• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2003.04a
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• pp.500-509
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• 2003

A land-speculation is to occur on a space. A purpose of this study interprets land speculation in a viewpoint of spatial, and to carry out effective decision-making about areas that land-speculation can be arise At the time of this, the focus of spatial interpretation Is recognition of a spatial continuity and consideration of a spatial association. In this study, I used fuzzy sets in order to recognize spatial continuity and. therefore a value in 0-1 was granted all area. And in order to consider spatial associations. carried out a local spatial association (Local Moran's I). Also. I introduced the spatial expert support systems (SESS) one of the computer-based decision support systems to dr efficient decision-making about a land-speculation in an object area. After that, as a case study I carried out decision-making about land-speculation's occurrence in the Seoul-Kyeonggi (2/4 quarter in 2001)

• Korean Journal of Remote Sensing
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• v.24 no.2
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• pp.117-124
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• 2008

This paper introduces a novel approach for automated mapping of a map feature that is vinyl green house in high spatial resolution imagery Some map features have their unique spatial patterns. These patterns are normally detected in high spatial resolution remotely sensed data by human recognition system. When spatial patterns can be applied to map feature identification, it will improve image classification accuracy and will be contributed a lot to feature identification. In this study, an automated feature identification approach using spatial aucorrelation is developed, specifically for the vinyl green house that has distinctive spatial pattern in its array. The algorithm aimed to develop the method without any human intervention such as digitizing. The method can investigate the characteristics of repeated spatial pattern of vinyl green house. The repeated spatial pattern comes from the orderly array of vinyl green house. For this, object-based approaches are essential because the pattern is recognized when the shapes that are consists of the groups of pixels are involved. The experimental result shows very effective vinyl house extraction. The targeted three vinyl green houses were exactly identified in the IKONOS image for a part of Jeju area.

• Journal of Korea Technology Innovation Society
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• v.15 no.3
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• pp.604-626
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• 2012

As a society of knowledge and information has been developed rapidly, because of changing from web environment to ubiquitous environment, a lot of countries across the world as well as S. Korea for e-Government have come to use the internet electronic voting for a variety of elections. So this research focused on the strategy consulting of the internet electronic voting of S. Korea with spatial information system analysed by the application of 'scenario planning' as a foresight method. And as a consequence, the strategy formulation of the electronic voting for the future S. Korea is to use the biometrics technology system as vein recognition and face recognition, using a part of the human body like a password, with spatial information system.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.9
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• pp.1123-1131
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• 2020

The quick-response (QR) code is a two-dimensional barcode which is widely being used. Due to several interesting features such as small code size, high error correction capabilities, easy code generation and reading process, the QR codes are used in many applications. Nowadays, a printed color QR code for offline applications is being studied to improve the information storage capacity. By multiplexing color information into the conventional black-white QR code, the storage capacity is increased, however, it is hard to extract the embedded information due to the color crosstalk and geometrical distortion. In this paper, to overcome these problems, a new type of QR code is designed based on the CMYK color model and the local spatial searching as well as the global spatial matching is introduced in the reading process. These results in the recognition rate increase. Through practical experiments, it is shown that the proposed algorithm can perform the bit recognition rate improvement of about 3% to 5%.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.19-26
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• 2022

With the recent development of intelligent transportation systems, car license plate recognition systems are being used in various fields. Such systems need to guarantee real-time performance to recognize the license plate of a driving car. Also, they should keep a high recognition rate even in problematic situations such as small license plates in low-resolution and unclear image due to distortion. In this paper, we propose a real-time car license plate recognition system that improved processing speed using object detection algorithm based on anchor-free method and text recognition algorithm based on Convolutional Neural Network(CNN). In addition, we used Spatial Transformer Network to increase the recognition rate on the low resolution or distorted images. We confirm that the proposed system is faster than previously existing car license plate recognition systems and maintains a high recognition rate in a variety of environment and quality images because the proposed system's recognition rate is 93.769% and the processing speed per image is about 0.006 seconds.

