Large Scale Asset Extraction for Urban Images

Large Scale Asset Extraction for Urban Images

Lama Affara, Liangliang Nan, Bernard Ghanem, Peter Wonka
"Large Scale Asset Extraction for Urban Images"
European Conference on Computer Vision (ECCV 2016)​
Lama Affara, Liangliang Nan, Bernard Ghanem, Peter Wonka
urban assets, object proposals, image rectification
2016
Object proposals are currently used for increasing the computational efficiency of object detection. We propose a novel adaptive pipeline for interleaving object proposals with object classification and use it as a formulation for asset detection. We first preprocess the images using a novel and efficient rectification technique. We then employ a particle filter approach to keep track of three priors, which guide proposed samples and get updated using classifier output. Tests performed on over 1000 urban images demonstrate that our rectification method is faster than existing methods without loss in quality, and that our interleaved proposal method outperforms current state-of-the-art. We further demonstrate that other methods can be improved by incorporating our interleaved proposals.