Conditional random fields for urban scene classification with full waveform LiDAR data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6952 LNCS, Issue , 2011, Pages 233-244

  Niemeyer, Joachim ^a   Wegner, Jan Dirk ^a   Mallet, Clément ^b   Rottensteiner, Franz ^a   Soergel, Uwe ^a  

^a LEIBNIZ UNIVERSITY HANNOVER   (Germany)

^b UNIVERSITÉ PARIS EST   (France)

Author keywords

3D Point Cloud; Classification; Conditional Random Fields; Full Waveform LiDAR; Urban

Indexed keywords

3D POINT CLOUD; AIRBORNE LASER SCANNERS; CLASSIFICATION PROCESS; CONDITIONAL RANDOM FIELD; CONDITIONAL RANDOM FIELDS; CONTEXT-BASED CLASSIFICATION; DENSE URBAN AREAS; ECHO WIDTH; FULL-WAVEFORMS; LIDAR DATA; MARKOV RANDOM FIELDS; OBJECT CLASS; OBJECT STRUCTURE; POINT CLOUD; POINT DENSITY; POINT-BASED; REAL-WORLD DATASETS; URBAN; URBAN SCENES;

CONTENT BASED RETRIEVAL; IMAGE ANALYSIS; OPTICAL RADAR; PHOTOGRAMMETRY; THREE DIMENSIONAL;

IMAGE SEGMENTATION;

EID: 80054034092     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-24393-6_20     Document Type: Conference Paper

References (13)

1. Anguelov, D., Taskar, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz, G., Ng, A.: Discriminative learning of markov random fields for segmentation of 3d scan data. In: Proc. of IEEE CVPR, vol. 2, pp. 169-176 (2005)
2. Chehata, N., Guo, L., Mallet, C.: Airborne lidar feature selection for urban classification using random forests. IntArchPhRS 38(3), 207-212 (2009)
3. Frey, B., MacKay, D.: A revolution: Belief propagation in graphs with cycles. In: Advances in Neural Information Processing Systems, vol. 10, pp. 479-485 (1998)
4. Kumar, S., Hebert, M.: Discriminative random fields. International Journal of Computer Vision 68(2), 179-201 (2006)
5. Lafferty, J., McCallum, A., Pereira, F.: Conditional random fields: Probabilistic models for segmenting and labeling sequence data. In: Proc. 18th Int. Conference on Machine Learning, pp. 282-289 (2001)
6. Lim, E., Suter, D.: Conditional random field for 3d point clouds with adaptive data reduction. In: Int. Conference on Cyberworlds, pp. 404-408 (2007)
7. Lim, E., Suter, D.: 3d terrestrial lidar classifications with super-voxels and multi-scale conditional random fields. Comp. Aided Design 41(10), 701-710 (2009)
8. Mallet, C.: Analysis of Full-Waveform Lidar Data for Urban Area Mapping. Ph.D. thesis, Télécom ParisTech (2010)
9. Munoz, D., Vandapel, N., Hebert, M.: Directional associative markov network for 3-d point cloud classification. In: Int. Symposium 3DPVT (2008)
10. Rusu, R., Holzbach, A., Blodow, N., Beetz, M.: Fast geometric point labeling using conditional random fields. In: IEEE IROS, pp. 7-12 (2009)
11. Shapovalov, R., Velizhev, A., Barinova, O.: Non-associative markov networks for 3d point cloud classification. In: PCV 2010. IntArchPhRS, vol. 38(A), pp. 103-108 (2010)
12. Vishwanathan, S., Schraudolph, N., Schmidt, M., Murphy, K.: Accelerated training of conditional random fields with stochastic gradient methods. In: 23rd Int. Conference on Machine Learning, pp. 969-976 (2006)
13. Wagner, W., Ullrich, A., Ducic, V., Melzer, T., Studnicka, N.: Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner. ISPRS Journal of Photogrammetry and Remote Sensing 60(2), 100-112 (2006)