Robust and Efficient 3-D Optical Flow Computing Framework
with scalable and flexible numerical methods
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This book presents a framework that improves the robustness and accuracyin computing dense optical-flow fields. We propose a global formulationwith a regularization term that can handle the 3D flow patterns, such as rotation, zoom, and their combinations, induced by a 3D rigid-body motion.The movitation of this book is that the traditionaloptical flow regularizers incorrectly penalize the above 3D flow patterns andresult in biased estimates. Hence, the purpose of this work is to derive a new suite of regularization expressions that treat all valid flow patterns resulting from a 3D rigid-body m...
This book presents a framework that improves the
robustness and accuracy
in computing dense optical-flow fields. We propose
a global formulation
with a regularization term that can handle the 3D
flow patterns, such as rotation, zoom, and their
combinations,
induced by a 3D rigid-body motion.
The movitation of this book is that the traditional
optical flow regularizers incorrectly penalize the
above 3D flow patterns and
result in biased estimates. Hence, the purpose of
this work is to derive a new suite of regularization
expressions that treat all valid flow patterns
resulting from a 3D rigid-body
motion equally, without unfairly penalizing any of them.
Furthermore, we
develop a framework of optical flow computation and
flow model inference
method. The proposed framework can fully utilize the
power of the proposed
3D rigid body motion regularizers. The proposed
framework is well suited for implementing
real-time and high accuracy image and video
processing applications.
robustness and accuracy
in computing dense optical-flow fields. We propose
a global formulation
with a regularization term that can handle the 3D
flow patterns, such as rotation, zoom, and their
combinations,
induced by a 3D rigid-body motion.
The movitation of this book is that the traditional
optical flow regularizers incorrectly penalize the
above 3D flow patterns and
result in biased estimates. Hence, the purpose of
this work is to derive a new suite of regularization
expressions that treat all valid flow patterns
resulting from a 3D rigid-body
motion equally, without unfairly penalizing any of them.
Furthermore, we
develop a framework of optical flow computation and
flow model inference
method. The proposed framework can fully utilize the
power of the proposed
3D rigid body motion regularizers. The proposed
framework is well suited for implementing
real-time and high accuracy image and video
processing applications.
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