• Korean Institute of Interior Design Journal
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• v.20 no.5
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• pp.143-151
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• 2011

Since it is important to understand data acquiring characteristics through relationship between spatial types and spatial elements and apply it to spatial plans for smooth communication between designer and user of space, the conclusions gained from analysis of data acquiring characteristics of spatial elements through image evaluation by types of interior space can be summarized as in the followings: First, for the amount of acquired data by types of interior space, it shows that the acquired amount of data is to change by types and data acquiring method (phrase and image) even though the spatial elements are same. Second, for the data acquiring process of spatial types by gender, it shows that there is a big difference in acquiring of data according to the evaluation method by phrase and image. Third, for the amount of acquired data of spatial types by gender, it shows that there is a difference between male and female, which is by "classic ${\rightarrow}$ modern ${\rightarrow}$ natural" in case of male and "classic ${\rightarrow}$ natural ${\rightarrow}$ modern" in case of female. regarding both of phrase and image. Fourth, for the evaluation by gender, it shows that there is a deviation in the value of difference according to the elements by which data acquiring characteristics evaluate space. It is considered that this deviation characteristic is in need of reflection in the process of spatial evaluation. This study analyzed data acquiring characteristics of space user's spatial elements through image evaluation by types of space to understand how data acquiring would be changed of spatial elements according to type and gender. Through this study, it expects to make clear that, when a designer is planning a certain space, if the space can be a space for the user by understanding of which elements should be exposed to users by types to acquire more data.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.2118-2125
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• 2014

Gait Recognition is a new technique to identify the people by the way they walk. Human gait is a spatio-temporal phenomenon that typifies the motion characteristics of an individual. The proposed method makes a simple but efficient attempt to gait recognition. For each video file, spatial silhouettes of a walker are extracted by an improved background subtraction procedure using Gaussian Mixture Model (GMM). Here GMM is used as a parametric probability density function represented as a weighted sum of Gaussian component densities. Then, the relevant features are extracted from the silhouette tracked from the given video file using the Principal Component Analysis (PCA) method. The Fisher Linear Discriminant Analysis (FLDA) classifier is used in the classification of dimensional reduced image derived by the PCA method for gait recognition. Although gait images can be easily acquired, the gait recognition is affected by clothes, shoes, carrying status and specific physical condition of an individual. To overcome this problem, it is combined with footprint as a multimodal biometric system. The minutiae is extracted from the footprint and then fused with silhouette image using the Discrete Stationary Wavelet Transform (DSWT). The experimental result shows that the efficiency of proposed fusion algorithm works well and attains better result while comparing with other fusion schemes.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.1
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• pp.272-287
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• 2017

This paper proposes new methods, named Derivative Code (DerivativeCode) and Derivative Code Pattern (DCP), for object recognition. The discriminative derivative code is used to capture the local relationship in the input image by concatenating binary results of the mathematical derivative value. Gabor based DerivativeCode is directly used to solve the palmprint recognition problem, which achieves a much better performance than the state-of-art results on the PolyU palmprint database. A new local pattern method, named Derivative Code Pattern (DCP), is further introduced to calculate the local pattern feature based on Dervativecode for object recognition. Similar to local binary pattern (LBP), DCP can be further combined with Gabor features and modeled by spatial histogram. To evaluate the performance of DCP and Gabor-DCP, we test them on the FERET and PolyU infrared face databases, and experimental results show that the proposed method achieves a better result than LBP and some state-of-the-arts.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.5
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• pp.1856-1869
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• 2015

This paper addresses the issues of 3D human activity detection, tracking and recognition from RGB-D video sequences using a feature structured framework. During human tracking and activity recognition, initially, dense depth images are captured using depth camera. In order to track human silhouettes, we considered spatial/temporal continuity, constraints of human motion information and compute centroids of each activity based on chain coding mechanism and centroids point extraction. In body skin joints features, we estimate human body skin color to identify human body parts (i.e., head, hands, and feet) likely to extract joint points information. These joints points are further processed as feature extraction process including distance position features and centroid distance features. Lastly, self-organized maps are used to recognize different activities. Experimental results demonstrate that the proposed method is reliable and efficient in recognizing human poses at different realistic scenes. The proposed system should be applicable to different consumer application systems such as healthcare system, video surveillance system and indoor monitoring systems which track and recognize different activities of multiple users.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.12
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• pp.5464-5484
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• 2016

Few samples face recognition has become a highly challenging task due to the limitation of available labeled samples. As two popular paradigms in face image representation, sparse component analysis is highly robust while parts-based paradigm is particularly flexible. In this paper, we propose a probabilistic generative model to incorporate the strengths of the two paradigms for face representation. This model finds a common spatial partition for given images and simultaneously learns a sparse component analysis model for each part of the partition. The two procedures are built into a probabilistic generative model. Then we derive the score function (i.e. feature mapping) from the generative score space. A similarity measure is defined over the derived score function for few samples face recognition. This model is driven by data and specifically good at representing face images. The derived generative score function and similarity measure encode information hidden in the data distribution. To validate the effectiveness of the proposed method, we perform few samples face recognition on two face datasets. The results show its advantages.