A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2016; you can also visit the original URL.
The file type is
Sixth International Conference on Machine Vision (ICMV 2013)
We present a novel approach for combining 3D depth and visual information for object class and object instance recognition. Object classes are recognized by first assigning local geometric primitive labels using a CRF, followed by an SVM classification. Object instances are recognized using Hough-transform clustering of SURF features. Both algorithms perform well on publicly available object databases as well as on acquired data with an RGB-D camera. The object instance recognition algorithmdoi:10.1117/12.2049915 fatcat:pv5fg4kv4zgwnodv4b3rt7x6l4