한국정보통신학회논문지 (Journal of the Korea Institute of Information and Communication Engineering)

  • 제26권7호
  • /
  • Pages.996-1003
  • /
  • 2022
  • /
  • 2234-4772(pISSN)
  • /
  • 2288-4165(eISSN)
한국정보통신학회 (The Korea Institute of Information and Commucation Engineering)
권호 표지
DOI QR코드

DOI QR Code

블록 HOG 군집화 기반의 1-D 바코드 크로스라인 결정

Determination of Bar Code Cross-line Based on Block HOG Clustering

  • Kim, Dong Wook (Department of Information & Communication Engineering, Jeonju University)
  • 투고 : 2022.05.11
  • 심사 : 2022.07.14
  • 발행 : 2022.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 비전 기반의 1-D 바코드 검출을 위한 스캔 라인 및 범위 결정을 위한 새로운 방법을 제시한다. 블록 HOG(histogram of gradient)를 바탕으로 DBSCAN 군집화 방법을 적용하여 유효한 바코드 대표점 및 방향을 검출하고 이를 바탕으로 스캔 라인 및 바코드 크로스라인을 결정하는 방법에 관한 연구이다. 본 논문에서는 얻어진 스캔라인을 바탕으로 바코드의 크로스라인 범위를 결정하기 위해 최소 및 최대탐색 기법이 적용되었다. 이것은 바코드의 크기에 무관하게 적용될 수 있다. 제안된 기법은 바코드의 일부 영역만 검출해도 바코드 인식이 가능하며, 또한 바코드 영역 검출 후 코드를 읽기 위해 회전을 필요로 하지 않는다. 또한, 다양한 크기의 바코드 검출이 가능하다. 본 논문의 제안된 기법에 대한 성능을 평가를 위해 다양한 실험결과를 제시하였다.

In this paper, we present a new method for determining the scan line and range for vision-based 1-D barcode recognition. This is a study on how to detect valid barcode representative points and directions by applying the DBSCAN clustering method based on block HOG (histogram of gradient) and determine scan lines and barcode crosslines based on this. In this paper, the minimum and maximum search techniques were applied to determine the cross-line range of barcodes based on the obtained scan lines. This can be applied regardless of the barcode size. This technique enables barcode recognition even by detecting only a partial area of the barcode, and does not require rotation to read the code after detecting the barcode area. In addition, it is possible to detect barcodes of various sizes. Various experimental results are presented to evaluate the performance of the proposed technique in this paper.

키워드

참고문헌

  1. T. Do, Y. Tolcha, T. J. Jun, and D. Kim, "Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding," in 2020 25th International Conference on Pattern Recognition (ICPR), Milan, Italy, pp. 3019-3026, 2021.
  2. M. Katona and L. Nyul. "A Novel Method for Accurate and Efficient Barcode Detection with Morphological Operations," in 2012 Eighth International Conference on Signal Image Technology and Internet Based Systems (SITIS), Sorrento, Italy, pp. 307-314, 2012.
  3. R. Oktem, "Bar code localization in wavelet domain by using binary morphology," in Proceedings of the IEEE 12th Signal Processing and Communications Applications Conference, Kusadasi, Turkey, pp. 499-501, 2004.
  4. A. Zamberletti, I. Gallo, and S. Albertini, "Robust angle invariant 1d barcode detection," in Pattern Recognition (ACPR), 2013 2nd IAPR Asian Conference, Naha, Japan, pp. 160-164, 2013.
  5. N. S. Ishtewi and M. A. E. Abdalla, "Vision-Based One-Dimensional Barcode Detection," in ICEMIS'20: Proceedings of the 6th International Conference on Engineering & MIS 2020, New York: NY, USA, pp. 1-5, 2020.
  6. Andrey Z. and I. Zagaynov, "Universal Barcode Detector via Semantic Segmentation," in International Conference on Document Analysis and Recognition (ICDAR), Sydney: NSW, Australia, pp. 837-842, 2019.
  7. I. Yun and J. Kim, "Vision-Based 1D Barcode Localization Method for Scale and Rotation Invariant," in Proceedings of the 2017 IEEE Region 10 Conference (TENCON), Penang, Malaysia, pp. 2204-2208, 2017.
  8. D. Kim and J. Kang, "Barcode Image Identification Based on Maximum a Posterior Probability," in 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas: NV, USA, pp. 1-2, 2022.