• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.11a
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• pp.54-57
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• 2013

최근 다양한 방송 및 영상 분야에서 사람의 행동을 인식하여는 연구들이 많이 이루어지고 있다. 영상은 다양한 형태를 가질 수 있기 때문에 제약된 환경에서 유용한 템플릿 방법들보다 특징점에 기반한 연구들이 실제 사용자 환경에서 더욱 관심을 받고 있다. 특징점 기반의 연구들은 영상에서 움직임이 발생하는 지점들을 찾아내어 이를 3차원 패치들로 생성한다. 이를 이용하여 영상의 움직임을 히스토그램에 기반한 descriptor(서술자)로 표현하고 학습기반의 판별기(classifier)로 최종적으로 영상 내에 존재하는 행동들을 인식하였다. 그러나 단일 판별기를 이용한 다양한 영상 인식을 수용하기에는 힘들다. 최근에 이를 개선하기 위하여 다중 판별기를 활용한 연구들이 영상 판별 및 물체 검출 영역에서 사용되고 있다. 따라서 본 논문에서는 행동 인식을 위하여 support vector machine과 spare representation을 이용한 decision-level fusion 방법을 제안하고자 한다. 제안된 논문의 방법은 영상에서 특징점 기반의 descriptor를 추출하고 이를 각각의 판별기를 통하여 판별 결과들을 획득한다. 이 후 학습단계에서 획득된 가중치를 활용하여 각 결과들을 융합하여 최종 결과를 도출하였다. 본 논문에 실험에서 제안된 방법은 기존의 융합 방법보다 높은 행동 인식 성능을 보여 주었다.

• Electronics and Telecommunications Trends
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• v.29 no.1
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• pp.61-71
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• 2014

모바일 비주얼 검색(MVS: Mobile Visual Search) 기술은 스마트폰 환경에서 질의 영상의 특징을 검출하고 그 주변 영역의 정보를 이용하여 추출한 서술자를 미리 구축된 영상정보 DB와 비교하여 검색하는 기술로 스마트폰의 보급과 네트워크 환경발달에 따라 응용서비스의 범위와 가능성이 확대되고 있다. 본 동향에서는 기존의 텍스트, 음성, QR코드 등의 검색을 넘어 인간이 보는 그대로를 검색하고자 하는 모바일 비주얼 검색의 기술 및 표준화 동향을 분석하고 향후 전망에 대해서 살펴보고자 한다.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.2
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• pp.89-98
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• 2009

In the field of computer vision, image mosaicking is a common method for effectively increasing restricted the field of view of a camera by combining a set of separate images into a single seamless image. Image mosaicking based on feature points has recently been a focus of research because of simple estimation for geometric transformation regardless distortions and differences of intensity generating by motion of a camera in consecutive images. Yet, since most feature-point matching algorithms extract feature points using gray values, identifying corresponding points becomes difficult in the case of changing illumination and images with a similar intensity. Accordingly, to solve these problems, this paper proposes a method of image mosaicking based on feature points using color information of images. Essentially, the digital values acquired from a digital color camera are converted to values of a virtual camera with distinct narrow bands. Values based on the surface reflectance and invariant to the chromaticity of various illuminations are then derived from the virtual camera values and defined as color-invariant values invariant to changing illuminations. The validity of these color-invariant values is verified in a test using a Macbeth Color-Checker under simulated illuminations. The test also compares the proposed method using the color-invariant values with the conventional SIFT algorithm. The accuracy of the matching between the feature points extracted using the proposed method is increased, while image mosaicking using color information is also achieved.

• Journal of Advanced Navigation Technology
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• v.15 no.1
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• pp.140-146
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• 2011

In this paper, we propose a fast object classification method based on texture and color information for video surveillance. We take the advantage of local patches by extracting SURF and color histogram from images. SURF gives intensity content information and color information strengthens distinctiveness by providing links to patch content. We achieve the advantages of fast computation of SURF as well as color cues of objects. We use Bag of Word models to generate global descriptors of a region of interest (ROI) or an image using the local features, and Na$\ddot{i}$ve Bayes model for classifying the global descriptor. In this paper, we also investigate discriminative descriptor named Scale Invariant Feature Transform (SIFT). Our experiment result for 4 classes of the objects shows 95.75% of classification rate.

• Journal of Platform Technology
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• v.11 no.3
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• pp.24-31
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• 2023

Recently, interest in UAM (Urban Air Mobility, UAM), which can take off and land vertically in the operation of urban air transportation systems, has been increasing. Therefore, various start-up companies are developing related technologies as eco-friendly future transportation with advanced technology. However, studies on ways to increase safety in the operation of UAM are still insignificant. In particular, efforts are more urgent to improve the safety of risks generated in the process of attempting to land in the city center by UAM equipped with autonomous driving. Accordingly, this study proposes a plan to safely land by avoiding dangerous region that interfere when autonomous UAM attempts to land in the city center. To this end, first, the latitude and longitude coordinate values of dangerous objects observed by the sense of the UAM are calculated. Based on this, we proposed to convert the coordinates of the distorted planar image from the 3D image to latitude and longitude and then use the calculated latitude and longitude to compare the pre-learned feature descriptor with the HOG (Histogram of Oriented Gradients, HOG) feature descriptor to extract the dangerous Region. Although the dangerous region could not be completely extracted, generally satisfactory results were obtained. Accordingly, the proposed research method reduces the enormous cost of selecting a take-off and landing site for UAM equipped with autonomous driving technology and contribute to basic measures to reduce risk increase safety when attempting to land in complex environments such as urban areas.

