• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.147-152
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• 2007

Face detection is a crucial part of the face recognition system. It determines the performance of the whole recognition system. Hausdorff distance metric has been used in face detection and recognition with good results. It defines the distance metric based only on the geometric similarity between two sets or points. However, not only the geometry but also the local patterns around the points are available in most cases. In this paper a new Hausdorff distance measure is proposed that makes hybrid use of the similarity of the geometry and the local patterns around the points. Several experiments shows that the new method outperforms the conventional method.

• Journal of KIISE:Databases
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• v.32 no.4
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• pp.429-440
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• 2005

As rapid improvement in electronic technologies makes sensor hardware more powerful and capable, the application range of sensor networks Is getting to be broader. The main purpose of sensor networks is to monitor the phenomena in interesting regions (e.g., factory warehouses, disaster areas, wild fields, etc) and return required data. The k Nearest Neighbor (KNN) query that finds k objects which are geographically close to the given point is an Important application in sensor networks. However, most previous approaches are either seem to be impractical or are not energy-efficient in resource-limited sensor networks. In this paper. we propose an efficient KNN query processing method in sensor networks. In the proposed method, we dynamically increase searching boundary, if necessary, and traverse nodes inside the boundary until finding k nearest neighbors. Since only the representative sensor nodes are visited, our algorithm reduces a number of messages. We show thorough experiments that the proposed method performs better than the existing method in various network environments.

• 한국경영정보학회:학술대회논문집
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• 2007.06a
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• pp.297-301
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• 2007

IS/IT 분야 신입사원에게 필요한 지식/능력과 소프트웨어 전문 기술에 대하여 인사채용 담당자와 IS/IT 관련 학과 전공교수간의 인식차를 조사하였다. 조사 결과 IS/IT 지식/기술 영역에서는 개인적인 능력에 속하는 ‘동기부여’, ‘창조직 사고’, ‘비판적 사고’ 등에 두 그룹 모두 높은 점수를 주고 있는 등 두 그룹간에 큰 차이를 보이지 않았다. 그러나 IS/IT 소프트웨어 기술 영역에서는 전공 교수는 객체지향 언어, 모델링 언어 등 현재 많은 이슈가 되고 있는 주제에 중요도를 높게 두는 반면, 인사채용 담당자는 대부분 중간값 근처로 응답하여 특별히 중요시 여기는 주제가 드러나지 않았다. 따라서 인사채용 담당자의 경우 변화의 필요는 느끼지만 아직 구체적으로 어떤 분야에 어떻게 대웅을 해야 하는지에 대해서는 명확히 하고 있지 못한 것으로 나타났다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2012.11a
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• pp.9-10
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• 2012

이진 특징 기술자는 실수 벡터 형태의 특징 기술자보다 빠르게 특징점 추출 및 정합이 가능하고 메모리 공간도 적게 차지하는 장점이 있다. 하지만, 특징점의 수가 많아질수록 정합에 많은 시간이 소요되므로 실시간 처리가 중요한 객체 추적에 적용하기 위해서는 정합의 고속화 방법에 대한 연구가 필요하다. 이에 본 논문에서는 이진 특징 기술자의 군집화를 통한 특징점의 고속 정합 방법을 제안한다. 제안된 방법은 k-means 군집화 알고리즘을 기반으로 정합을 위한 기술자 탐색을 효과적으로 수행함으로써 군집화를 사용하지 않는 기존의 정합 방법에 비해 빠르면서도 높은 정확도를 유지한다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.195-198
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• 2013

Recognition and Tracking of interesting object is the significant field in Computer Vision. Mean-Shift algorithm have chronic problems that some errors are occurred when histogram of tracking area is similar to another area. in this paper, we propose to solve the problem. Each algorithm blocks skin color filtering, face detect and Mean-Shift started consecutive order assists better operation of the next algorithm. Avoid to operations of the overhead of tracking area similar to a histogram distribution areas overlap only consider the number of white pixels by running the Viola-Jones algorithm, simple arithmetic increases the convergence of the Mean-Shift. The experimental results, it comes to 78% or more of white pixels in the Mean-Shift search area, only if the recognition of the face area when it is configured to perform a Viola-Jones algorithm is tracking the object, was 100 percent successful.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.9
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• pp.1319-1324
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• 2013

