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• W2044159596 abstract "영상에서 임의의 점에 대한 고유한 특징을 계산하는 알고리즘은 파노라마 영상의 제작, 스테레오 영상의 획득, 물체 인식, 이미지 분석 등에 다양하게 사용되는 중요한 요소이다. 일반적으로 어떤 점의 특징은 스칼라 형태가 아닌 벡터형태로 나타나게 되는데, 무수히 많은 특징 점들을 서로 비교하는 작업은 매우 많은 계산량을 요구한다. 본 연구에서는 영상의 특징점 계산에 SURF(speeded up robust features)를 이용하였고, 이미지로부터 추출된 특징을 PCA(principal component analysis)기법을 이용하여 벡터의 차원을 축소하여 연결리스트 자료구조에 정렬한 다음 특징을 비교하는 기법을 제안한다. 제안된 특징의 비교 방법을 적용할 경우 기존 방법의 매칭 정확도는 유지한 상태에서 계산시간을 줄일 수 있는 것을 실험을 통하여 확인하였다. Extracting unique features from an image is a fundamental issue when making panorama images, acquiring stereo images, recognizing objects and analyzing images. Generally, the task to compare features to other images requires much computing time because some features are formed as a vector which has many elements. In this paper, we present a method that compares features after reducing the feature dimension extracted from an image using PCA(principal component analysis) and sorting the features in a linked list. SURF(speeded up robust features) is used to describe image features. When the dimension reduction method is applied, we can reduce the computing time without decreasing the matching accuracy. The proposed method is proved to be fast and robust in experiments." @default.
• W2044159596 created "2016-06-24" @default.
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• W2044159596 date "2013-07-31" @default.
• W2044159596 modified "2023-10-03" @default.
• W2044159596 title "Speed Improvement of SURF Matching Algorithm Using Reduction of Searching Range Based on PCA" @default.
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• W2044159596 doi "https://doi.org/10.9717/kmms.2013.16.7.820" @default.
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