• Journal of Broadcast Engineering
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• v.19 no.2
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• pp.166-173
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• 2014

Recently, human action recognition have been developed for various broadcasting and video process. Since a video can consist of various scenes, keypoint approaches have been more attracted than template based methods for real application. Keypoint approahces tried to find regions having motion in video, and made 3-dimensional patches. Then, descriptors using histograms were computed from the patches, and a classifier based on machine learning method was applied to detect actions in video. However, a single classifier was difficult to handle various human actions. In order to improve this problem, approaches using multi classifiers were used to detect and to recognize objects. Thus, we propose a new human action recognition using decision-level fusion with support vector machine and sparse representation. The proposed method extracted descriptors based on keypoint approach from a video, and acquired results from each classifier for human action recognition. Then, we applied weights which were acquired by training stage to fuse each results from two classifiers. The experiment results in this paper show better result than a previous fusion method.

• Journal of KIISE
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• v.41 no.9
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• pp.666-673
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• 2014

This paper proposes a scalable face recognition method for unconstrained face databases, and shows a simple experimental result. Existing face recognition research usually has focused on improving the recognition rate in a constrained environment where illumination, face alignment, facial expression, and background is controlled. Therefore, it cannot be applied in unconstrained face databases. The proposed system is face feature extraction algorithm for unconstrained face recognition. First of all, we extract the area that represent the important features(landmarks) in the face, like the eyes, nose, and mouth. Each landmark is represented by a high-dimensional LBP(Local Binary Pattern) histogram feature vector. The multi-scale LBP histogram vector corresponding to a single landmark, becomes a low-dimensional face feature vector through the feature reduction process, PCA(Principal Component Analysis) and LDA(Linear Discriminant Analysis). We use the Rank acquisition method and Precision at k(p@k) performance verification method for verifying the face recognition performance of the low-dimensional face feature by the proposed algorithm. To generate the experimental results of face recognition we used the FERET, LFW and PubFig83 database. The face recognition system using the proposed algorithm showed a better classification performance over the existing methods.

• 한국HCI학회:학술대회논문집
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• 2008.02a
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• pp.607-611
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• 2008

This paper proposes new video signature using spatio-temporal information for copy detection. The proposed video copy detection method is based on concentric circle partitioning method for each key frame. Firstly, key frames are extracted from whole video using temporal bilinear interpolation periodically and each frame is partitioned as a shape of concentric circle. For the partitioned sub-regions, 4 feature distributions of average intensity, its difference, symmetric difference and circular difference distributions are obtained by using the relation between the sub-regions. Finally these feature distributions are converted into binary signature by using simple hash function and merged together. For the proposed video signature, the similarity distance is calculated by simple Hamming distance so that its matching speed is very fast. From experiment results, the proposed method shows high detection success ratio of average 97.4% for various modifications. Therefore it is expected that the proposed method can be utilized for video copy detection widely.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.26 no.4
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• pp.407-414
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• 2008

This study introduces an automatic image matching algorithm that can be applied for the scale different image pairs consisting of the satellite pushbroom images and the aerial frame images. The proposed method is based on several image processing techniques such as pre-processing, filtering, edge thinning, interest point extraction, and key-descriptor matching, in order to enhance the matching accuracy and the processing speed. The proposed method utilizes various characteristics, such as the different geometry of image acquisition and the different radiometric characteristics, of the multi-sensor images. In addition, the suggested method uses the sensor model to minimize search area and eliminate false-matching points automatically.

• Journal of Korean Home Economics Education Association
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• v.25 no.1
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• pp.37-54
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• 2013

This study aimed at examining the characteristics of the Korean home economics achievement standards at secondary education level, and comparing with those of U.S. home economics national standards. The specific research questions were as follows. First, the characteristics of Korean and U.S. achievement standards were examined respectively. Second, the standards of the two countries were examined in terms of the grades involved, development areas, specification level, sequence of courses, and format of statements, adopting the analysis framework of Lee, Jang, and Park(2012). Third, the achievement standards of the two countries for specific content areas, i.e. 'understanding adolescents' and 'understanding family' were compared in terms of the styles and format of statements. The findings suggest that in developing the Korean achievement standards, integration with the curriculum and detailed and specific statements are important to facilitate adoption at schools. The results of the comparison with the U.S. national standards indicates that it is important to emphasize academic rationale to home economics as a subject by clearly presenting the concepts, contents, and categories. Also, the standards will need to be stated in specific terms to allow practices and evaluations, rather than in terms that reflect value judgments.