• 전자공학회논문지SC
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• 제41권5호
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• pp.9-18
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• 2004

본 논문에서는 펄스레이더를 이용한 차량검지 알고리즘을 제안하였다. 제안된 알고리즘은 likelihood개념을 이용한 likelihood ration를 통해 하나의 펄스신호에서 분류위치를 추출하고 추출된 분리점을 기준으로 신호를 분리, 간략화 하였다. 이렇게 처리 된 펄스신호에서 연속된 신호의 유클리드거리를 이용하여 단순 군집 탐색 알고리즘에 의해 차량을 검지하였고, 실험을 통해 제안된 알고리즘의 유용성을 확인하였다.

• 대한원격탐사학회지
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• 제20권5호
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• pp.353-359
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• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.