Detecting Patterns in Vector Fields

Xu, Liefei; Dinh, Huong Quynh; Mordohai, Philippos; Ramsay, Thomas

doi:10.2514/6.2011-1136

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…This notion is similar to what Cain [1994] proposed as Motion Constraints; however, our method focuses on capturing the dynamic nature of interactions of two or more shapes and not just static relationships. Moreover, we show that interactions can be factorized by analyzing their motion patterns in an appropriate vector space [Xu et al 2011]. Related mathematical ideas based on "currents" for describing surfaces can also be found in Durlemann [2010].…”

Section: Related Workmentioning

confidence: 88%

“…tionally, we derive the attributes vector orientation O, and vector magnitude M directly from the vector field. In contrast to the approach of Xu et al [2011], we do not consider the attribute pressure P , as it cannot easily be acquired for all motion drivers in our framework.…”

Section: Appendixmentioning

confidence: 99%

“…To compute this distance we compare locally captured motion flows. We compute the gradient for each vector field vi ∈ V1, V2 to derive a set of its first-order attributes [Xu et al 2011], which associate a real value to each cell ci,j. In particular, we measure the magnitude of the attributes vorticity, dilatation, shear strain rate, and the gradient tensor field T (see Appendix 7 for the precise definitions).…”

Section: Encoding Motion Flowsmentioning

confidence: 99%

“…We analyze the captured motion flows by computing a set of firstorder attributes on a vector field. As shown in Xu et al [2011] the gradient of a 3D vector field vi is an asymmetric 3D tensor field that can be decomposed into symmetric S and asymmetric A components. While the diagonal and off-diagonal entries of S represents dilatation and shearing respectively, the off-diagonal entries of A measure vorticity.…”

Section: Appendixmentioning

confidence: 99%

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Understanding and Exploiting Object Interaction Landscapes

Pirk

Krs

et al. 2017

ACM Trans. Graph.

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Figure 1: Different interaction landscapes representing the interactions of a motion driver with a static object. We capture the motion trajectories (red) and encode their signatures into a descriptor that can be used for comparing interactions. From left to right: a cloth simulation interacting with a support structure, a human model walking on a floor, a wind simulation interacting with a car, and a robotic hand grasping a cup. AbstractInteractions play a key role in understanding objects and scenes, for both virtual and real world agents. We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved. The representation is based on tracking particles on one of the participating objects and then observing them with sensors appropriately placed in the interaction volume or on the interaction surfaces. We show how to factorize these interaction descriptors and project them into a particular participating object so as to obtain a new functional descriptor for that object, its interaction landscape, capturing its observed use in a spatio-temporal framework. Interaction landscapes are independent of the particular interaction and capture subtle dynamic effects in how objects move and behave when in functional use. Our method relates objects based on their function, establishes correspondences between shapes based on functional key points and regions, and retrieves peer and partner objects with respect to an interaction.

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Section: Related Workmentioning

confidence: 88%

Section: Appendixmentioning

confidence: 99%

Section: Encoding Motion Flowsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

See 2 more Smart Citations

Understanding and Exploiting Object Interaction Landscapes

Pirk

Krs

et al. 2017

ACM Trans. Graph.

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Quantifying Flow Field Distances Based on a Compact Streamline Representation

Graening

Ramsay

2013

Intelligent Data Engineering and Automated Learning – IDEAL 2013

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Understanding and Exploiting Object Interaction Landscapes

Pirk

Krs

et al. 2017

ACM Trans. Graph.

View full text Add to dashboard Cite

Interactions play a key role in understanding objects and scenes for both virtual and real-world agents. We introduce a new general representation for proximal interactions among physical objects that is agnostic to the type of objects or interaction involved. The representation is based on tracking particles on one of the participating objects and then observing them with sensors appropriately placed in the interaction volume or on the interaction surfaces. We show how to factorize these interaction descriptors and project them into a particular participating object so as to obtain a new functional descriptor for that object, its interaction landscape , capturing its observed use in a spatiotemporal framework. Interaction landscapes are independent of the particular interaction and capture subtle dynamic effects in how objects move and behave when in functional use. Our method relates objects based on their function, establishes correspondences between shapes based on functional key points and regions, and retrieves peer and partner objects with respect to an interaction.

show abstract

Detecting Patterns in Vector Fields

Cited by 4 publications

References 7 publications

Understanding and Exploiting Object Interaction Landscapes

Understanding and Exploiting Object Interaction Landscapes

Quantifying Flow Field Distances Based on a Compact Streamline Representation

Understanding and Exploiting Object Interaction Landscapes

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