Two-dimensional local ternary patterns using synchronized images for outdoor place categorization

Hojung Jung, Ryo Kurazume, Yumi Iwashita, Oscar Martinez Mozos

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present a novel approach for outdoor place categorization using synchronized texture and depth images obtained using a laser scanner. Categorizing outdoor places according to type is useful for autonomous driving or service robots, which work adaptively according to the surrounding conditions. However, place categorization is not straight forward due to the wide variety of environments and sensor performance limitations. In the present paper, we introduce a two-dimensional local ternary pattern (2D-LTP) descriptor using a pair of synchronized texture and depth images. The proposed 2D-LTP describes the local co-occurrence of a synchronized and complementary image pair with ternary patterns. In the present study, we construct histograms of a 2D-LTP as a feature of an outdoor place and apply singular value decomposition (SVD) to deal with the high dimensionality of the place. The novel descriptor, i.e., the 2D-LTP, exhibits a higher categorization performance than conventional image descriptors with outdoor place experiments.

Original languageEnglish
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages5726-5730
Number of pages5
ISBN (Electronic)9781479957514
DOIs
Publication statusPublished - Jan 28 2014

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition

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    Jung, H., Kurazume, R., Iwashita, Y., & Mozos, O. M. (2014). Two-dimensional local ternary patterns using synchronized images for outdoor place categorization. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 5726-5730). [7026158] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIP.2014.7026158