왜곡 영상을 위한 효과적인 최소-최대 유사도(Min- Max Similarity) 기반의 영상 정합 알고리즘
An Efficient Image Matching Scheme Based on Min-Max Similarity for Distorted Images
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초록

Educational books commonly use some copyrighted images with various kinds of deformation for helping students understanding. When using several copyrighted images made by merging or editing distortion in legal, we need to pay a charge to original copyright holders for each image. In this paper, we propose an efficient matching algorithm by separating each copyrighted image with the merged and edited type including rotation, illumination change, and change of size. We use the Oriented FAST and Rotated BRIEF (ORB) method as a basic feature matching scheme. To improve the matching accuracy, we design a new MIN-MAX similarity in matching stage. With the distorted dataset, the proposed method shows up-to 97% of precision in experiments. Also, we demonstrate that the proposed similarity measure also outperforms compared to other measure which is commonly used.

키워드

Image ProcessingImage MatchingSimilarity MeasureMIN-MAX SimilarityDistorted Images
제목
왜곡 영상을 위한 효과적인 최소-최대 유사도(Min- Max Similarity) 기반의 영상 정합 알고리즘
제목 (타언어)
An Efficient Image Matching Scheme Based on Min-Max Similarity for Distorted Images
저자
허영진정다미김병규
DOI
10.9717/kmms.2019.22.12.1404
발행일
2019-12
저널명
멀티미디어학회논문지
22
12
페이지
1404 ~ 1414