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Titlebook: Advances in Computer Graphics; 40th Computer Graphi Bin Sheng,Lei Bi,Daniel Thalmann Conference proceedings 2024 The Editor(s) (if applicab

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,Die Mehrheit von Schuldnern und Gläubigern,sting manifold-based hashing methods cannot capture the intrinsic structure and discriminative information of image samples. In this paper, we propose a new learning-based hashing method, namely, Sparse Graph Hashing with Spectral Regression (SGHSR), for approximate nearest neighbor search. We first
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https://doi.org/10.1007/978-3-322-85443-8ual descriptors and group collectivity as an athletic descriptor. We used these descriptors to develop a crowd performance behavior classification algorithm that can distinguish between different behaviors in large-scale performances. The study found that the descriptors were weakly correlated, indi
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https://doi.org/10.1007/978-3-322-85443-8imulation analysis, and manufacturing. When designing CAD models, engineers typically start by sketching the model’s orthographic view on paper or a whiteboard and then translate the design intent into a CAD program. Although this design method is powerful, it often involves challenging and repetiti
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Bürgerliches Recht für Betriebswirteimizing the rendering quality with a low sampling rate is an important problem for real-time ray tracing. Reservoir-based spatiotemporal importance resampling with multi-bounce global illumination (ReSTIR GI) improves the rendering quality of a low sampling rate. However, the noise introduced by Mon
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https://doi.org/10.1007/978-3-322-83633-5er, be implemented on portable FPGA devices because to their high computing, storage, and energy needs. In this paper, we propose a generic solution for image dehazing from CNN models to mobile FPGAs. The proposed solution designs lightweight network using depth-wise separable convolution and channe
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Bürgerliches Recht für Betriebswirtee critical anatomical structures, such as optic nerves and pituitary glands, exist nearby, surgeons usually rely on computer-aided surgical navigation (CSN) to provide a wide field of view in the surgical site and to allow for precise control of surgical instruments. In the CSNs, it is essential to
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Die Anfechtung von Willenserklärungenion methods to segment 3D volumes. However, the meshes extracted from such methods often suffer from undesired staircase artefacts. In this paper, we evaluate the existing mesh deformation methods that deform a template mesh to desired shapes. We evaluate two variants of such method on three dataset
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