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

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Robust k-means Clustering-based High-speed Barcode Decoding Method to Blur and Illumination Variation

블러와 조명 변화에 강인한 k-means 클러스터링 기반 고속 바코드 정보 추출 방법

  • Kim, Geun-Jun (Department of Electronic Engineering, Dong-A University) ;
  • Cho, Hosang (Department of Electronic Engineering, Dong-A University) ;
  • Kang, Bongsoon (Department of Electronic Engineering, Dong-A University)
  • Received : 2015.09.02
  • Accepted : 2015.10.19
  • Published : 2016.01.31
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Abstract

In this paper presents Robust k-means clustering-based high-speed bar code decoding method to blur and lighting. for fast operation speed and robust decoding to blur, proposed method uses adaptive local threshold binarization methods that calculate threshold value by dividing blur region and a non-blurred region. Also, in order to prevent decoding fail from the noise, decoder based on k-means clustering algorithm is implemented using area data summed pixel width line of the same number of element. Results of simulation using samples taken at various worst case environment, the average success rate of proposed method is 98.47%. it showed the highest decoding success rate among the three comparison programs.

본 논문에서는 블러와 조명 변화에 강인한 바코드 디코딩 방법을 제안한다. 제안하는 디코딩 방법은 블러에 강인 디코딩과 빠른 연산속도를 위해 블러 영역과 비블러영역을 나누어 임계값을 연산하는 부분 지역 임계값 이진화 방법을 사용하였다. 또한 노이즈 데이터에 의한 인식 실패를 막기 위해서 동일한 엘리먼트 개수를 가지는 라인의 픽셀 너비를 모두 합한 면적 데이터를 이용하여 군집분류를 수행하는 k-means 알고리즘 기반의 디코더를 구현하였다. 다양한 악조건 환경에서 촬영된 샘플을 이용하여 실험 결과, 평균 98.47%로 높은 성공률을 보였으며 3개의 비교 프로그램 보다 성공률이 높았다.

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References

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