정보처리학회논문지B (The KIPS Transactions:PartB)

한국정보처리학회 (Korea Information Processing Society)
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상대거리-곡률 특징 공간을 이용한 형태 기술 및 인식

Shape Description and Recognition Using the Relative Distance-Curvature Feature Space

  • 발행 : 2005.10.01
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초록

영상에 회전이나 크기 변형이 가해지면 영상을 구성하는 점들의 좌표값들이 변경되어 형태 기술 및 인식이 어렵게 된다. 그러나 영상을 구성하는 점들 간의 위치관계나 무게중심과의 위치 관계는 변하지 않는다. 따라서 x-y 좌표계로 기술되는 영상 공간의 점들을 회전 및 크기 변형에 불변하는 새로운 좌표계로 사상할 수 있다면, 형태 기술 및 인식의 문제는 보다 수월해진다. 본 논문에서는 영상 공간의 점들을 회전 및 크기 변형에 무관한 새로운 특징 공간으로 사상하여 형태를 기술하는 방법을 제안한다. 특징 공간을 나타내는 새로운 좌표계는 무게중심으로부터의 상대거리와 윤곽선 세그먼트 곡률을 두 축으로 하는 직교 좌표계이다. 상대거리는 윤곽선 상의 임의의 한 점이 무게중심에서 얼마나 멀리 벗어나 있는지를 나타내는 값이고, 윤곽선 세그먼트 곡률은 세그먼트의 굴곡도를 나타내는 값이다. 특징 공간에 사상된 점들의 형태 기술은 메쉬 특징을 통해 이루어진다. 실험을 통해 제안된 형태 기술 방법이 회전 및 크기 변형에 강건함을 확인하였다.

Rotation and scale variations make it difficult to solve the problem of shape description and recognition because these variations change the location of points composing the shape. However, some geometric Invariant points and the relations among them are not changed by these variations. Therefore, if points in image space depicted with the r-y coordinates system can be transformed into a new coordinates system that are invariant to rotation and scale, the problem of shape description and recognition becomes easier. This paper presents a shape description method via transformation from the image space into the invariant feature space having two axes: representing relative distance from a centroid and contour segment curvature(CSC). The relative distance describes how far a point departs from the centroid, and the CSC represents the degree of fluctuation in a contour segment. After transformation, mesh features were used to describe the shape mapped onto the feature space. Experimental results show that the proposed method is robust to rotation and scale variations.

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