KAUST DepartmentComputer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Electrical Engineering Program
Visual Computing Center (VCC)
Permanent link to this recordhttp://hdl.handle.net/10754/622212
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AbstractObject 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. © Springer International Publishing AG 2016.
CitationAffara L, Nan L, Ghanem B, Wonka P (2016) Large Scale Asset Extraction for Urban Images. Lecture Notes in Computer Science: 437–452. Available: http://dx.doi.org/10.1007/978-3-319-46487-9_27.
SponsorsThis research work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research.