Journal of the Institute of Electronics Engineers of Korea SP (대한전자공학회논문지SP)

The Institute of Electronics and Information Engineers (대한전자공학회)
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Outlier-Object Detection Using an Image Pair Based on Regression Analysis: Noise Variance Estimation and Performance Analysis

영상 쌍에서 회귀분석에 기초한 이상 물체 검출: 잡음분산의 추정과 성능 분석

  • Kim, Dong-Sik (Department of Electronics and Information Engineering, Hankuk University of Foreign Studies)
  • 김동식 (한국외국어대학교 전자정보공학부)
  • Published : 2008.09.25
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Abstract

By comparing two images, which are captured with the same scene at different time, we can detect a set of outliers, such as occluding objects due to moving vehicles. To reduce the influence from the different intensity properties of the images, an intensity compensation scheme, which is based on the polynomial regression model, is employed. For an accurate detection of outliers alleviating the influence from a set of outliers, a simple technique that reruns the regression is employed. In this paper, an algorithm that iteratively reruns the regression is theoretically analyzed by observing the convergence property of the estimates of the noise variance. Using a correction constant for the estimate of the noise variance is proposed. The correction enables the detection algorithm robust to the choice of thresholds for selecting outliers. Numerical analysis using both synthetic and Teal images are also shown in this paper to show the robust performance of the detection algorithm.

동일한 위치에서 같은 장면을 담고 있지만 서로 다른 시간에 획득된 두 장의 영상을 서로 비교하여 움직이는 자동차등에 의한 겹침과 같은 이상점의 집합을 검출할 수 있다. 영상들의 서로 다른 밝기 특성에 의한 영향을 줄이기 위하여 다항식 회귀 모델에 근거한 밝기 보정을 하였다. 이상점 집합으로 인한 영향을 약화시키면서 정확한 이상점 검출을 위하여 회귀분석을 단순히 반복하는 알고리듬을 도입하였다. 본 논문에서는 회귀분석을 반복하는 알고리듬의 성능을 잡음분산의 추정의 수렴 특성을 관찰하므로 분석하였으며, 교정 상수를 잡음분산 추정에 사용하여 강인한 검출이 가능하도록 하였다. 합성 영상과 실제 영상에 검출 알고리듬을 실험하여 그 강인성을 보였다.

Keywords

References

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