지역적 밝기 변화에 강인한 물체 인식을 위한 지역 서술자와 엔트로피 기반 유사도 척도에 관한 연구
A study on a local descriptor and entropy-based similarity measure for object recognition system being robust to local illumination change
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참고문헌
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초록
본 논문에서는 지역적인 밝기 변화에 강인한 지역 서술자와 유사도 척도를 제안한다. 제안한 지역 서술자는 Haar웨이블렛 필터를 이용하여 특징점과 주변의 주파수 특성을 포함한 지역 서술자를 정의하여 지역적으로 불균일한 조명의영향에도 특징점을 명확히 서술할 수 있다. 제안한 유사도 척도는 기존의 엔트로피 기반의 유사도에 지역 서술자로 계산한 유사도를 결합한 형태이다. 이는 지역적인 조명의 변화가 발생한 영역의 유사도를 정확히 반영할 수 있다. 실험을통해 제안한 방법의 성능을 검증하였다.
Abstract
In this paper, we propose a local descriptor and a similarity measure that is robust to radiometic variations. The proposedlocal descriptor is made up Haar wavelet filter and it can contain frequency informations about the feature point and itssurrounding pixels in fixed region, and it is able to describe feature point clearly under ununiform illumination condition. Anda proposed similarity measure is combined with conventional entropy-based similarity and another similarities that is generatedby local descriptor. It can reflect similarities between image regions accurately under radiometic illumination variations. We validatewith experimental results on some images and we confirm that the proposed algorithm is more superior than conventionalalgorithms.
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