• Title/Summary/Keyword: 볼록 외피

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The Center of Hand Detection Using Geometric feature of Hand Image (손 이미지의 기하학적 특징을 이용한 중심 검출)

  • Kim, Min-Ha;Lee, Sang-Geol;Cho, Jae-Hyun;Cha, Eui-Young
    • Proceedings of the Korean Society of Computer Information Conference
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    • 2012.07a
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    • pp.311-313
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    • 2012
  • 본 논문에서는 RGBD(Red Green Blue Depth)센서를 이용하여 얻은 영상의 깊이 정보와 손 이미지의 기하학적 특징을 이용하여 손의 중심을 검출하는 방법을 제안한다. 영상의 깊이 정보와 피부색 정보를 이용하여 손 영역을 검출한다. 검출된 손의 기하학적 정보로 손에 대한 볼록 외피(convex hull)를 형성한다. 볼록 외피의 정점들(vertices)의 위치 정보를 이용하여 손의 중심을 찾는다. 손의 중심은 손의 위치를 추적하거나 손가락 개수를 구하는 것 등에 이용될 수 있다. 이러한 응용은 인간과 컴퓨터의 상호작용(HCI, Human Computer Interface)을 이용한 시스템에 적용될 수 있다.

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A Convex Layer Tree for the Ray-Shooting Problem (광선 슈팅 문제를 위한 볼록 레이어 트리)

  • Kim, Soo-Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.21 no.4
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    • pp.753-758
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    • 2017
  • The ray-shooting problem is to find the first intersection point on the surface of given geometric objects where a ray moving along a straight line hits. Since rays are usually given in the form of queries, this problem is typically solved as follows. First, a data structure for a collection of objects is constructed as preprocessing. Then, the answer for each query ray is quickly computed using the data structure. In this paper, we consider the ray-shooting problem about the set of vertical line segments on the x-axis. We present a new data structure called a convex layer tree for n vertical line segments given by input. This is a tree structure consisting of layers of convex hulls of vertical line segments. It can be constructed in O(n log n) time and O(n) space and is easy to implement. We also present an algorithm to solve each query in O(log n) time using this data structure.

A Fast Shortest Path Algorithm Between Two Points inside a Segment-Visible Polygon (선분가시 다각형 내부에 있는 두 점 사이의 최단 경로를 구하는 빠른 알고리즘)

  • Kim, Soo-Hwan
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.2
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    • pp.369-374
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    • 2010
  • The shortest path between two points inside a simple polygon P is a minimum-length path among all paths connecting them which don't pass by the exterior of P. A linear time algorithm for computing the shortest path in a general simple polygon requires triangulating a polygon as preprocessing. The linear time triangulating is known to very complex to understand and implement it. It is also inefficient in case that the input without very large size is given because its time complexity has a big constant factor. In this paper, we present the customized shortest path algorithm for a segment-visible polygon which is a simple polygon weakly visible from an internal line segment. Our algorithm doesn't require triangulating as preprocessing and consists of simple procedures such as construction of convex hulls, so it is easy to implement and runs very fast in linear time.

Handwritten Numeral Recognition using Composite Features and SVM classifier (복합특징과 SVM 분류기를 이용한 필기체 숫자인식)

  • Park, Joong-Jo;Kim, Tae-Woong;Kim, Kyoung-Min
    • Journal of the Korea Institute of Information and Communication Engineering
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    • v.14 no.12
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    • pp.2761-2768
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    • 2010
  • In this paper, we studied the use of the foreground and background features and SVM classifier to improve the accuracy of offline handwritten numeral recognition. The foreground features are two directional features: directional gradient feature by Kirsch operators and directional stroke feature by projection runlength, and the background feature is concavity feature which is extracted from the convex hull of the numeral, where concavity feature functions as complement to the directional features. During classification of the numeral, these three features are combined to obtain good discrimination power. The efficiency of our feature sets was tested by recognition experiments on the handwritten numeral database CENPARMI, where we used SVM with RBF kernel as a classifier. The experimental results showed that each combination of two or three features gave a better performance than a single feature. This means that each single feature works with a different discriminating power and cooperates with other features to enhance the recognition accuracy. By using the composite feature of the three features, we achieved a recognition rate of 98.90%.