융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
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일반화된 허프변환의 성능평가

Performance Evaluation of the Generalized Hough Transform

  • 투고 : 2017.11.01
  • 심사 : 2017.12.20
  • 발행 : 2017.12.31
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초록

일반화된 허프변환은 임의의 형태의 2차원 모델을 입력영상에서 탐지 및 추출하는데 사용되어지는 효과적인 방법이다. 그러나 일반화된 허프변환의 단점으로 실행시간이 오래 걸린다는 것과 과도한 메모리 사용을 들 수 있다. 그래서 현재까지의 대부분의 연구는 일반화된 허프변환의 실행시간과 메모리 사용량을 줄이는데 집중되어왔다. 그러나 실행시간과 메모리 사용을 줄여서 개선된 알고리즘이 입력 영상에 존재하는 노이즈를 고려할 경우 어떤 성능을 제공하는가는 여전히 불분명하다. 그러므로 본 논문은 일반화된 허프변환의 성능 평가를 위한 새로운 프레임워크를 제안한다. 이를 위해 일반화된 허프변환을 신호탐지 이론의 탐지기로 간주하며 ROC 커브를 사용해서 일반화된 허프변환의 성능을 정의한다. 마지막으로 입력 영상에서의 노이즈를 고려한 정량적인 성능 평가가 가능함을 보인다.

The generalized Hough transform(GHough) can be used effectively for detecting and extracting an arbitrary-shaped 2-D model in an input image. However, the main drawbacks of the GHough are both heavy computation and an excessive storage requirement. Thus, most of the researches so far have focused on reducing both the time and space requirement of the GHough. But it is still not clear how well their improved algorithms will perform under various noise in an input image. Thus, this paper proposes a new framework that can measure the performance of the GHough quantitatively. For this purpose, we view the GHough as a detector in signal detection theory and the ROC curve will be used to specify the performance of the GHough. Finally, we show that we can evaluate the GHough under various noise conditions in an input image.

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