Journal of the Korea Society of Computer and Information (한국컴퓨터정보학회논문지)

Korean Society of Computer Information (한국컴퓨터정보학회)
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An Efficient Feature Point Extraction and Comparison Method through Distorted Region Correction in 360-degree Realistic Contents

  • Park, Byeong-Chan (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Kim, Jin-Sung (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Won, Yu-Hyeon (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Kim, Young-Mo (Dept. of Computer Science and Engineering, Soongsil University) ;
  • Kim, Seok-Yoon (Dept. of Computer Science and Engineering, Soongsil University)
  • Received : 2018.12.06
  • Accepted : 2019.01.25
  • Published : 2019.01.31
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Abstract

One of critical issues in dealing with 360-degree realistic contents is the performance degradation in searching and recognition process since they support up to 4K UHD quality and have all image angles including the front, back, left, right, top, and bottom parts of a screen. To solve this problem, in this paper, we propose an efficient search and comparison method for 360-degree realistic contents. The proposed method first corrects the distortion at the less distorted regions such as front, left and right parts of the image excluding severely distorted regions such as upper and lower parts, and then it extracts feature points at the corrected region and selects the representative images through sequence classification. When the query image is inputted, the search results are provided through feature points comparison. The experimental results of the proposed method shows that it can solve the problem of performance deterioration when 360-degree realistic contents are recognized comparing with traditional 2D contents.

Keywords

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Fig. 1. OMAF Architecture of MPEG-I

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Fig. 2. Image extraction and ERP

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Fig. 3. Image matching process

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Fig. 4. SIFT Algorithm

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Fig. 5. Frame extraction in VR content

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Fig. 6. Expression of ERP

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Fig. 7. Image area specification ratio

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Fig. 8. Distortion correction

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Fig. 9. Matching Process

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Fig. 10. 360 VR content used in experiment

Table 1. Sequence classification in Fig. 5

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Table 2. Image resize

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Table 3. Specify area

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Table 4. ① Experiment Result

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Table 5. ② Experiment Result

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Table 6. ③ Experiment Result

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Table 7. ④ Experiment Result

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Table 8. Matching Result

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