Rigid Shape Registration Based on Extended Hamiltonian Learning
نویسندگان
چکیده
منابع مشابه
Elastic Model Based Non-rigid Registration Incorporation Statistical Shape Information
This paper describes a new method of non-rigid registration using the combined power of elastic and statistical shape models. The transformations are constrained to be consistent with a physical model of elasticity to maintain smoothness and continuity. A Bayesian formulation, based on this model, on an intensity similarity measure, and on statistical shape information embedded in corresponding...
متن کاملElastic Model Based Non-Rigid Registration Incorporating Statistical Shape Information
This paper describes a new method of non-rigid registration using the combined power of elastic and statistical shape models. The transformations are constrained to be consistent with a physical model of elasticity to maintain smoothness and continuity. A Bayesian formulation, based on this model, on an intensity similarity measure, and on statistical shape information embedded in corresponding...
متن کاملPhysical model-based non-rigid registration incorporating statistical shape information
This paper describes two new atlas-based methods of 2D single modality non-rigid registration using the combined power of physical and statistical shape models. The transformations are constrained to be consistent with the physical properties of deformable elastic solids in the first method and those of viscous fluids in the second, to maintain smoothness and continuity. A Bayesian formulation,...
متن کاملFast Musculoskeletal Registration Based on Shape Matching
This paper presents a new method for computing elastic and plastic deformations in the context of discrete deformable model-based registration. Internal forces are estimated by averaging local transforms between reference and current particle positions. Our technique can accommodate large non-linear deformations, and is unconditionally stable. Moreover, it is simple to implement and versatile. ...
متن کاملNon-rigid 3D Shape Registration using an Adaptive Template
We present a new fully-automatic non-rigid 3D shape registration (morphing) framework comprising (1) a new 3D landmarking and pose normalisation method; (2) an adaptive shape template method to accelerate the convergence of registration algorithms and achieve a better final shape correspondence and (3) a new iterative registration method that combines Iterative Closest Points with Coherent Poin...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Entropy
سال: 2020
ISSN: 1099-4300
DOI: 10.3390/e22050539