3D Metamorphosis Between Different Types of Geometric Models

Breen, David E.; Mauch, Sean; Whitaker, Ross T.; Mao, Jia

doi:10.1111/1467-8659.00496

Cited by 9 publications

(5 citation statements)

References 26 publications

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“…An algorithm with linear complexity is described in Ref. [41]. This method utilizes the Voronoi diagram for faces, edges and vertices of the mesh.…”

Section: Figure 13mentioning

confidence: 99%

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Collision Detection for Deformable Objects

Teschner

Kimmerle

Heidelberger

et al. 2005

Computer Graphics Forum

444

333

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Interactive environments for dynamically deforming objects play an important role in surgery simulation and entertainment technology. These environments require fast deformable models and very efficient collision handling techniques. While collision detection for rigid bodies is well-investigated, collision detection for deformable objects introduces additional challenging problems. This paper focuses on these aspects and summarizes recent research in the area of deformable collision detection. Various approaches based on bounding volume hierarchies

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“…An algorithm with linear complexity is described in Ref. [41]. This method utilizes the Voronoi diagram for faces, edges and vertices of the mesh.…”

Section: Figure 13mentioning

confidence: 99%

“…Besides collision detection, distance fields have a wide range of applications. They have been used for morphing [41,42], volumetric modeling [43,44], motion planning [45] and recently for the animation of fire [46]. Distance fields are sometimes called distance volumes [41] or distance functions [47].…”

Section: Distance Fieldsmentioning

confidence: 99%

Collision Detection for Deformable Objects

Teschner

Kimmerle

Heidelberger

et al. 2005

Computer Graphics Forum

444

333

View full text Add to dashboard Cite

show abstract

“…Distance field is sometimes called distance volumes [64] or distance functions [65]. The distance map of the object surface is computed by a specific distance function and the collisions queries are performed on these values.…”

Section: Distance Fieldmentioning

confidence: 99%

Calculation and Visualization of Range of Motion of Hip Joint from MRI

Aghayan

Lee

2014

2014 IEEE 27th International Symposium on Computer-Based Medical Systems

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Femoro-Acetabular Impingement (FAI) is a hip joint disease which affects and impairs the range of hip motion during performing activities of daily living, jogging, walking, or climbing stairs due to the bony abnormalities of the joint. Ballet dancers and athletes (e.g. gymnasts and hockey players) put their hips at the risk of FAI by extremely moving the hip mainly by excessively rotating the joint.In this research, we introduce a visualization system which helps surgeons to analyze the range of hip motions as well as to have a better communication with patients. These goals are achieved by presenting three dimensional (3D) visualizations of motion envelope by examining the maximum possible rotation of the digital hip bones. Our computer simulation system estimates, analyzes and visualizes the maximum hip range of motion (ROM) for the constructed 3D bone models that are extracted from Magnetic Resonance Images (MRI) after segmenting the bones. These tasks are accomplished by first calculating Hip Joint Center (HJC) which is center of rotation of femoral head on the 3D segmented MRI models followed by simulating hip motions with examining impingement between the femur and the acetabulum using our collision detection system. In our collision detection system, surfaces of femoral head and acetabulum bones are sampled in the spherical coordinates based on rasterization and interpolation. Then, the distance between the femoral head and acetabulum are computed to prevent impingement between them. The maximum motion degree of femur bone within depression of acetabulum in every direction iii during the digital simulation shows the ROMs of the inputted MRI of the hip joint. Six primary plane motions (flexion/extension, abduction/adduction and internal/external rotation) as well as various combinations of these motions (maximum rotation of the hip between every two rotational movements) and successive movements (maximum rotational movement of the hip per another rotational movement) are simulated and analyzed along with 3D visualization of estimated range of these motions.Generally, the ROM differs by some factors such as age, gender, ethnicity, and geographic location. For instance, newborns up to age two have considerably greater motion in hip flexion and hip abduction than adults. Our system by 3D visualization of motion envelope will provide a platform to understand quicker and better the effect of bony morphology of the hip joint on the possible ROM.We also examine the long-standing question about moving center of rotation related to ROM.We found out the ROM becomes bigger especially when the center moves outward to the direction of acetabulum axis.This thesis does not consider the effect of muscle and other surrounding connective tissue on the hip ROM since they can be altered significantly by physical training to show the potential of maximum ROM. For example a ballerina has a bigger ROM leading a bigger motion envelope compared with non-dancers. Hence we visualize the range of joint motions and their envel...

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“…This is done by walking in the normal direction of the interface with a distance proportional to the target signed distance map value. In [9] a level set approach for morphing between signed distance maps is presented. Here signed distance maps are constructed from a characteristic scan conversion algorithm, and a similarity measure is formulated as a functional.…”

Section: Fig 2 Leftmentioning

confidence: 99%

Guessing Tangents in Normal Flows

2008

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Given a flow field parallel to isophote normals, a normal flow field, we seek a unobservable tangential field as the minimum of a general energy functional of the total field. We generalize existing methods to any linear, differential operator order on the combined field while keeping the projection onto the isophote normal constant. We discuss invariant flow fields, present a novel iterative solution based on Euler-Lagrange equations, prove continuous convergence, and give synthetic examples for common energy functionals. Possible uses are: estimating physical flow in image sequences, estimating human growth processes, and co-warping textures in animation sequences.

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3D Metamorphosis Between Different Types of Geometric Models

Cited by 9 publications

References 26 publications

Collision Detection for Deformable Objects

Collision Detection for Deformable Objects

Calculation and Visualization of Range of Motion of Hip Joint from MRI

Guessing Tangents in Normal Flows

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