• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.19 no.2
• /
• pp.109-115
• /
• 2001

Recently, in accordance with the introduction of Digital Photogrammetry Systems the use of Digital ortho-photo images have increased and progressed in the study which extract the features from digital ortho-photo image semi-automatically or automatically. However, there are a limit. It has proved in many studies that recognition of the attribution or the features from panchromatic aerial photo is restricted. In this study, I compared color aerial images with panchromatic aerial images and analyzed the characteristics of color aerial images and feature entities which can be extracted semi-automatically. I analyzed extracted feature entities are compared with digital map at a scale of 1:5,000 have constructed in National Geography Institute. With this result, I analyzed the capability of feature extraction and proposed a plan for the study in the future.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.47 no.3
• /
• pp.36-41
• /
• 2010

An advanced form of Multistage Feature-based Classification Model(AMFCM), called AMFCM, is proposed in this paper. AMFCM like MFCM does not use the concatenated form of available feature vectors extracted from original data to classify each data, but uses only groups related to each feature vector to classify separately. The prpposed AMFCM improves the contribution rate used in MFCM and proposes a confusion table for each local classifier using a specific feature vector group. The confusion table for each local classifier contains accuracy information of each local classifier on each class of data. The proposed AMFCM is applied to the problem of music genre classification on a set of music data. The results demonstrate that the proposed AMFCM outperforms MFCM by 8% - 15% on average in terms of classification accuracy depending on the grouping algorithms used for local classifiers and the number of clusters.

• Journal of KIISE:Databases
• /
• v.33 no.4
• /
• pp.372-383
• /
• 2006

Among the approaches to image database management, content-based image retrieval (CBIR) is viewed as having the best support for effective searching and browsing of large digital image libraries. Typical CBIR systems allow a user to provide a query image, from which low-level features are extracted and used to find 'similar' images in a database. However, there exists the semantic gap between human visual perception and low-level representations. An effective methodology for overcoming this semantic gap involves relevance feedback to perform feature re-weighting. Current approaches to feature re-weighting require the number of components for a feature representation to be the same for every image in consideration. Following this assumption, they map each component to an axis in the n-dimensional space, which we call the metric space; likewise the feature representation is stored in a fixed-length vector. However, with the emergence of features that do not have a fixed number of components in their representation, existing feature re-weighting approaches are invalidated. In this paper we propose a feature re-weighting technique that supports features regardless of whether or not they can be mapped into a metric space. Our approach analyses the feature distances calculated between the query image and the images in the database. Two-sided confidence intervals are used with the distances to obtain the information for feature re-weighting. There is no restriction on how the distances are calculated for each feature. This provides freedom for how feature representations are structured, i.e. there is no requirement for features to be represented in fixed-length vectors or metric space. Our experimental results show the effectiveness of our approach and in a comparison with other work, we can see how it outperforms previous work.

• Journal of the Korea Convergence Society
• /
• v.9 no.10
• /
• pp.13-19
• /
• 2018

MapReduce, the Hadoop's essential core technology, is most commonly used to process big data based on the Hadoop distributed file system. However, the existing MapReduce-based big data processing techniques have a feature of dividing and storing files in blocks predefined in the Hadoop distributed file system, thus wasting huge infrastructure resources. Therefore, in this paper, we propose an efficient MapReduce-based big data processing scheme. The proposed method enhances the storage efficiency of a big data infrastructure environment by converting and compressing the data to be processed into a data format in advance suitable for processing by MapReduce. In addition, the proposed method solves the problem of the data processing time delay arising from when implementing with focus on the storage efficiency.

• Journal of KIISE:Software and Applications
• /
• v.30 no.11
• /
• pp.1054-1061
• /
• 2003

Q-Learning, a representative algorithm of reinforcement learning, experiences repeatedly until estimation values about all state-action pairs of state space converge and achieve optimal policies. When the state space is high dimensional or continuous, complex reinforcement learning tasks involve very large state space and suffer from storing all individual state values in a single table. We introduce Q-Map that is new function approximation method to get classified policies. As an agent learns on-line, Q-Map groups states of similar situations and adapts to new experiences repeatedly. State-action pairs necessary for fine control are treated in the form of rule. As a result of experiment in maze environment and mountain car problem, we can achieve classified knowledge and extract easily rules from Q-Map

• Journal of the Korea Society of Computer and Information
• /
• v.14 no.11
• /
• pp.135-144
• /
• 2009

In this paper, a methodology for the risk probability map generation of the crime prevention to be subject to the urban area in the group residential area is presented. The interpretation key is set up to the distinctive feature distinguishing with the unaided eye based on the object composing with the urban area information such as the topology, the facility, and the characteristic information of the corresponding area by analyzing the crime prevention case occurred by gone. This interpretation key is generated, and this information is applied to another area equally, and so, the risk probability map for the crime prevention and the disaster prevention is generated. At this time, the object interpretation key for the urban area information is divided into the various size cell by the crime prevention case. and the risk index according with this cell is set up. Also, the generated various risk probability map is unified, and the integration risk probability map is generated.

