Occlusion: Creating Disorientation, Fugue, and Apophenia in an Art Game

Occlusion is a procedurally randomized interactive art experience which uses the motifs of repetition, isolation, incongruity and mutability to develop an experience of a Folie àDeux: a madness shared by two. It draws from traditional video game forms, development methods, and tools to situate itself in context with games as well as other forms of interactive digital media. In this way, Occlusion approaches the making of game-like media from the art criticism perspective of Materiality, and the written work accompanying the prototype discusses critical aesthetic concerns for Occlusion both as an art experience borrowing from games and as a text that can be academically understood in relation to other practices of media making. In addition to the produced software artifact and written analysis, this thesis includes primary research in the form of four interviews with artists, authors, game makers and game critics concerning Materiality and dissociative themes in game-like media. The written work first introduces Occlusion in context with other approaches to procedural remixing, Glitch Art, net.art, and analogue and digital collage and décollage, with special attention to recontextualization and apophenia. The experience, visual, and audio design approach of Occlusion is reviewed through a discussion of explicit design choices which define generative space. Development process, release process, post-release distribution, testing, and maintenance are reviewed, and the paper concludes with a description of future work and a post- mortem discussion. Included as appendices are a full specification document, script, and transcripts of all interviews

Revisiting Far/Near Infrared Pyramid-Based Fusion Types for Night Vision Using Matlab

The night vision imaging mechanisms are developed to increase the visibility beyond normal human perception capabilities. So far, night vision methods reported in literature, such as, Morphological, Low Pass Pyramid, Contrast Pyramid, Filter Subtract Decimate and Shift Invariant methods, the Laplacian fusion method has been rated, the best method [1][2]. In this research paper four different methods of fusion of images, Gradient, Wavelet, Quincuns Lifting, including Laplacian are processed using Matlab toolbox called Matifus for night vision. For comparing the results of processed images using above methods, Mean Opinion Score (MOS) is used. MOS result of Laplacian, wavelet, gradient and quincunx methods are compared. The MOS results on the scale of 1-5 indicate a score of 4.15 for Laplacian, that means the quality of image perceived by the scorers is rated between good and excellent. By using MOS and perceptually proving that Laplacian technique is better than all others for night vision systems. However Gradient scored 3.56, Wavelet scored 3.15 and lastly 2.22 was scored by Quincunx Lifting method

Context-based Information Fusion: A survey and discussion

This survey aims to provide a comprehensive status of recent and current research on context-based Information Fusion (IF) systems, tracing back the roots of the original thinking behind the development of the concept of \u201ccontext\u201d. It shows how its fortune in the distributed computing world eventually permeated in the world of IF, discussing the current strategies and techniques, and hinting possible future trends. IF processes can represent context at different levels (structural and physical constraints of the scenario, a priori known operational rules between entities and environment, dynamic relationships modelled to interpret the system output, etc.). In addition to the survey, several novel context exploitation dynamics and architectural aspects peculiar to the fusion domain are presented and discussed

PENDETEKSIAN OBJEK PADA KONDISI PENCAHAYAAN MINIM MENGGUNAKAN YOLOV7 DAN LOW-LIGHT IMAGE ENHANCEMENT

Object detection atau deteksi objek merupakan salah satu teknik yang penting dalam computer vision. Object detection merupakan suatu metode untuk mengidentifikasi objek, seperti manusia, binatang, atau kendaraan, dan letak objek-objek tersebut pada gambar digital. Salah satu tantangan pada object detection adalah pencahayaan yang minim pada gambar sehingga terjadi penurunan kualitas gambar. Untuk mengatasi permasalahan tersebut penelitian ini menggunakan dua metode low-light image enhancement untuk meningkatkan pencahayaan dari gambar, yaitu Zero-DCE dan LLFlow. Gambar yang diperoleh dari proses low-light image enhancement kemudian digunakan sebagai gambar input dari YOLOV7 untuk dilakukan proses pendeteksian objek. Dari hasil pengujian terhadap dataset ExDark, diperoleh nilai mAP@0,5 sebesar 0,785 untuk penggunaan YOLOV7 tanpa low-light image enhancement, 0,794 menggunakan Zero-DCE, dan 0,781 menggunakan LLFlow. Object detection is a vital technique in computer vision, as it involves classifying and locating various objects within digital images. One of the main challenges of object detection is lighting variation, such as low-light conditions. This paper aims to resolve challenges associated with low-light conditions by using two low-light image enhancement methods, namely Zero-DCE and LLFlow. These enhancements are used to enhance the lighting condition within the image from a low-light image to sufficient lighting condition. Images processed by low-light image enhancement are used as inputs for YOLOV7. By evaluating the trained model on the ExDark dataset, it produces mAP@0,5 values of 0,785 when YOLOV7 is used without any enhancement, 0,794 when combined with Zero-DCE, and 0,781 when combined with LLFlow

How Digital Scenography and Images Affect the Visual Spectacle in a Site-Specific Choreographic Installation

The aims of the research project were to gain a better understanding of digital scenography, mainly in the field of dance as used by recent choreographers, to create an experimental, improvisatory dance performance. This was eventually entitled Απεραντοσύνη/ Vastness, and successfully staged in a non-theatre installation space at the Attic, University of Hertfordshire, on September 16, 2016. The three main research questions are: Can a narrative, as represented by images in a projected animation, be a chorographic tool? Is it possible to combine projected animation with projected interactive motion generated images successfully for developing improvisatory dance performances in non-theatre spaces? And if so, can this combination also be a choreographic tool? The thesis of the project is that firstly, despite the apparent lack of historical precedents, it would be possible to combine scripted animations and un-scripted interactively generated graphics successfully in a dance performance project, presenting a decorative and aesthetic enhancement to the visual spectacle of the performance. Secondly, that such use could also be identified as a valuable choreographic tool for the development of improvisatory dance performances in non-theatre spaces. This dissertation analyses the historical, theoretical and practical processes of developing Απεραντοσύνη/ Vastness. It concludes that all of the questions have been given positive answers and these support the thesis