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Soft Cost Aggregation with Multi-resolution Fusion
Xiao Tan1, 2, Changming Sun1, Dadong Wang1, Yi Guo1, and Tuan D. Pham3
1CSIRO Computational Informatics, North Ryde, NSW1670, Australia
tanxchong@gmail.com
changming.sun@csiro.au
dadong.wang@csiro.au
yi.guo@csiro.au
2The University of New South Wales, Canberra, ACT 2600, Australia
3The University of Aizu, Fukushima, Japan
tdpham@u-aizu.ac.jp
Abstract. This paper presents a simple and effective cost volume aggregation framework for addressing pixels labeling problem. Our idea is based on the observation that incorrect labelings are greatly reduced in cost volume aggregation results from low resolutions. However, image details may be lost in the low resolution results. To take advantage of the results from low resolution for reducing these incorrect labelings while preserving details, we propose a multi-resolution cost aggregation method (MultiAgg) by using a soft fusion scheme based on min-convolution. We implement our MultiAgg in applications on stereo matching and interactive image segmentation. Experimental results show that our method significantly outperforms conventional cost aggregation methods in labeling accuracy. Moreover, although MultiAgg is a simple and straight-forward method, it produces results which are close to or even better than those from iterative methods based on global optimization.
Keywords: Multi-resolution fusion, Cost aggregation, Stereo matching, Interactive segmentation
LNCS 8693, p. 17 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014