• Journal of the Institute of Electronics Engineers of Korea SP
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• v.40 no.3
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• pp.203-216
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• 2003

In this paper a new feature extraction and target classification method is proposed for the recognition part of FLIR(Forwar Looking Infrared)-image-based ATR system. Proposed feature extraction method is "cluster(=set of classes)-based"version of previous fisherfaces method that is known by its robustness to illumination changes in face recognition. Expecially introduced class clustering and cluster-based projection method maximizes the performance of fisherfaces method. Proposed target image classification method is based on the mixture of experts model which consists of RBF-type experts and MLP-type gating networks. Mixture of experts model is well-suited with ATR system because it should recognizee various targets in complexed feature space by variously mixed conditions. In proposed classification method, one expert takes charge of one cluster and the separated structure with experts reduces the complexity of feature space and achieves more accurate local discrimination between classes. Proposed feature extraction and classification method showed distinguished performances in recognition test with customized. FLIR-vehicle-image database. Expecially robustness to pixelwise sensor noise and un-wanted intensity variations was verified by simulation.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.3
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• pp.268-276
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• 1992

In this paper, we present an discrimination method of unknown digital modulated signals in noisy communication environment. We propose the use of an identification procedure based on time domain signal parameters. First, We extract instantaneous envelope. Frequency and difference phase as the basic feature informations from received signals. In order to identify signals using the extracted feature informations, we design the two dimensional feature space. The extracted feature infomations are mapped into2Dfeature space using 2D feature points. The procedure has been tested by simulations on a computer in noisy communication environment, and the considered signals are ASK-W, ASK-4, BPSK, QPSK, 8PSK, FSK, and QAM.

• Journal of the Korea Society of Computer and Information
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• v.13 no.1
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• pp.117-124
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• 2008

We suggest CBIR(Content Based Image Retrieval) method using color and shape information. Using just one feature information may cause inaccuracy compared with using more than two feature information. Therefore many image retrieval system use many feature informations like color, shape and other features. We use two feature, HSI color information especially Hue value and CSS(Curvature Scale Space) as shape information. We search candidate image form DB which include feature information of many images. When we use two features, we could approach better result.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.2
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• pp.473-476
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• 2005

This paper is proposed a robust various feature parameters in noise. Feature parameter MFCC(Mel Frequency Cepstral Coefficient) used in conventional speech recognition shows good performance. But, parameter transformed feature space that uses PCA(Principal Component Analysis)and ICA(Independent Component Analysis) that is algorithm transformed parameter MFCC's feature space that use in old for more robust performance in noise is compared with the conventional parameter MFCC's performance. The result shows more superior performance than parameter and MFCC that feature parameter transformed by the result ICA is transformed by PCA.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.8
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• pp.759-766
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• 2009

The purpose of this paper is an image processing algorithm development as a base research achieving performance enhancement of integrated navigation system. We used the SIFT (Scale Invariant Feature Transform) algorithm for image processing, and developed feature point matching filter for rejecting mismatched points. By applying the proposed algorithm, it is obtained better result than other methods of parameter tuning and KLT based feature point tracking. For further study, integration with INS and algorithm optimization for the real-time implementation are under investigation.

• Korean Institute of Interior Design Journal
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• v.25 no.6
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• pp.52-60
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• 2016

Observation about space is looked steady in an instant, but in continuous movement, one's observation unconsciously stays at different points. In department store, customer actually observes around the store for buying, not focusing on certain point. By studying customer's movement and observation feature, buying desire and interest can be found. For analysis of the different searching-features according to the continuous-observation depending on sex, the study is set up to record movements of customers at women in Department store. The following are the findings. First, Men observed 0.2-0.4 units more in I-II section which are assumed as predominant. The result shows that men can focus on more section (around +0.4%) and longer (around +5.7%) than women do. Second, the same feature of observation depending on sex is that both men and women observe left and right section while keep focusing on middle section. Third, according to the fact that right-focused observation magnificently occurred in the image curved to right, the Space-composition has influenced on the observation of both men and women on the space. Forth, excessive number of display can cause avoidance of observation. Moreover, observation does not stay on the coverage due to wall or post, but is attracted to the brand name. As brand name causes right-focused observation in the image [(8)], brand name can be one of the reasons to attract observation in women apparel store. To sum up, this study is noticeable as it researches about continuous-observation. Furthermore, verifying the result that the composition of space and the placement of products can cause big differences in the observation feature is meaningful outcome.

• ETRI Journal
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• v.40 no.4
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• pp.499-510
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• 2018

A vector space based augmented structural kinematic (VSASK) feature descriptor is proposed for human activity recognition. An action descriptor is built by integrating the structural and kinematic properties of the actor using vector space based augmented matrix representation. Using the local or global information separately may not provide sufficient action characteristics. The proposed action descriptor combines both the local (pose) and global (position and velocity) features using augmented matrix schema and thereby increases the robustness of the descriptor. A multiclass support vector machine (SVM) is used to learn each action descriptor for the corresponding activity classification and understanding. The performance of the proposed descriptor is experimentally analyzed using the Weizmann and KTH datasets. The average recognition rate for the Weizmann and KTH datasets is 100% and 99.89%, respectively. The computational time for the proposed descriptor learning is 0.003 seconds, which is an improvement of approximately 1.4% over the existing methods.

• Journal of the Korean housing association
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• v.8 no.2
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• pp.135-145
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• 1997

The purpose of this study was to investigate the space feature of private kindergarten in Taegu city. This study was conducted by means of the observation on the equipments, the actual measurement of space of kindergarten and environment, and the questionnaire survey by the chief of kindergarten. The samples for analysis were 20 kindergartens on Taegu city. The Major findings were as follows :1) The chiefs of kindergartens were generally satisfied with the whole range of institutions and it's management.2) Generally the environmental coditions were satisfactory, but the design of the equipments had no consideration for children's body size.3) According to observation on the equipment, it was found that generally environment of kindergarten were desireable.

• Journal of KIISE:Software and Applications
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• v.26 no.1
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• pp.150-150
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• 1999

In this paper, a content-based image retrieval scheme based on scale-space theory is proposed. The existing methods using scale-space theory consider all scales for image retrieval,thereby requiring a lot of computation. To overcome this problem, the proposed algorithm utilizes amodified histogram intersection method to select candidate images from database. The relative scalebetween a query image and a candidate image is calculated by the ratio of histograms. Feature pointsare extracted from the candidates using a corner detection algorithm. The feature vector for eachfeature point is composed of RGB color components and differential invariants. For computing thesimilarity between a query image and a candidate image, the euclidean distance measure is used. Theproposed image retrieval method has been applied to various images and the performance improvementover the existing methods has been verified.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.1
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• pp.101-108
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• 2010

In this paper, we proposed a new lazy classifier with fuzzy k-nearest neighbors approach and feature selection which is based on reconstruction error. Reconstruction error is the performance index for locally linear reconstruction. When a new query point is given, fuzzy k-nearest neighbors approach defines the local area where the local classifier is available and assigns the weighting values to the data patterns which are involved within the local area. After defining the local area and assigning the weighting value, the feature selection is carried out to reduce the dimension of the feature space. When some features are selected in terms of the reconstruction error, the local classifier which is a sort of polynomial is developed using weighted least square estimation. In addition, the experimental application covers a comparative analysis including several previously commonly encountered methods such as standard neural networks, support vector machine, linear discriminant analysis, and C4.5 trees.