• Journal of Korea Society of Industrial Information Systems
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• v.11 no.1
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• pp.77-85
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• 2006

In this paper, we propose a hierarchical classifier of object images using neural networks for content-based image classification. The images for classification are object images that can be divided into foreground and background. In the preprocessing step, we extract the object region and shape-based texture features extracted from wavelet transformed images. We group the image classes into clusters which have similar texture features using Principal Component Analysis(PCA) and K-means. The hierarchical classifier has five layes which combine the clusters. The hierarchical classifier consists of 59 neural network classifiers learned with the back propagation algorithm. Among the various texture features, the diagonal moment was the most effective. A test with 1000 training data and 1000 test data composed of 10 images from each of 100 classes shows classification rates of 81.5% and 75.1% correct, respectively.

• Journal of Sensor Science and Technology
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• v.22 no.2
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• pp.162-173
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• 2013

The purpose of this paper is to classify VOC gases by emulating the characteristics found in biological olfaction. For this purpose, we propose new signal processing method based a polymeric chemical sensor array consisting of 4096 sensors which is created by NEUROCHEM project. To remove unstable sensors generated in the manufacturing process of very large scaled chemical sensor array, we used discrete wavelet transformation and cosine similarity. And, to remove the supernumerary redundancy, we proposed the method of selecting candidates of representative sensor representing sensors with similar features by Fuzzy c-means algorithm. In addition, we proposed an improved algorithm for selecting representative sensors among candidates of representative sensors to better enhance classification ability. However, Classification for very large scaled sensor array has a great deal of time in process of learning because many sensors are used for learning though a redundancy is removed. Throughout experimental trials for classification, we confirmed the proposed method have an outstanding classification ability, at transient state as well as steady state.

• 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 the Korea Society of Computer and Information
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• v.16 no.10
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• pp.83-92
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• 2011

The aim of this study is to evaluate whether 3D nuclear chromatin texture features are significant in recognizing the progression of cervical cancer. In particular, we assessed that our method could detect subtle differences in the chromatin pattern of seemingly normal cells on specimens with malignancy. We extracted nuclear texture features based on 3D GLCM(Gray Level Co occurrence Matrix) and 3D Wavelet transform from 100 cell volume data for each group (Normal, LSIL and HSIL). To evaluate the feasibility of 3D chromatin texture analysis, we compared the correct classification rate for each of the classifiers using them. In addition to this, we compared the correct classification rates for the classifiers using the proposed 3D nuclear texture features and the 2D nuclear texture features which were extracted in the same way. The results showed that the classifier using the 3D nuclear texture features provided better results. This means our method could improve the accuracy and reproducibility of quantification of cervical cell.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.12
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• pp.1460-1466
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• 2010

In this paper, we analyzed the performance of radar target classification using the monostatic and bistatic radar cross section(RCS) for four different wire targets. Short time Fourier transform(STFT) and continuous wavelet transform (CWT) were used for feature extraction from the monostatic RCS and the bistatic RCS of each target, and a multi-layered perceptron(MLP) neural network was used as a classifier. Results show that CWT yields better performance than STFT for both the monostatic RCS and the bistatic RCS. And, when STFT was used, the performance of the bistatic RCS was slightly better than that of the monostatic RCS. However, when CWT was used, the performance of the monostatic RCS was slightly better than that of the bistatic RCS. Resultingly, it is proven that bistatic RCS is a good cadndidate for application to radar target classification in combination with a monostatic RCS.

• Journal of the Institute of Electronics and Information Engineers
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• v.52 no.8
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• pp.117-125
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• 2015

In this paper, we propose a classification algorithm based on the obtained EEG(Electroencephalogram) signal for the control of 'left' and 'right' turnings of which a driving system composed of EEG sensor, Labview, DAQ, Matlab and driving robot. The proposed algorithm uses features extracted from frequency band information obtained by DWT (Discrete Wavelet Transform) and selects features of high discrimination by using Fisher score. We, also propose the number of feature vectors for the best classification performance by using SVM(Support Vector Machine) classifier and propose a decision pending algorithm based on MLD (Maximum Likelihood Decision) to prevent malfunction due to misclassification. The selected four feature vectors for the proposed algorithm are the mean of absolute value of voltage and the standard deviation of d5(2-4Hz) and d2(16-32Hz) frequency bands of P8 channel according to the international standard electrode placement method. By using the SVM classifier, we obtained 98.75% accuracy and 1.25% error rate. Also, when we specify error probability of 70% for decision pending, we obtained 95.63% accuracy and 0% error rate by using the proposed decision pending algorithm.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.53 no.4
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• pp.293-299
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• 2004

This paper is about the extraction of basis function for ECG signal processing. In the first step, it is assumed that ECG signal consists of linearly mixed independent source signals. 12 channel ECG signals, which were sampled at 600sps, were used and the basis function, which can separate and detect source signals - QRS complex, P and T waves, - was found by applying the fast fixed point algorithm, which is one of learning algorithms in independent component analysis(ICA). The possibilities of significant point detection and classification of normal and abnormal ECG, using the basis function, were suggested. Finally, the proposed method showed that it could overcome the difficulty in separating specific frequency in ECG signal processing by wavelet transform. And, it was found that independent component analysis(ICA) could be applied to ECG signal processing for detection of significant points and classification of abnormal beats.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• Proceedings of the IEEK Conference
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• summer
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• pp.237-243
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• 2004

A multichannel smart sound sensor capable to detect and identify sound events in noisy conditions is presented in this paper. Sound information extraction is a complex task and the main difficulty consists is the extraction of high­level information from an one-dimensional signal. The input of smart sound sensor is composed of data collected by 5 microphones and its output data is sent through a network. For a real time working purpose, the sound analysis is divided in three steps: sound event detection for each sound channel, fusion between simultaneously events and sound identification. The event detection module find impulsive signals in the noise and extracts them from the signal flow. Our smart sensor must be capable to identify impulsive signals but also speech presence too, in a noisy environment. The classification module is launched in a parallel task on the channel chosen by data fusion process. It looks to identify the event sound between seven predefined sound classes and uses a Gaussian Mixture Model (GMM) method. Mel Frequency Cepstral Coefficients are used in combination with new ones like zero crossing rate, centroid and roll-off point. This smart sound sensor is a part of a medical telemonitoring project with the aim of detecting serious accidents.

• Journal of Advanced Navigation Technology
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• v.10 no.3
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• pp.219-225
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• 2006

This paper presents a method of object classification based on discrete wavelet transform (DWT) and fuzzy inference(FI). It concentrated not only on the design of fuzzy inference algorithm which is suitable for low speed uninhabited transportation such as, conveyor but also on the minimize the number of fuzzy rule. In the preprocess of feature extracting, feature parameters are extracted by using characteristics of the coefficients matrix of DWT. Such feature parameters as area, perimeter and a/p ratio are used obtained from DWT coefficients blocks. Secondly, fuzzy if - then rules that can be able to adapt the variety of surroundings are developed. In order to verify the performance of proposed scheme, In the middle of fuzzy inference, the Mamdani's and the Larsen 's implication operators are utilized. Experimental results showed that proposed scheme can be applied to the variety of surroundings.