• Title/Summary/Keyword: Projection Profile Cutting

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Effect of Tool Angles on Surface Roughness in Face milling (정면밀링에서 공구각이 표면거칠기에 미치는 영향)

  • 이호연
    • Proceedings of the Korean Society of Machine Tool Engineers Conference
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    • 1998.10a
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    • pp.26-31
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    • 1998
  • The effect of tool angles on the surface roughness in face milling is studied. First, the relation between tool angles and rotation angles is identified. Using this relationship, it is obtained that the projection of insert nose shape on cutting profile, which is a part of ellipse. The effect of spindle tilt is also considered for the tool angles. It si shown that tool angles along with nose radius and feed rate have an effect on surface roughness.

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Subimage Detection of Window Image Using AdaBoost (AdaBoost를 이용한 윈도우 영상의 하위 영상 검출)

  • Gil, Jong In;Kim, Manbae
    • Journal of Broadcast Engineering
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    • v.19 no.5
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    • pp.578-589
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    • 2014
  • Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.