• Journal of Biomedical Engineering Research
• /
• v.28 no.1
• /
• pp.8-16
• /
• 2007

Finite element analysis (FEA) is an effective means for the analysis of bioelectromagnetism. It has been successfully applied to various problems over conventional methods such as boundary element analysis and finite difference analysis. However, its utilization has been limited due to the overwhelming computational load despite of its analytical power. We have previously developed a novel mesh generation scheme that produces FE meshes that are content-adaptive to given MR images. MRI content-adaptive FE meshes (cMeshes) represent the electrically conducting domain more effectively with far less number of nodes and elements, thus lessen the computational load. In general, the cMesh generation is affected by the quality of feature maps derived from MRI. In this study, we have tested various feature maps created based on the improved differential geometry measures for more effective cMesh head models. As performance indices, correlation coefficient (CC), root mean squared error (RMSE), relative error (RE), and the quality of cMesh triangle elements are used. The results show that there is a significant variation according to the characteristics of specific feature maps on cMesh generation, and offer additional choices of feature maps to yield more effective and efficient generation of cMeshes. We believe that cMeshes with specific and improved feature map generation schemes should be useful in the FEA of bioelectromagnetic problems.

• The Journal of the Korea Contents Association
• /
• v.19 no.1
• /
• pp.125-131
• /
• 2019

A vanishing point is a point on an image to which parallel lines projected from a real space gather. A vanishing point in a road space provides important spatial information. It is possible to improve the position of an extracted lane or generate a depth map image using a vanishing point in the road space. In this paper, we propose a method of detecting vanishing points on images taken from a vehicle's point of view using Deep Neural Network (DNN) and Histogram of Oriented Gradient (HoG). The proposed algorithm is divided into a HoG feature extraction step, in which the edge direction is extracted by dividing an image into blocks, a DNN learning step, and a test step. In the learning stage, learning is performed using 2,300 road images taken from a vehicle's point of views. In the test phase, the efficiency of the proposed algorithm using the Normalized Euclidean Distance (NormDist) method is measured.

• Spatial Information Research
• /
• v.10 no.3
• /
• pp.455-468
• /
• 2002

Toward the systematic and efficient management of national land, National Geography Institute(NGI, National mapping agency) has been producing national basemap in automated process since middle of 1980's. Under the National Geographic Information System(NGIS) Development Plan, NGI began to produce digital maps in the scales of 1:1,000, 1:5,000, 1:25,000 since 1995. However, those of digital maps that have been generated under NGIS Development Plan need to be modified and corrected due to lack of technology and experience in making digital maps. In this context, those digital maps generated are currently in great need for improving the data dictionary. It is fully appreciated in previous research that data dictionary will be a key element far users and generators of digital maps to rectify the existing problems in digital maps as well as to maximize the application of digital maps. In this paper, we analyzed existing problems in digital maps based on previous researches and interviews with engineers in different fields of geospatial engineering. And then, the existing data dictionary has been redefined and modified. In the line of modification process, a relational matrix was established fur each topographic feature defined in the existing feature classification system. This paper presents newly proposed data dictionary which conforms to newly defined feature classification system from previous research performed by NGI.

• Journal of KIISE:Software and Applications
• /
• v.35 no.12
• /
• pp.789-799
• /
• 2008

For the communication between human and computer in an interactive computing environment, the gesture recognition has been studied vigorously. The algorithms which use the 2D features for the feature extraction and the feature comparison are faster, but there are some environmental limitations for the accurate recognition. The algorithms which use the 2.5D features provide higher accuracy than 2D features, but these are influenced by rotation of objects. And the algorithms which use the 3D features are slow for the recognition, because these algorithms need the 3d object reconstruction as the preprocessing for the feature extraction. In this paper, we propose a method to extract the 3D features combined with the 3D object reconstruction in real-time. This method generates three kinds of 3D projection maps using the modified GPU-based visual hull generation algorithm. This process only executes data generation parts only for the gesture recognition and calculates the Hu-moment which is corresponding to each projection map. In the section of experimental results, we compare the computational time of the proposed method with the previous methods. And the result shows that the proposed method can apply to real time gesture recognition environment.