To extract a high-quality data space (the so-called kinematic invariants) is a key factor to a successful implementation of stereo-tomography. The structuretensor algorithm demonstrated itself a robust tool to pick t...
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To extract a high-quality data space (the so-called kinematic invariants) is a key factor to a successful implementation of stereo-tomography. The structuretensor algorithm demonstrated itself a robust tool to pick the kinematic invariants for stereo-tomography. However, if there are lots of diffractions and other noises in the data, it could be risky to extract the data space from the data domain. Meanwhile, for any reflector, we try to pick all the relevant primary reflections as much as possible within a wide offset range. To achieve this, in this paper, we design a scheme to extract a high-quality data space for stereo-tomography based on 3d structure tensor and kinematic de-migration. Firstly, we apply an automatic, dense volumetric picking for residual move-out (RMO) and the structural dip in the depth-migrateddomain with an advanced3d structure tensor algorithm. Then, a set of key horizons are picked manually in a few selecteddepth-migrated common offset gathers. Finally, all the picked horizons are extrapolated along the offset axis based on the RMO information picked in advance. Thus, the initial high-density points picked in the depth-migrated volume are greatly refined. After this processing, a final and refineddata space for stereo-tomography is extracted through a kinematic de-migration. We demonstrate the correctness and the robustness of the presented scheme with synthetic and real data examples.
Since the 3d structure tensor at each pixel can interpret the local between frames well, it can be used to estimate dense flow. According to the assumptions of brightness constancy, the optical flow estimation can be ...
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ISBN:
(纸本)9783319168418;9783319168401
Since the 3d structure tensor at each pixel can interpret the local between frames well, it can be used to estimate dense flow. According to the assumptions of brightness constancy, the optical flow estimation can be converted to the calculation the eigenvector of the structuretensor, rather than the complex calculation of linear system. Iterative coarse-to-fine refinement is used to improve the performance. Experimental results show that the proposed algorithm is robust and effective for computing the dense flow.
Volumetric texture synthesis is mainly used in computer graphics for texturing objects in order to increase the realism of the 3d scenario. It is also of particular interest in many application domains such as studyin...
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ISBN:
(纸本)9781538618424
Volumetric texture synthesis is mainly used in computer graphics for texturing objects in order to increase the realism of the 3d scenario. It is also of particular interest in many application domains such as studying the three-dimensional internal structure of materials and modelling volumetric data obtained by 3d imaging techniques for medical purposes. Based on a previously proposed 2dstructure/texture synthesis algorithm, this paper proposes a two-stage 3d texture synthesis approach where the volumetric structure layer of the input texture is first synthesized, then used to help the synthesis of the volumetric texture. Results show that, using the structural information helps the synthesis of the volumetric texture and can outperform the synthesis based only on intensity information.
To address multiple motions anddeformable objects' motions encountered in existing region-based approaches, an automatic video object (VO) segmentation methodology is proposed in this paper by exploiting the dual...
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To address multiple motions anddeformable objects' motions encountered in existing region-based approaches, an automatic video object (VO) segmentation methodology is proposed in this paper by exploiting the duality of image segmentation and motion estimation such that spatial and temporal information could assist each other to jointly yield much improved segmentation results. The key novelties of our method are (1) scale-adaptive tensor computation, (2) spatial-constrained motion mask generation without invoking dense motion-field computation, (3) rigidity analysis, (4) motion mask generation and selection, and (5) motion-constrained spatial region merging. Experimental results demonstrate that these novelties jointly contribute much more accurate VO segmentation both in spatial and temporal domains.
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