Electronic Dance Music in Narrative Film

As a growing number of filmmakers are moving away from the traditional model of orchestral underscoring in favor of a more contemporary approach to film sound, electronic dance music (EDM) is playing an increasingly important role in current soundtrack practice. With a focus on two specific examples, Tom Tykwer’s Run Lola Run (1998) and Darren Aronofsky’s Pi (1998), this essay discusses the possibilities that such a distinctive aesthetics brings to filmmaking, especially with regard to audiovisual rhythm and sonic integration

Automated 3D model generation for urban environments [online]

Abstract In this thesis, we present a fast approach to automated generation of textured 3D city models with both high details at ground level and complete coverage for birds-eye view. A ground-based facade model is acquired by driving a vehicle equipped with two 2D laser scanners and a digital camera under normal traffic conditions on public roads. One scanner is mounted horizontally and is used to determine the approximate component of relative motion along the movement of the acquisition vehicle via scan matching; the obtained relative motion estimates are concatenated to form an initial path. Assuming that features such as buildings are visible from both ground-based and airborne view, this initial path is globally corrected by Monte-Carlo Localization techniques using an aerial photograph or a Digital Surface Model as a global map. The second scanner is mounted vertically and is used to capture the 3D shape of the building facades. Applying a series of automated processing steps, a texture-mapped 3D facade model is reconstructed from the vertical laser scans and the camera images. In order to obtain an airborne model containing the roof and terrain shape complementary to the facade model, a Digital Surface Model is created from airborne laser scans, then triangulated, and finally texturemapped with aerial imagery. Finally, the facade model and the airborne model are fused to one single model usable for both walk- and fly-thrus. The developed algorithms are evaluated on a large data set acquired in downtown Berkeley, and the results are shown and discussed

Visual working memory contents bias ambiguous structure from motion perception

The way we perceive the visual world depends crucially on the state of the observer. In the present study we show that what we are holding in working memory (WM) can bias the way we perceive ambiguous structure from motion stimuli. Holding in memory the percept of an unambiguously rotating sphere influenced the perceived direction of motion of an ambiguously rotating sphere presented shortly thereafter. In particular, we found a systematic difference between congruent dominance periods where the perceived direction of the ambiguous stimulus corresponded to the direction of the unambiguous one and incongruent dominance periods. Congruent dominance periods were more frequent when participants memorized the speed of the unambiguous sphere for delayed discrimination than when they performed an immediate judgment on a change in its speed. The analysis of dominance time-course showed that a sustained tendency to perceive the same direction of motion as the prior stimulus emerged only in the WM condition, whereas in the attention condition perceptual dominance dropped to chance levels at the end of the trial. The results are explained in terms of a direct involvement of early visual areas in the active representation of visual motion in WM

Locality sensitive modelling approach for object detection, tracking and segmentation in biomedical images

Biomedical imaging techniques play an important role in visualisation of e.g., biological structures, tissues, diseases and medical conditions in cellular level. The techniques bring us enormous image datasets for studying biological processes, clinical diagnosis and medical analysis. Thanks to recent advances in computer technology and hardware, automatic analysis of biomedical images becomes more feasible and popular. Although computer scientists have made a great effort in developing advanced imaging processing algorithms, many problems regarding object analysis still remain unsolved due to the diversity of biomedical imaging. In this thesis, we focus on developing object analysis solutions for two entirely different biomedical image types: uorescence microscopy sequences and endometrial histology images. In uorescence microscopy, our task is to track massive uorescent spots with similar appearances and complicated motion pattern in noisy environments over hundreds of frames. In endometrial histology, we are challenged by detecting different types of cells with similar appearance and in terms of colour and morphology. The proposed solutions utilise several novel locality sensitive models which can extract spatial or/and temporal relational features of the objects, i.e., local neighbouring objects exhibiting certain structures or patterns, for overcoming the difficulties of object analysis in uorescence microscopy and endometrial histology