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Advanced feature point transformation of corner points for mobile object recognition

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Abstract

In this paper, a new method is proposed for recognizing rotated objects based on extracted feature points from the Harris corner detector using object corner points at real time in a mobile environment. With this proposed method, corner points can be rapidly extracted from the input image and object recognition can be conducted by comparing existing feature points. Color values for corner pixels in the feature points of rotated objects are generally varied in accordance with the degree of object rotation. The color values of the feature points are affected by the degree of rotated objects; therefore, there is a possibility that these values can be mixed with nearby pixel values. By analyzing the color values of the rotated pixels, we can extract feature points of rotated objects and use them for object detection to be effectively applied for rotated object detection. Although overall recognition rate is somewhat degraded, using the proposed object recognition with corner information of feature points makes it possible to execute in real time in mobile environments by reducing the amount of computation. Accordingly, this method can be applied to recognizing rotated objects, even in a mobile environment with limited computing capabilities. Experimental results show that the proposed method can provide approximately 96 % accuracy and high performance compared to other methods. Therefore, the proposed method can be adapted to recognize any rotated object with performance and accuracy.

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and future Planning (NRF-2012R1A1A2043400).

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Authors and Affiliations

  1. Computer Science, Yonsei University, Seoul, South Korea

    Xiyuan Yin & Shin-Dug Kim

  2. LG Electronics Seoul, Seoul, South Korea

    Dae-Hwan Kim

  3. Mobile Communications, LG Electronics, Seoul, South Korea

    Chung-Pyo Hong

  4. Computer Science, Namseoul University, Cheonan-si, South Korea

    Cheong-Ghil Kim

  5. Department of Convergence Security, Kyonggi University, Suwon-si, South Korea

    Kuinam J. Kim

Authors
  1. Xiyuan Yin
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  2. Dae-Hwan Kim
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  3. Chung-Pyo Hong
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  4. Cheong-Ghil Kim
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  5. Kuinam J. Kim
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  6. Shin-Dug Kim
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Corresponding author

Correspondence to Shin-Dug Kim.

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Yin, X., Kim, DH., Hong, CP. et al. Advanced feature point transformation of corner points for mobile object recognition. Multimed Tools Appl 74, 6541–6556 (2015). https://doi.org/10.1007/s11042-014-2241-6

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  • DOI: https://doi.org/10.1007/s11042-014-2241-6

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