The mean shift algorithm has achieved considerable success in object tracking due to its simplicity and robustness. It finds local minima of a similarity measure between the color histograms or kernel density estimates of the target and candidate image. However, it is sensitive to the noises due to objects or background having similar color distributions. In addition, occlusion by another object often causes a face region to change in size and position although a face region is a critical clue to perform face recognition or compute face orientation. We assume that depth and color are effective to separate a face from a background and a face from objects, respectively. From the assumption we devised a bilateral filter using color and depth and incorporate it into the mean-shift algorithm. We demonstrated the proposed method by some experiments.

• Journal of the Korea Society of Computer and Information
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• v.14 no.1
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• pp.99-106
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• 2009

Though edge detection, an important stage that significantly affecting the performance of image recognition, has been given numerous researches on its execution methods, it still remains as difficult problem and it is one of the components for image recognition applications while it is not the only way to identify an object or track a specific area. This paper, unlike gradient operator using edge detection method, found out edge pixel by referring to 2 neighboring pixels information in binary image and comparing them with pre-defined 4 edge pixels pattern, and detected binary image edge by determining the direction of the next edge detection exploring pixel and proposed method to detect binary image edge by repeating step of edge detection to detect another area edge. When recognizing image, if edge is detected with the use of gradient operator, thinning process, the stage next to edge detection, can be omitted, and with the edge detection algorithm executing time reduced compared with existing area edge tracing method, the entire image recognizing time can be reduced by applying real-time image recognizing system.

• Spatial Information Research
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• v.8 no.2
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• pp.213-232
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• 2000

It is important to scrutinize spatial patterns in crime analysis since crime data has geographical attribute in itself. We focus on the development of ¨Crime Analysis GIS¨ prototype which can discover spatial patterns in crime data by integrating mapping functions of GIS and spatial analysis techniques. The structure of this system involves integration of DBMS and GIS, and the major functions of the system include (i) exploring spatial distribution of point data, (ii) mapping hot-spot, (iii) clustering analysis of crime occurrence, and (iv) analyzing aggregated areal data. The process of design and implementation of this system is based on object-oriented methodologies. A web-based extension of the prototype using 3-tier architecture is currently under development.

• Korean Journal of Remote Sensing
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• v.19 no.4
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• pp.319-328
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• 2003

The automatic analysis of high-resolution satellite image is important in cartography, surveillance, exploiting resources etc. However, the automatic analysis of high resolution satellite image in the urban area has lots of difficulty including a shadow, the difference of illumination with time, the complexity of image so that the present techniques are seemed to be impossible to resolve. This paper proposes a new way of change detection of building objects in urban area, in which the objects in digital vector map are designated and superimposed on the the high-resolution satellite image. The proposed way makes the buildings on the vector map parameterize, and searches them in the preprocessed high-resolution satellite image by using generalized Hough transform. The designated building objects are overlaid on the satellite image and the result can help to search the changes in building objects rapidly.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.23-51
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• 2003

With the recent expansion of internet shopping mall, the importance of intelligent products recommendation agents has been increasing. for the products recommendation, This paper propose case-based reasoning approach, and developed a case-based bundle products recommendation system which can recommend a set of sea food used in family events. To apply CBR approach to the bundle products recommendation, it requires the following 4R steps : \circled1 Retrieval, \circled2 Reuse, \circled3 Revise, \circled4 Retain. To retrieve similar cases from the case-base efficiently, case representation scheme is most important. This paper used OW(Object Modeling Technique) to represent bundle products recommendation cases, and developed a similarity measure method to search similar cases. To measure similarity, we used weight-sum approach basically. Especially This paper propose the meaning and uses of taxonomies for representing case features.