4 resultados para swd: High Dynamic Range

em Digital Peer Publishing


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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.

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Adding virtual objects to real environments plays an important role in todays computer graphics: Typical examples are virtual furniture in a real room and virtual characters in real movies. For a believable appearance, consistent lighting of the virtual objects is required. We present an augmented reality system that displays virtual objects with consistent illumination and shadows in the image of a simple webcam. We use two high dynamic range video cameras with fisheye lenses permanently recording the environment illumination. A sampling algorithm selects a few bright parts in one of the wide angle images and the corresponding points in the second camera image. The 3D position can then be calculated using epipolar geometry. Finally, the selected point lights are used in a multi pass algorithm to draw the virtual object with shadows. To validate our approach, we compare the appearance and shadows of the synthetic objects with real objects.

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Radio Frequenzidentifikation (RFID) auf Basis pas-siver Transponder im Ultra-High-Frequenzbereich (UHF) findet in der Logistik immer häufiger Anwen-dung. Zur Ausschöpfung der Potenziale dieser AutoID-Technologie wird vorausgesetzt, dass die Identifikation der Waren und Güter zuverlässig erfolgt. Dies gestaltet sich aufgrund von Umgebungseinflüssen auf das elek-tromagnetische Lesefeld, das die passiven Transponder zur Identifikation mit Energie versorgt, oftmals sehr schwierig. Die Kenntnis der elektromagnetischen Feld-stärkeverteilung im Raum kann somit als Grundlage für die Bewertung der zuverlässigen Erfassung durch RFID-Installationen herangezogen werden. Das im Bei-trag vorgestellte Messkonzept mit Methodik zeigt eine Möglichkeit zur schnellen Erfassung der Lese-feldausprägung auf, um anhand der Ergebnisse die Kon-figuration dieser Systeme zu erleichtern.

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We present an algorithm for estimating dense image correspondences. Our versatile approach lends itself to various tasks typical for video post-processing, including image morphing, optical flow estimation, stereo rectification, disparity/depth reconstruction, and baseline adjustment. We incorporate recent advances in feature matching, energy minimization, stereo vision, and data clustering into our approach. At the core of our correspondence estimation we use Efficient Belief Propagation for energy minimization. While state-of-the-art algorithms only work on thumbnail-sized images, our novel feature downsampling scheme in combination with a simple, yet efficient data term compression, can cope with high-resolution data. The incorporation of SIFT (Scale-Invariant Feature Transform) features into data term computation further resolves matching ambiguities, making long-range correspondence estimation possible. We detect occluded areas by evaluating the correspondence symmetry, we further apply Geodesic matting to automatically determine plausible values in these regions.