Maneuvering Contested Space and Community An Ethnographic Study of the Underground Electronic Music Scene in Itaewon

This thesis examines the dynamics of the underground electronic music scene in Itaewon, Seoul, South Korea, within the framework of contested space. Building upon the theories of Henri Lefebvre, Anthony Cohen, and Sarah Thornton, this research explores the formation of communities, spatiality, and the impact of the COVID-19 pandemic on Itaewon’s cultural landscape. Drawing from Lefebvre's reflections on spatial contestation, the study investigates how physical and symbolic spaces in Itaewon shape the experiences and interactions within the underground music scene. It delves into the significance of venues such as clubs and bars as cultural hubs, where diverse groups come together to express themselves and forge communities. Informed by Cohen's theory of community, the research sheds light on the social bonds, shared practices, and sense of belonging that emerge within the underground electronic music scene. It explores the collaborative endeavors, mutual support, and navigation of the complexities of the urban environment and the covid pandemic through stories from interlocuters and ethnography. Thornton's work on club culture provides insights into the role of music and cultural practices in shaping the experiences of individuals within the Itaewon underground club scene. It examines the intersections between music, and identity, highlighting the ambiance and social dynamics of the underground electronic music community. Furthermore, the study delves into the impact of the COVID-19 pandemic on the underground music scene in Itaewon. It delves into the myriad challenges artists, organizers, and participants confront as they navigate the constraints imposed by restrictions, strive to sustain connections, explore the quest for safe spaces, and seek out alternative pathways for creative expression. By integrating these theoretical perspectives, this thesis provides a comprehensive understanding of the underground electronic music scene in Itaewon, emphasizing its significance within the LGBTQ+ community. It illuminates the transformative power of inclusive cultural spaces, the role of music in identity formation, and the resilience of communities in the face of adversity. The findings contribute to urban anthropology and our understanding of contested spaces, cultural expressions, and the ongoing evolution of underground scenes.MasteroppgaveSANT350MASV-SAN

Automated Semantic Content Extraction from Images

In this study, an automatic semantic segmentation and object recognition methodology is implemented which bridges the semantic gap between low level features of image content and high level conceptual meaning. Semantically understanding an image is essential in modeling autonomous robots, targeting customers in marketing or reverse engineering of building information modeling in the construction industry. To achieve an understanding of a room from a single image we proposed a new object recognition framework which has four major components: segmentation, scene detection, conceptual cueing and object recognition. The new segmentation methodology developed in this research extends Felzenswalb\u27s cost function to include new surface index and depth features as well as color, texture and normal features to overcome issues of occlusion and shadowing commonly found in images. Adding depth allows capturing new features for object recognition stage to achieve high accuracy compared to the current state of the art. The goal was to develop an approach to capture and label perceptually important regions which often reflect global representation and understanding of the image. We developed a system by using contextual and common sense information for improving object recognition and scene detection, and fused the information from scene and objects to reduce the level of uncertainty. This study in addition to improving segmentation, scene detection and object recognition, can be used in applications that require physical parsing of the image into objects, surfaces and their relations. The applications include robotics, social networking, intelligence and anti-terrorism efforts, criminal investigations and security, marketing, and building information modeling in the construction industry. In this dissertation a structural framework (ontology) is developed that generates text descriptions based on understanding of objects, structures and the attributes of an image

Vision for Social Robots: Human Perception and Pose Estimation

In order to extract the underlying meaning from a scene captured from the surrounding world in a single still image, social robots will need to learn the human ability to detect different objects, understand their arrangement and relationships relative both to their own parts and to each other, and infer the dynamics under which they are evolving. Furthermore, they will need to develop and hold a notion of context to allow assigning different meanings (semantics) to the same visual configuration (syntax) of a scene. The underlying thread of this Thesis is the investigation of new ways for enabling interactions between social robots and humans, by advancing the visual perception capabilities of robots when they process images and videos in which humans are the main focus of attention. First, we analyze the general problem of scene understanding, as social robots moving through the world need to be able to interpret scenes without having been assigned a specific preset goal. Throughout this line of research, i) we observe that human actions and interactions which can be visually discriminated from an image follow a very heavy-tailed distribution; ii) we develop an algorithm that can obtain a spatial understanding of a scene by only using cues arising from the effect of perspective on a picture of a person’s face; and iii) we define a novel taxonomy of errors for the task of estimating the 2D body pose of people in images to better explain the behavior of algorithms and highlight their underlying causes of error. Second, we focus on the specific task of 3D human pose and motion estimation from monocular 2D images using weakly supervised training data, as accurately predicting human pose will open up the possibility of richer interactions between humans and social robots. We show that when 3D ground-truth data is only available in small quantities, or not at all, it is possible to leverage knowledge about the physical properties of the human body, along with additional constraints related to alternative types of supervisory signals, to learn models that can regress the full 3D pose of the human body and predict its motions from monocular 2D images. Taken in its entirety, the intent of this Thesis is to highlight the importance of, and provide novel methodologies for, social robots' ability to interpret their surrounding environment, learn in a way that is robust to low data availability, and generalize previously observed behaviors to unknown situations in a similar way to humans.</p