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      Annotating shadows, highlights and faces: the contribution of a 'human in the loop' for digital art history

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          Abstract

          While automatic computational techniques appear to reveal novel insights in digital art history, a complementary approach seems to get less attention: that of human annotation. We argue and exemplify that a 'human in the loop' can reveal insights that may be difficult to detect automatically. Specifically, we focussed on perceptual aspects within pictorial art. Using rather simple annotation tasks (e.g. delineate human lengths, indicate highlights and classify gaze direction) we could both replicate earlier findings and reveal novel insights into pictorial conventions. We found that Canaletto depicted human figures in rather accurate perspective, varied viewpoint elevation between approximately 3 and 9 meters and highly preferred light directions parallel to the projection plane. Furthermore, we found that taking the averaged images of leftward looking faces reveals a woman, and for rightward looking faces showed a male, confirming earlier accounts on lateral gender bias in pictorial art. Lastly, we confirmed and refined the well-known light-from-the-left bias. Together, the annotations, analyses and results exemplify how human annotation can contribute and complement to technical and digital art history.

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          Most cited references 6

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          Ill-posed problems in early vision

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            Experience can change the 'light-from-above' prior.

            To interpret complex and ambiguous input, the human visual system uses prior knowledge or assumptions about the world. We show that the 'light-from-above' prior, used to extract information about shape from shading is modified in response to active experience with the scene. The resultant adaptation is not specific to the learned scene but generalizes to a different task, demonstrating that priors are constantly adapted by interactive experience with the environment.
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              Specular reflections and the perception of shape.

              Many materials, including leaves, water, plastic, and chrome exhibit specular reflections. It seems reasonable that the visual system can somehow exploit specular reflections to recover three-dimensional (3D) shape. Previous studies (e.g., J. T. Todd & E. Mingolla, 1983; J. F. Norman, J. T. Todd, & G. A. Orban, 2004) have shown that specular reflections aid shape estimation, but the relevant image information has not yet been isolated. Here we explain how specular reflections can provide reliable and accurate constraints on 3D shape. We argue that the visual system can treat specularities somewhat like textures, by using the systematic patterns of distortion across the image of a specular surface to recover 3D shape. However, there is a crucial difference between textures and specularities: In the case of textures, the image compressions depend on the first derivative of the surface depth (i.e., surface orientation), whereas in the case of specularities, the image compressions depend on the second derivative (i.e., surfaces curvatures). We suggest that this difference provides a cue that can help the visual system distinguish between textures and specularities, even when present simultaneously. More importantly, we show that the dependency of specular distortions on the second derivative of the surface leads to distinctive fields of image orientation as the reflected world is warped across the surface. We find that these "orientation fields" are (i) diagnostic of 3D shape, (ii) remain surprisingly stable when the world reflected in the surface is changed, and (iii) can be extracted from the image by populations of simple oriented filters. Thus the use of specular reflections for 3D shape perception is both easier and more reliable than previous computational work would suggest.
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                Author and article information

                Journal
                10 September 2018
                Article
                1809.03539

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Presented at the "1st KDD Workshop on Data Science for Digital Art History: tackling big data Challenges, Algorithms, and Systems", see http://dsdah2018.blogs.dsv.su.se for more info. Manuscript should eventually be published in Journal of Digital Art History (www.dah-journal.org/)
                cs.CV cs.HC

                Computer vision & Pattern recognition, Human-computer-interaction

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