As-rigid-as-possible shape deformation and interpolation

Guo, He; Fu, Xinyuan; Chen, Feng; Yang, Hongji; Wang, Yuxin; Han, Li

doi:10.1016/j.jvcir.2008.01.003

Cited by 16 publications

(5 citation statements)

References 20 publications

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“…An as-rigid-as-possible (ARAP) shape-deformation algorithm is the key computation approach used in the present study for the deformation of the MRCPs. The ARAP algorithm has been widely used for realistic shape deformation or interpolation in various applications including 3D animation (Alexa et al 2000, Igarashi et al 2005, Guo et al 2008, Yan et al 2008, Levi and Gotsman 2015. The ARAP algorithm is a mathematical approach for shape deformation, i.e.…”

Section: As-rigid-as-possible Shape-deformation Algorithmmentioning

confidence: 99%

“…In the present study, we deformed the MRCPs into five non-standing postures (i.e. walking, sitting, bending, kneeling, and squatting) by developing and using a systematic posture-change method based on an as-rigidas-possible (ARAP) shape-deformation algorithm (Yan et al 2008) and motion-capture technology, which are widely used in various applications including 3D animation and virtual reality (Alexa et al 2000, Starck and Hilton 2007, Guo et al 2008, Chan et al 2011. The deformed MRCPs were then used to produce a comprehensive DC dataset for photon external exposures by performing Monte Carlo dose calculations with the Geant4 code (Alliso n et al 2016).…”

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confidence: 99%

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Posture-dependent dose coefficients of mesh-type ICRP reference computational phantoms for photon external exposures

et al. 2019

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Recently, the International Commission on Radiological Protection (ICRP) developed new mesh-type reference computational phantoms (MRCPs) that provide high deformability compared with the current voxel-type reference computational phantoms of ICRP Publication 110. Taking advantage of this deformability, in the present study, the MRCPs were deformed to five non-standing postures (i.e. walking, sitting, bending, kneeling, and squatting) by developing and using a systematic posture-change method based on the as-rigid-as-possible (ARAP) shape-deformation algorithm and motion-capture technology. The non-standing MRCPs were then implemented in the Geant4 Monte Carlo code to calculate a comprehensive dataset of dose coefficients (DCs) for photon external exposures. These include the dose coefficients for 29 individual organs/tissues and the dose coefficients for effective doses from 0.01 MeV to 10 GeV in the antero-posterior (AP), postero-anterior (PA), left-lateral (LLAT), right-lateral (RLAT), rotational (ROT), and isotropic (ISO) geometries. To investigate the dosimetric impact of posture, the DCs of the non-standing MRCPs were compared with those of the original MRCPs (in the standing posture). The results showed that organ/tissue doses are significantly influenced by posture, with arm position mostly influencing dose to organs/tissues in the torso region and leg position influencing dose in the pelvic region. For most cases, the gonads showed notably large differences, ranging from a few tens of percentage points to several orders of magnitude, depending on posture and irradiation geometry. The effective doses showed much smaller differences than the organ/tissue doses, but they were nonetheless significant: for example, the kneeling MRCPs in the AP geometry showed lower values at energies <10 MeV by up to 30% and greater values at higher energies by up to 40%. The presented results indicate that not only different irradiation geometries, but also different postures might be necessary in DC calculations for reliable dose estimates for radiological protection purposes.

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Section: As-rigid-as-possible Shape-deformation Algorithmmentioning

confidence: 99%

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confidence: 99%

Posture-dependent dose coefficients of mesh-type ICRP reference computational phantoms for photon external exposures

et al. 2019

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“…Cosine interpolation and some similar interpolation function are used in complex situation to get accelerating and decelerating effects at the start and the end of expression animation. Richer expression can be achieved by using bilinear interpolation and other techniques [7][8][9][10].…”

Section: A Morphingmentioning

confidence: 99%

Review of Image-Based Facial Expression Generation Techniques

2009

2009 International Conference on Computational Intelligence and Software Engineering

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The facial expression generation is one of the important issues of intelligent human-machine interface. It is of great challenge due to the complexity and mutability of the human face. Image-based facial expression generation techniques inherit naturally some features of images which are beneficial to reduce complexity and improve realism of expression generation and have interested more and more researchers. The paper makes a classification of published researches, and discusses the features and shortcomings of typical algorithms. It provides some useful information and gives a reference to related works.

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“…The method preserved the global and local characteristics of the original image, which was useful for the deformation of rigid bodies. Guo H. et al proposed a kind of shape deformation based on non‐linear least‐square optimisation [10]. In the process of deformation, the length of the mesh was restrained to preserve the shape characteristics of the object's boundary.…”

Section: Introductionmentioning

confidence: 99%