Art, sex, and institutions: defining, collecting, and displaying Shunga

In Edo-Japan (c.1603 – 1868) shunga, sexually explicit prints, paintings and illustrated books, were widely produced and disseminated. However, from the 1850s onwards, shunga was suppressed by the government and it has largely been omitted from art history, excluded from exhibitions and censored in publications. Although changes have taken place, cultural institutions continue to be cautious about what they collect and exhibit, with shunga largely remaining a prohibited subject in Japan. Since the 1970s there has been a gradual increase in the acceptance of shunga outside Japan, as evidenced in the growing number of exhibitions and publications. The initial impetus behind this thesis was: Why and how did shunga become increasingly acceptable in Europe and North America in the twentieth century, whilst conversely becoming unacceptable in post-Edo Japan? I discuss how and why attitudes to shunga in the UK and Japan have changed from the Edo period to the present day, and consider how definitions can affect this. My research examines how shunga has been dealt with in relation to private and institutional collecting and exhibitions. In order to gauge modern responses, the 2013 Shunga: Sex and Pleasure in Japanese Art exhibition at the British Museum is used as an in-depth study – utilising mixed methods and an interdisciplinary approach to analyse curatorial and legal decisions, as well as visitor feedback. To-date there are no official or standardised guidelines for the acquisition, cataloguing, or display of sexually explicit artefacts. It is intended that institutions will benefit from my analysis of the changing perceptions of shunga and of previous shunga collections and exhibitions when dealing with shunga or other sexually explicit items in the future.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.