Note: Generalized optical flow in the scale space

Authors:
Haifeng Gong;Chunhong Pan;Qing Yang;Hanqing Lu;Songde Ma
Affiliations:
National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China;National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China
Venue:
Computer Vision and Image Understanding
Year:
2007

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Abstract

Scale space is a natural way to handle multi-scale problems. Yang and Ma have considered the correspondence between scales, and proposed optical flow in the scale space. In this paper, we generalized Yang and Ma's work to generic images. We first generalize the Horn-Schunck algorithm to multi-dimensional multi-channel image sequence. Since the global smoothness constraint for regularization is no longer suitable in general cases, we introduce localized smoothness regularization. In scale space optical flow, points in original image trends to aggregate at a large scale, so we introduce aggregation density as an additional smoothness coefficient. At last, we apply the proposed methods to color images and 3D images.