Use of Multiple Low Level Features to Find Interesting Regions
Michael Borck, Geoff West, Tele Tan
2014
Abstract
Vehicle-based mobile mapping systems capture co-registered imagery and 3D point cloud information over hundreds of kilometres of transport corridor. Methods for extracting information from these large datasets are labour intensive and automatic methods are desired. In addition, such methods need to be easily configured by non-expert users to detect and measure many classes of objects. This paper describes a workflow to take a large number of image and depth features, use machine learning to generate an object detection system that is fast to configure and run. The output is high detection of the objects of interest but with an acceptable number of false alarms. This is desirable as the output is fed into a more complex and hence more computationally expensive analysis system to reject the false alarms and measure the remaining objects. Image and depth features from bounding boxes around objects of interest and random background are used for training with some popular learning algorithms. The interface allows a non-expert user to observe the performance and make modifications to improve the performance.
References
- Alexe, B., Deselaers, T., and Ferrari, V. (2010). What is an object? Computer Vision and Pattern Recognition, IEEE Computer Society Conference on.
- Badami, I., Stü ckler, J., and Behnke, S. (2013). Depthenhanced hough forests for object-class detection and continuous pose estimation. Semantic Perception, Mapping and Exploration, SPME-2013.
- Bay, H., Ess, A., Tuytelaars, T., and Gool, L. V. (2008). Speeded-up robust features (surf). Computer Vision and Image Understanding, 110(3):346 - 359.
- Besl, P. J. (1988). Active, optical range imaging sensors. Machine vision and applications, 1(2):127-152.
- Cadena, C. and Kos?ecka, J. (2013). Semantic parsing for priming object detection in rgb-d scenes. In Semantic Perception, Mapping and Exploration (SPME) 2013.
- Coleman, S., Scotney, B., and Suganthan, S. (2007). Feature extraction on range images - a new approach. In Robotics and Automation, 2007 IEEE International Conference on, pages 1098 -1103.
- Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Schmid, C., Soatto, S., and Tomasi, C., editors, International Conference on Computer Vision & Pattern Recognition, volume 2, pages 886-893.
- Dems?ar, J., Zupan, B., Leban, G., and Curk, T. (2004). Orange: From experimental machine learning to interactive data mining. In Boulicaut, J.-F., Esposito, F., Giannotti, F., and Pedreschi, D., editors, Knowledge Discovery in Databases: PKDD 2004, pages 537-539. Springer.
- Guinn, J. (2002). Enhanced formation flying validation report (jpl algorithm). NASA Goddard Space Flight Center Rept, pages 02-0548.
- He, D.-C. and Wang, L. (1991). Texture features based on texture spectrum. Pattern Recognition, 24(5):391 - 399.
- Huang, J., Lu, J., and Ling, C. (2003). Comparing naive bayes, decision trees, and svm with auc and accuracy. In Data Mining, 2003. ICDM 2003. Third IEEE International Conference on, pages 553-556.
- Jackson, D. A. (1993). Stopping rules in principal components analysis: a comparison of heuristical and statistical approaches. Ecology, pages 2204-2214.
- Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and psychological measurement.
- Kurita, T. and Boulanger, P. (1992). Computation of surface curvature from range images using geometrically intrinsic weights. MVA, pages 389-392.
- Ling, C. X., Huang, J., and Zhang, H. (2003). Auc: a better measure than accuracy in comparing learning algorithms. In Advances in Artificial Intelligence, pages 329-341. Springer.
- Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
- Mikolajczyk, K. and Schmid, C. (2001). Indexing based on scale invariant interest points. In Proceedings of Eighth IEEE International Conference on Computer Vision, 2001., volume 1, pages 525 -531.
- Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis & Machine Intelligence, 27(10):1615-1630.
- Motoda, H. and Liu, H. (2002). Feature selection, extraction and construction. Communication of IICM (Institute of Information and Computing Machinery, Taiwan) Vol, 5:67-72.
- Phung, S. and Bouzerdoum, A. (2007). Detecting people in images: An edge density approach. In Acoustics, Speech and Signal Processing, 2007. ICASSP 2007. IEEE International Conference on, volume 1, pages I-1229 -I-1232.
- Rosten, E., Porter, R., and Drummond, T. (2010). Faster and better: A machine learning approach to corner detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(1):105 -119.
- Tang, S., Wang, X., Lv, X., Han, T. X., Keller, J., He, Z., Skubic, M., and Lao, S. (2012). Histogram of oriented normal vectors for object recognition with a depth sensor. In Proceedings of 11th Asian Conference on Computer Vision (ACCV 2012).
- Wu, P., Ro, Y., Won, C., and Choi, Y. (2001). Texture descriptors in mpeg-7. In Skarbek, W., editor, Computer Analysis of Images and Patterns, volume 2124 of Lecture Notes in Computer Science, pages 21-28. Springer Berlin Heidelberg.
- Yan, L., Mozer, M. C., and Wolniewicz, R. (2003). Optimizing classifier performance via an approximation to the wilcoxon-mann-whitney statistic. Proceedings of the 20th International Conference on Machine Learning.
- Zhao, G. and Pietikainen, M. (2006). Local binary pattern descriptors for dynamic texture recognition. In Pattern Recognition, 2006. ICPR 2006. 18th International Conference on, volume 2, pages 211-214. IEEE.
- Zhao, Y., Liu, Z., Yang, L., and Cheng, H. (2012). Combing rgb and depth map features for human activity recognition. In Signal Information Processing Association Annual Summit Conference (APSIPA ASC), 2012 Asia-Pacific, pages 1-4.
Paper Citation
in Harvard Style
Borck M., West G. and Tan T. (2014). Use of Multiple Low Level Features to Find Interesting Regions . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 654-661. DOI: 10.5220/0004827506540661
in Bibtex Style
@conference{icpram14,
author={Michael Borck and Geoff West and Tele Tan},
title={Use of Multiple Low Level Features to Find Interesting Regions},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={654-661},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004827506540661},
isbn={978-989-758-018-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Use of Multiple Low Level Features to Find Interesting Regions
SN - 978-989-758-018-5
AU - Borck M.
AU - West G.
AU - Tan T.
PY - 2014
SP - 654
EP - 661
DO - 10.5220/0004827506540661