TY - GEN
T1 - Building facade detection, segmentation, and parameter estimation for mobile robot localization and guidance
AU - Delmerico, Jeffrey A.
AU - David, Philip
AU - Corso, Jason J.
PY - 2011
Y1 - 2011
N2 - Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with an accurate segmentation of the various building facades in a scene. Toward that end, we present a system for segmenting and labeling an input image that for each pixel, seeks to answer the question "Is this pixel part of a building facade, and if so, which one?" The proposed method determines a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar model. The corresponding disparity map and a discriminative classification provide prior information for a two-layer Markov Random Field model. This MRF problem is solved via Graph Cuts to obtain a labeling of building facade pixels at the mid-level, and a segmentation of those pixels into particular planes at the high-level. The results indicate a strong improvement in the accuracy of the binary building detection problem over the discriminative classifier alone, and the planar surface estimates provide a good approximation to the ground truth planes.
AB - Building facade detection is an important problem in computer vision, with applications in mobile robotics and semantic scene understanding. In particular, mobile platform localization and guidance in urban environments can be enabled with an accurate segmentation of the various building facades in a scene. Toward that end, we present a system for segmenting and labeling an input image that for each pixel, seeks to answer the question "Is this pixel part of a building facade, and if so, which one?" The proposed method determines a set of candidate planes by sampling and clustering points from the image with Random Sample Consensus (RANSAC), using local normal estimates derived from Principal Component Analysis (PCA) to inform the planar model. The corresponding disparity map and a discriminative classification provide prior information for a two-layer Markov Random Field model. This MRF problem is solved via Graph Cuts to obtain a labeling of building facade pixels at the mid-level, and a segmentation of those pixels into particular planes at the high-level. The results indicate a strong improvement in the accuracy of the binary building detection problem over the discriminative classifier alone, and the planar surface estimates provide a good approximation to the ground truth planes.
UR - http://www.scopus.com/inward/record.url?scp=84455175349&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84455175349&partnerID=8YFLogxK
U2 - 10.1109/IROS.2011.6048385
DO - 10.1109/IROS.2011.6048385
M3 - Conference contribution
AN - SCOPUS:84455175349
SN - 9781612844541
T3 - IEEE International Conference on Intelligent Robots and Systems
SP - 1632
EP - 1639
BT - IROS'11 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
T2 - 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems: Celebrating 50 Years of Robotics, IROS'11
Y2 - 25 September 2011 through 30 September 2011
ER -