Use of Multiple Low Level Features to Find Interesting Regions

Michael Borck, Geoff West, Tele Tan


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.


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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

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,},

in EndNote Style

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