Uncertainty Models of the Gradient Constraint for Optical Flow Computation

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概要

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• The uncertainty involved in the gradient constraint for optical flow detection is often modeled as constant Gaussian noise added to the time-derivative of the image intensity. In this paper, we examine this modeling closely and investigate the error behavior by ekperiments. Our result indicates that the error depends on both the spatial derivatives and the image motion. We propose alternative uncertainty models based on our experiments. It is shown that the optical flow computation algorithms based on them can detect more accurate optical flow than the conventional least-squares method.

• 一般社団法人電子情報通信学会の論文
• 1996-07-25

著者

• Ohta Naoya

  Department Of Computer Science Gunma University

関連論文

• Optimal Structure-from-Motion Algorithm for Optical Flow
• Optical Flow Detection Using a General Noise Model
• Uncertainty Models of the Gradient Constraint for Optical Flow Computation
• Optimal Robot Self-Localization and Accuracy Bounds

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