• Korean Journal of Remote Sensing
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• v.31 no.3
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• pp.205-214
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• 2015

In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.

• Korean journal of communication and information
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• v.35
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• pp.292-328
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• 2006

This study examined Korean Public Relations trend and social acceptance in 1960s. It centers on the acceptance conditions of PR concept, corporate PR and governmental PR. It analyzed PR recognition and practical activities of the general corporations, financial institutions and public corporations in dealing with corporate PR. It also analyzed Korean governmental PR through examining the structure of governmental PR system, PR activities in rural communities and the police. In those days, PR concept had been misused to no small degree and not been accepted correctly that the necessity to let people know PR in its proper meaning was raised. Laying emphasis upon the technical side, corporate PR activities had centered on publicity. Especially, at that time, the banks, the insurance companies and the public corporations had perceived the importance of PR and implemented a set of PR activities. It can be said that a special feature in government PR in 1960s lies in the expansion of the public information system and reinforcement of PR policies. Also, PR in rural communities had been doing to give information relating to agriculture technique and to improve the living conditions of the farm area. The importance of PR in police had been on the side of promoting relations with people and improving treatment of the people by policeman.

• The KIPS Transactions:PartB
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• v.9B no.3
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• pp.375-382
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• 2002

Handwritten character recognition is classified into on-line handwritten character recognition and off-line handwritten character recognition. On-line handwritten character recognition has made a remarkable outcome compared to off-line hacdwritten character recognition. This method can acquire the dynamic written information such as the writing order and the position of a stroke by means of pen-based electronic input device such as a tablet board. On the contrary, Any dynamic information can not be acquired in off-line handwritten character recognition since there are extreme overlapping between consonants and vowels, and heavily noisy images between strokes, which change the recognition performance with the result of the preprocessing. This paper proposes a method that effectively extracts the stroke including dynamic information of characters for off-line Korean handwritten character recognition. First of all, this method makes improvement and binarization of input handwritten character image as preprocessing procedure using watershed algorithm. The next procedure is extraction of skeleton by using the transformed Lu and Wang's thinning: algorithm, and segment pixel array is extracted by abstracting the feature point of the characters. Then, the vectorization is executed with a maximum permission error method. In the case that a few strokes are bound in a segment, a segment pixel array is divided with two or more segment vectors. In order to reconstruct the extracted segment vector with a complete stroke, the directional component of the vector is mortified by using right-hand writing coordinate system. With combination of segment vectors which are adjacent and can be combined, the reconstruction of complete stroke is made out which is suitable for character recognition. As experimentation, it is verified that the proposed method is suitable for handwritten Korean character recognition.

• Korean Institute of Interior Design Journal
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• v.20 no.3
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• pp.127-134
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• 2011

Interior design has diverse functions and roles according to recent changes in life and cultural style. As the meaning of space varies, the furniture as another element of the space should also be planned and included in the beginning of the project to accomplish its functional role on the finished space and not only be placed. The relationship between Interior space and furniture should be studied and understood at the same realm because they have a complementary relationship. The final destination of design, which is very challenging to accommodate people, the designer needs to approach to the interior space and furniture in a cognitive view. The study of Semiotics views in a knowledgeable and epistemological way every element as a sign and by the relationship of their characters it makes to understand the structure of the signs. Charles Sanders Peirce, a leader of Semiotics in USA, classified the structure of Sign into Representamen, Object and Interpretant to complete the theory which could systematically describe physical characteristic and function of the Sign and its significance in practical use. Peirce stated that all nine types of Sign would be generated if each structure of Sign in combined with three categories of Sign such as Firstness, Secondness, and Thirdness. In this study, based on Peirce's nine types of Sign, the interrelationship between space and furniture in interior design was investigated with examples in Living Space, Office Space, and Commercial Space, respectively. In the category of Representamen, which can be classified into Qualisign, Sinsign, and Legisign, it is a feature of Living Space to be expressed in harmonized and stabilized ways. In Office Space vertical/horizontal elements and open expression are differently showed depending on fields. And splendid and interesting expressions are showed in Commercial Space. In the category of Object, which can be classified into Icon, Index, and Symbol, each function of Sign has distinctly been showed as Symbol in Living Space, Index in Office Space, and Icon in Commercial Space, respectively. In the category of Interpretant, which can be classified into Rheme, Dicisign, and Argument, it was found that space image of Living Space is transferred to human being, Office Space regulates human bing, and Commercial Space binds human being with the space. In conclusion the function of the interior space and the furniture could be explained in another way with re-interpretation of interrelationship among interior space elements in the concept of Semiotics. It is expected that interpretation with Semiotics in interior design will grow to new theory of design.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.11
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• pp.305-313
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• 2019

Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.10 no.3
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• pp.249-255
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• 2017

The finger vein recognition technology is a method to acquire a finger vein image by illuminating infrared light to the finger and to authenticate a person through processes such as feature extraction and matching. In order to recognize a finger vein, a 2D mask-based two-dimensional convolution method can be used to detect a finger edge but it takes too much computation time when it is applied to a low cost micro-processor or micro-controller. To solve this problem and improve the recognition rate, this study proposed an extraction method for the region of interest based on virtual core points and moving average filtering based on the threshold and absolute value of difference between pixels without using 2D convolution and 2D masks. To evaluate the performance of the proposed method, 600 finger vein images were used to compare the edge extraction speed and accuracy of ROI extraction between the proposed method and existing methods. The comparison result showed that a processing speed of the proposed method was at least twice faster than those of the existing methods and the accuracy of ROI extraction was 6% higher than those of the existing methods. From the results, the proposed method is expected to have high processing speed and high recognition rate when it is applied to inexpensive microprocessors.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.407-414
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• 2015

Interest points are generally located at the pixels where height changes occur. So, interest points can be the significant pixels for DSM generation, and these have the important role to generate accurate and reliable matching results. Manual operation is widely used to extract the interest points and to match stereo satellite images using these for generating height information, but it causes economic and time consuming problems. Thus, a tie point extraction method using Harris-affine technique and SIFT(Scale Invariant Feature Transform) descriptors was suggested to analyze height information of buildings in this study. Interest points on buildings were extracted by Harris-affine technique, and tie points were collected efficiently by SIFT descriptors, which is invariant for scale. Searching window for each interest points was used, and direction of tie points pairs were considered for more efficient tie point extraction method. Tie point pairs estimated by proposed method was used to analyze height information of buildings. The result had RMSE values less than 2m comparing to the height information estimated by manual method.

• Journal of KIISE:Software and Applications
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• v.34 no.12
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• pp.1045-1055
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• 2007

Latent topic models are statistical models which automatically captures salient patterns or correlation among features underlying a data collection in a probabilistic way. They are gaining an increased popularity as an effective tool in the application of automatic semantic feature extraction from text corpus, multimedia data analysis including image data, and bioinformatics. Among the important issues for the effectiveness in the application of latent topic models to the massive data set is the efficient learning of the model. The paper proposes an accelerated learning technique for PLSA model, one of the popular latent topic models, by an incremental EM algorithm instead of conventional EM algorithm. The incremental EM algorithm can be characterized by the employment of a series of partial E-steps that are performed on the corresponding subsets of the entire data collection, unlike in the conventional EM algorithm where one batch E-step is done for the whole data set. By the replacement of a single batch E-M step with a series of partial E-steps and M-steps, the inference result for the previous data subset can be directly reflected to the next inference process, which can enhance the learning speed for the entire data set. The algorithm is advantageous also in that it is guaranteed to converge to a local maximum solution and can be easily implemented just with slight modification of the existing algorithm based on the conventional EM. We present the basic application of the incremental EM algorithm to the learning of PLSA and empirically evaluate the acceleration performance with several possible data partitioning methods for the practical application. The experimental results on a real-world news data set show that the proposed approach can accomplish a meaningful enhancement of the convergence rate in the learning of latent topic model. Additionally, we present an interesting result which supports a possible synergistic effect of the combination of incremental EM algorithm with parallel computing.

• 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 Korea Multimedia Society
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• v.13 no.7
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• pp.1044-1050
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• 2010

Endoscopic ultrasonography is a medical procedure in endoscopy combined with ultrasound to obtain images of the internal organs. It is useful to have a predictive pathological manifestation since a doctor can observe tumors under mucosa. However, it is often subjective to judge the degree of malignant degeneration of tumors. Thus, in this paper, we propose a feature analysis procedure to make the pathological manifestation more objective so as to improve the accuracy and recall of the diagnosis. In the process, we extract the ultrasound region from the image obtained by endoscopic ultrasonography. It is necessary to standardize the intensity of this region with the intensity of water region as a base since frequently found small intensity difference is only to be inefficient in the analysis. Then, we analyze the spot region with high echo and calcium deposited region by applying LVQ algorithm and bit plane partitioning procedure to tumor regions selected by medical expert. For detailed analysis, features such as intensity value, intensity information included within two random points chosen by medical expert in tumor region, and the slant of outline of tumor region in order to decide the degree of malignant degeneration. Such procedure is proven to be helpful for medical experts in tumor analysis.