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Linear Regression-based 1D Invariant Image for Shadow Detection and Removal in Single Natural Image

단일 자연 영상에서 그림자 검출 및 제거를 위한 선형 회귀 기반의 1D 불변 영상

  • Park, Ki-Hong (Division of Convergence Computer & Media, Mokwon University)
  • 박기홍 (목원대학교 융합컴퓨터.미디어학부)
  • Received : 2018.08.28
  • Accepted : 2018.09.15
  • Published : 2018.09.30

Abstract

Shadow is a common phenomenon observed in natural scenes, but it has a negative influence on image analysis such as object recognition, feature detection and scene analysis. Therefore, the process of detecting and removing shadows included in digital images must be considered as a pre-processing process of image analysis. In this paper, the existing methods for acquiring 1D invariant images, one of the feature elements for detecting and removing shadows contained in a single natural image, are described, and a method for obtaining 1D invariant images based on linear regression has been proposed. The proposed method calculates the log of the band-ratio between each channel of the RGB color image, and obtains the grayscale image line by linear regression. The final 1D invariant images were obtained by projecting the log image of the band-ratio onto the estimated grayscale image line. Experimental results show that the proposed method has lower computational complexity than the existing projection method using entropy minimization, and shadow detection and removal based on 1D invariant images are performed effectively.

그림자는 자연 경관에서 관찰되는 일반적인 현상이지만 물체 인식, 특징 검출 및 장면 분석등과 같은 영상 분석에 부정적인 영향을 미치는 요소이므로 디지털 영상에 포함된 그림자 처리는 디지털 영상 분석 과정에서 필수적으로 고려되어야 한다. 본 논문에서는 단일 자연 영상에 포함된 그림자를 검출하고 제거하기 위한 특징 요소 중의 하나인 1D 불변 영상의 획득을 위한 기존 방법들에 대해 기술하고, 선형 회귀 기반의 1D 불변 영상 획득 방법을 제안하였다. 제안하는 방법은 RGB 칼라 영상의 각 채널 간의 밴드 비의 로그를 계산한 후 선형 회귀를 통해 그레이스케일 영상 라인을 획득하고, 최종 1D 불변 영상은 밴드 비의 로그 영상들을 추정된 그레이스케일 영상 라인으로 투영시켜 획득하였다. 실험 결과, 제안하는 방법이 기존의 엔트로피 최소화 기반의 투영 각도를 계산하는 방법보다 계산 복잡도가 낮았으며, 1D 불변 영상을 이용한 그림자가 검출 및 제거가 효과적으로 수행됨을 보였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

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