Anzeige
Erschienen in:
2016 | OriginalPaper | Buchkapitel
Stereo Matching Based on CF-EM Joint Algorithm
verfasst von : Baoping Li, Long Ye, Yun Tie, Qin Zhang
Erschienen in: Advances in Multimedia Information Processing - PCM 2016
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Abstract
Cost Filtering (CF) and Energy Minimization (EM) are two main cost aggregation methods in stereo matching. Due to global smoothness assumption, EM methods can get higher matching accuracy. However, they tend to fail in occluded areas, while locally adaptive support-weight CF method can solve it well. This paper proposed a CF-EM joint stereo matching framework on the basis of the proof that CF and EM methods can realize interconversion to each other. In this joint framework, we firstly use CF method with fully connected Markov Random Field (F-MRF) model to yield a more robust unary potential. And then, the output unary potential is used as the input to a standard EM method to compute the final disparity in Local connected MRF (L-MRF) model. Experiments results demonstrate that our method can improve the stereo matching accuracy as the achievement of energy transferring from F-MRF to L-MRF.