• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.6
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• pp.146-153
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• 2010

For the indoor location, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor location which does not require any special equipments dedicated for positioning. As fingerprinting method,k-nearest neighbor(KNN) has been widely applied for indoor location in wireless location area networks(WLAN), but its performance is sensitive to number of neighborsk and positions of reference points(RPs). So possibilistic fuzzy c-means(PFCM) clustering algorithm is applied to improve KNN, which is the KNN/PFCM hybrid algorithm presented in this paper. In the proposed algorithm, through KNN,k RPs are firstly chosen as the data samples of PFCM based on signal to noise ratio(SNR). Then, thek RPs are classified into different clusters through PFCM based on SNR. Experimental results indicate that the proposed KNN/PFCM hybrid algorithm generally outperforms KNN and KNN/FCM algorithm when the locations error is less than 2m.

• Proceedings of the Korea Information Processing Society Conference
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• 2010.11a
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• pp.1708-1710
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• 2010

무선 네트워크 기반 실내 측위는 측위를 위한 특수 장비를 필요로 하지 않고, Fingerprinting 방식은 무선네트워크 기반 측위를 위한 기술 중에서 가장 정확도가 높기 때문에 무선 네트워크 Fingerprinting 방식이 가장 적당한 실내 측위 방법이다. Fingerprinting 방식에서 KNN은 WLAN 기반 실내 측위에 가장 많이 적용되고 있지만 KNN의 성능은 k개의 이웃 수와 RP의 수에 따라 민감하다. 논문에서는 KNN 성능을 향상시키기 위해 PFCM 군집화를 적용한 KNN과 PFCM을 혼합한 알고리즘을 제안하였다. 제안한 알고리즘은 신호잡음비 데이터를 KNN 방법에 적용하여 k개의 RP를 선택한 후 선택된 RP의 신호잡음비를 PFCM에 적용하여 k개의 RP를 군집하여 분류한다. 실험 결과에서는 위치 오차가 2m 이내에서 KNN/PFCM 알고리즘이 KNN과 KNN/FCM 알고리즘보다 성능이 우수하다.

• Journal of Korea Multimedia Society
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• v.11 no.11
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• pp.1501-1514
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• 2008

The face shape extracted by the depth values has different appearance as the most important facial information. The face images decomposed into frequency subband are signified personal features in detail. In this paper, we develop a method for recognizing the range face images by multiple frequency domains for each depth image using the modified fuzzy c-mean algorithm. For the proposed approach, the first step tries to find the nose tip that has a protrusion shape on the face from the extracted face area. And the second step takes into consideration of the orientated frontal posture to normalize. Multiple contour line areas which have a different shape for each person are extracted by the depth threshold values from the reference point, nose tip. And then, the frequency component extracted from the wavelet subband can be adopted as feature information for the authentication problems. The third step of approach concerns the application of eigenface to reduce the dimension. And the linear discriminant analysis (LDA) method to improve the classification ability between the similar features is adapted. In the last step, the individual classifiers using the modified fuzzy c-mean method based on the K-NN to initialize the membership degree is explained for extracted coefficient at each resolution level. In the experimental results, using the depth threshold value 60 (DT60) showed the highest recognition rate among the extracted regions, and the proposed classification method achieved 98.3% recognition rate, incase of fuzzy cluster.

• Journal of Intelligence and Information Systems
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• v.8 no.2
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• pp.1-13
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• 2002

In this paper, we propose an object tracking system which can be convinced of moving area shaped on objects through color sequential images, decided moving directions of foot messengers or vehicles of image sequences. In static camera, we suggests a new evaluating method extracting co-occurrence matrix with feature vectors of RGB after analyzing and blocking difference images, which is accessed to field of camera view for motion. They are energy, entropy, contrast, maximum probability, inverse difference moment, and correlation of RGB color vectors. we describe how to analyze and compute corresponding relations of objects between adjacent frames. In the clustering, we apply an algorithm of FCM(fuzzy c means) to analyze matching and clustering problems of adjacent frames of the featured vectors, energy and entropy, gotten from previous phase. In the matching phase, we also propose a method to know correspondence relation that can track motion each objects by clustering with similar area, compute object centers and cluster around them in case of same objects based on membership function of motion area of adjacent frames.