• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.3
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• pp.37-44
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• 2019

In this study, we propose the automatic extraction method of Rescue Requests from Drone Images. A central object is extracted from each image by using central object extraction method[7] before classification. A central object in an images are defined as a set of regions that is lined around center of the image and has significant texture distribution against its surrounding. In this case of artificial objects, edge of straight line is often found, and texture is regular and directive. However, natural object's case is not. Such characteristics are extracted using Edge direction histogram energy and texture Gabor energy. The Edge direction histogram energy calculated based on the direction of only non-circular edges. The texture Gabor energy is calculated based on the 24-dimension Gebor filter bank. Maximum and minimum energy along direction in Gabor filter dictionary is selected. Finally, the extracted rescue requestor object areas using the dominant features of the objects. Through experiments, we obtain accuracy of more than 75% for extraction method using each features.

• Journal of Korea Multimedia Society
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• v.7 no.12
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• pp.1657-1664
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• 2004

We propose a method that classifies images into two object types man-made and natural objects. A central object is extracted from each image by using central object extraction method[1] before classification. A central object in an images defined as a set of regions that lies around center of the image and has significant color distribution against its surrounding. We define three measures to classify the object images. The first measure is energy of edge direction histogram. The energy is calculated based on the direction of only non-circular edges. The second measure is an energy difference along directions in Gabor filter dictionary. Maximum and minimum energy along directions in Gabor filter dictionary are selected and the energy difference is computed as the ratio of the maximum to the minimum value. The last one is a shape of an object, which is also represented by Gabor filter dictionary. Gabor filter dictionary for the shape of an object differs from the one for the texture in an object in which the former is computed from a binarized object image. Each measure is combined by using majority rule tin which decisions are made by the majority. A test with 600 images shows a classification accuracy of 86%.

• Proceedings of the Korea Multimedia Society Conference
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• 2004.05a
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• pp.656-659
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• 2004

최근 많은 연구에서, 동일한 영상그룹들로부터 추출된 저수준의 특징들을 이용해서 고수준의 정보를 분석한 뒤, 이를 이용해서 영상을 분류하는 방법들을 소개하고 있다. 이러한 연구는 CBIR의 인덱싱에서 저수준의 특징만을 사용할 때 발생하는 의미적인 차이(semantic gap)문제를 해결하여, 검색의 효율을 높일 수 있게 한다. 하지만 이들 연구는 대부분 전경(scenery)영상만을 대상으로 하고 있다. 한편 영상을 객체 단위로 다루는 것은 CBIR의 성능을 크게 향상 시킬 수 있는 요인이 된다. 왜냐하면 대부분의 사용자는 관심있는 객체가 포함된 영상을 검색하기 원하기 때문이다. 본 논문에서는 영상의 객체를 인공객체와 자연객체로 분류하는 방법을 제안한다. 인공객체의 경우 자연객체에 비해 상대적으로 직선형태의 에지가 많이 발견되며 객체를 구성하는 패턴이 규칙적이고 방향성을 가진다. 또한 인공객체는 자연객체에 비해 객체영역의 경계가 직선에 의한 단순한 형태로 나타난다. 이러한 특징들을 EDH(edge Direction Histogram)의 에너지, EDAS(Energy Difference of Adjacent Sector)와 가버 필터를 통해 추출하여 분류에 이용한다. 실험을 통하여 각 특징들을 개별적으로 사용해서 76%에서 84% 사이의 분류 정확성을 얻었으며, 제안한 머징 방법을 이용하여 최종적으로 약 90%의 정확성으로 분류하였다.