, 70--81.
JahodaG.DeregowskiJ. B., A. E.WilliamsN. (1977). Pictorial recognition as an unlearned ability: A replication with children from pictorially deprived environments. In ButterworthG. (Ed.), The child’s representation of the world.
Springer US, pp. 203–217.
LiuD.NabailM.HertzmannA.and KalogerakisE. (2020). Neural contours: Learning to draw lines from 3d shapes. In Proc CVPR, https://doi.org/10.1109/CVPR42600.2020.00547.
18.
MarrD.HildrethE. (1980).
Theory of edge detection. Proceedings of the Royal Society of London B: Biological Sciences,
207, 187–217.
19.
MorganA. T.PetroL. S.MuckliL. (2019).
Scene representations conveyed by cortical feedback to early visual cortex can be described by line drawings. Journal of Neuroscience,
39, 9410--9423.
20.
OlshausenB. A.FieldD. J. (2005).
How close are we to understanding v1?Neural Computation,
17, 1665--1699.
PearsonD. E.RobinsonJ. A. (1985).
Visual communication at very low data rates. Proceedings of the IEEE,
73, 795–812. https://doi.org/10.1109/PROC.1985.13202
WaltherD. B.ChaiB.CaddiganE.BeckD. M.Fei-FeiL. (2011).
Simple line drawings suffice for functional mri decoding of natural scene categories. Proceedings of the National Academy of Sciences,
108, 9661–9666. https://doi.org/10.1073/pnas.1015666108.
27.
YaminsD.DiCarloJ. (2016).
Using goal-driven deep learning models to understand sensory cortex. Nature Neuroscience,
19, 356–365. https://doi.org/10.1038/nn.4244
28.
YoungR. A. (1987).
The gaussian derivative model for spatial vision: I. retinal mechanisms. Spatial Vision,
2, 273–293. https://doi.org/10.1163/156856887X00222
29.
ZeilerM.FergusR. (2014). Visualizing and understanding convolutional networks. In Proceedings of ECCV. https://doi.org/10.1007/978-3-319-10590-1_53
