TY - JOUR
T1 - Building facade detection, segmentation, and parameter estimation for mobile robot stereo vision
AU - Delmerico, Jeffrey A.
AU - David, Philip
AU - Corso, Jason J.
PY - 2013
Y1 - 2013
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 accurate models of the various building facades in a scene. Toward that end, we present a system for detection, segmentation, and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model, and generates 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 models. These two models are incorporated into a two-layer Markov Random Field (MRF): an appearance- and disparity-based discriminative classifier at the mid-level, and a geometric model to segment the building pixels into facades at the high-level. By using object-specific stereo features, our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance-based features. Furthermore, the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models 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 accurate models of the various building facades in a scene. Toward that end, we present a system for detection, segmentation, and parameter estimation of building facades in stereo imagery. The proposed method incorporates multilevel appearance and disparity features in a binary discriminative model, and generates 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 models. These two models are incorporated into a two-layer Markov Random Field (MRF): an appearance- and disparity-based discriminative classifier at the mid-level, and a geometric model to segment the building pixels into facades at the high-level. By using object-specific stereo features, our discriminative classifier is able to achieve substantially higher accuracy than standard boosting or modeling with only appearance-based features. Furthermore, the results of our MRF classification indicate a strong improvement in accuracy for the binary building detection problem and the labeled planar surface models provide a good approximation to the ground truth planes.
KW - Building facade detection
KW - Hierarchical
KW - Markov random field
KW - Mobile robot perception
KW - Model-based stereo vision
KW - Stereo vision
UR - http://www.scopus.com/inward/record.url?scp=84885137400&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885137400&partnerID=8YFLogxK
U2 - 10.1016/j.imavis.2013.08.006
DO - 10.1016/j.imavis.2013.08.006
M3 - Article
AN - SCOPUS:84885137400
SN - 0262-8856
VL - 31
SP - 841
EP - 852
JO - Image and Vision Computing
JF - Image and Vision Computing
IS - 11
ER -