2 resultados para scene change detection

em Digital Peer Publishing


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In this paper I first discuss some non-causal change constructions which have largely gone unnoticed in the literature, such as The butler bowed the guests in (which is said to code mild causation) and The supporters booed Newcastle off at the interval (which only codes temporal coextension between its two constitutive subevents). Since the same structure (i.e. the transitive object-oriented change construction) can be used to code a wide spectrum of causal and temporal relations, the question arises of what cognitive mechanisms may be involved in such meaning shifts. I argue that variation can be motivated on the basis of the figure/ground segregation which the conceptualiser can impose upon the integrated scene coded by the change construction. The integrated scene depicts a force-dynamic scenario but also evokes a unique temporal setting (i.e. temporal overlap or coextension between the constitutive subevents). Such a “bias” towards temporal overlap can be used by the conceptualiser to background causation and highlight temporal overlap interpretations. It is also shown that figure/ground segregation can be appealed to to account for the causal interpretation of intransitive change constructions, e.g. The kettle boiled dry. If the conceptual distance between the verbal event and the non-verbal event is (relatively) great, causality can be highlighted even in intransitive patterns.

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Exposure Fusion and other HDR techniques generate well-exposed images from a bracketed image sequence while reproducing a large dynamic range that far exceeds the dynamic range of a single exposure. Common to all these techniques is the problem that the smallest movements in the captured images generate artefacts (ghosting) that dramatically affect the quality of the final images. This limits the use of HDR and Exposure Fusion techniques because common scenes of interest are usually dynamic. We present a method that adapts Exposure Fusion, as well as standard HDR techniques, to allow for dynamic scene without introducing artefacts. Our method detects clusters of moving pixels within a bracketed exposure sequence with simple binary operations. We show that the proposed technique is able to deal with a large amount of movement in the scene and different movement configurations. The result is a ghost-free and highly detailed exposure fused image at a low computational cost.