Optimal Filters for Gradient-based Motion Estimation

Gradient based approaches for motion estimation (Optical-Flow) estimate the motion of an image sequence based on local changes in the image intensities. In order to best evaluate local changes in the intensities, speci c lters are applied to the image sequence. These lters are typically composed of spatio-temporal derivatives. The design of these lters plays an important role in the estimation accuracy. This paper proposes a method for the design of these lters in an optimal manner. Unlike previous approaches that design optimal derivative lters in some sense, the proposed technique de nes the optimality directly with respect to the motion estimation goal The suggested approach takes into account prior knowledge on the motion distribution, the image characteristics, and the allocated lter length. Simulations demonstrate the advantage of the new design approach.

• Type: Conference
• Year: 1999
• Where: ICCV
• Authors: Michael Elad, Patrick C. Teo, Yacov Hel-Or
• Keywords: Computer Vision, Estimation Accuracy, Evaluate Local Changes, ICCV 1999, Image Intensities, Image Sequence, Motion Estimation Goal