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  • OceanRep  (2)
  • IEEE  (2)
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  • OceanRep  (2)
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  • 1
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    IEEE
    In:  [Paper] In: 2020 International Conference on Computer Vision, Image and Deep Learning, CVIDL 2020, 10.07.2020, Chongqing; China . Proceedings - 2020 International Conference on Computer Vision, Image and Deep Learning ; pp. 64-69 .
    Publication Date: 2021-01-11
    Description: Compared to traditional gas flow quantification methods, the stereo vision system has some advantages. However, underwater vision systems usually suffer from light refraction which can degrade the measurement accuracy from images. Cameras centered in spherical glass housings, dome ports, can theoretically avoid refraction, but misalignments in the dome create even more complex refraction effects than cameras behind flat glass windows. This paper introduces the spherical refraction model into a stereo vision gas flow quantification system. Also, this paper adds some contributions to an existing bubble quantification workflow for bubble size histogram and bubble volume estimation. First, the spherical glass dome port and the light propagation are modeled, and then the camera system is calibrated via underwater/in-air image pairs. Afterwards, the Epipolar Geometry Constraint is used to optimize the bubble matching. For volume estimation, an ellipsoid triangulation method is employed to improve ellipsoidal volume estimation. According to the calibration experiments and control experiments, the results show that the stereo vision gas flow quantification system can produce the volume of gas release accurately, which satisfies the requirements of long-term gas release monitoring in marine science.
    Type: Conference or Workshop Item , NonPeerReviewed
    Format: text
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  • 2
    Publication Date: 2024-02-26
    Description: Macro photography is characterized by a very shallow depth of field, which challenges classical structure from motion and even camera calibration techniques, since images suffer from large defocussed areas. Computational photography methods such as focus stacking combine the sharp areas of many photos into one, which can produce spectacular images of insects or small structures. In this contribution we analyse the camera model to describe such focus stacked images in photogrammetry and computer vision and derive a camera calibration pipeline for macro photography to enable photogrammetry and 3D reconstruction of tiny objects. We demonstrate the effectiveness of the approach on raytraced images with ground truth and real images.
    Type: Book chapter , NonPeerReviewed , info:eu-repo/semantics/bookPart
    Format: text
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