An adaptive local binary pattern for 3D hand tracking

Joongrock Kim, Sunjin Yu, Dongchul Kim, Kar Ann Toh, Sangyoun Lee

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

Ever since the availability of real-time three-dimensional (3D) data acquisition sensors such as time-of-flight and Kinect depth sensor, the performance of gesture recognition can be largely enhanced. However, since conventional two-dimensional (2D) image based feature extraction methods such as local binary pattern (LBP) generally use texture information, they cannot be applied to depth or range image which does not contain texture information. In this paper, we propose an adaptive local binary pattern (ALBP) for effective depth images based applications. Contrasting to the conventional LBP which is only rotation invariant, the proposed ALBP is invariant to both rotation and the depth distance in range images. Using ALBP, we can extract object features without using texture or color information. We further apply the proposed ALBP for hand tracking using depth images to show its effectiveness and its usefulness. Our experimental results validate the proposal.

Original languageEnglish
Pages (from-to)139-152
Number of pages14
JournalPattern Recognition
Volume61
DOIs
Publication statusPublished - 2017 Jan 1

Bibliographical note

Funding Information:
This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education ( NRF-2015R1D1A1A01061315 ).

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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