• Journal of the Institute of Electronics Engineers of Korea SP
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• v.43 no.2 s.308
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• pp.1-11
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• 2006

In this paper, we propose the novel video retrieval algorithm using non-parametric based motion classification in the shot-based video indexing structure. The proposed system firstly gets the key frame and motion information from each shot segmented by scene change detection method, and then extracts visual features and non-parametric based motion information from them. Finally, we construct real-time retrieval system supporting similarity comparison of these spatio-temporal features. After the normalized motion vector fields is created from MPEG compressed stream, the extraction of non-parametric based motion feature is effectively achieved by discretizing each normalized motion vectors into various angle bins, and considering a mean, a variance, and a direction of these bins. We use the edge-based spatial descriptor to extract the visual feature in key frames. Experimental evidence shows that our algorithm outperforms other video retrieval methods for image indexing and retrieval. To index the feature vectors, we use R*-tree structures.

• Journal of KIISE
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• v.44 no.7
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• pp.692-702
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• 2017

Face detection using the LBP based feature descriptor has issues in that it can not represent spatial information between facial shape and facial components such as eyes, nose and mouth. To address these issues, in previous research, a facial image was divided into a number of square sub-regions. However, since the sub-regions are divided into different numbers and sizes, the division criteria of the sub-region suitable for the database used in the experiment is ambiguous, the dimension of the LBP histogram increases in proportion to the number of sub-regions and as the number of sub-regions increases, the sensitivity to facial orientation rotation increases significantly. In this paper, we present a novel facial region segmentation method that can solve in-plane rotation issues associated with LBP based feature descriptors and the number of dimensions of feature descriptors. As a result, the proposed method showed detection accuracy of 99.0278% from a single facial image rotated in orientation.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06d
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• pp.346-348
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• 2006

본 논문은 잎맥 특징을 이용한 식물의 잎 이미지 검색 방법을 제안한다. 식물의 검색을 위해 모양 기반의 검색방법을 사용하였으며, 잎의 외곽선 분만 아니라 내부의 잎맥 정보를 이용하여 정확율을 향상시켰다. 외곽선은 MPP(Minimum Perimeter Polygons) 알고리즘을 개선하여 표현하고, 내부의 잎맥의 특징은 CSS(Curvature Scale Space)를 개선하여 주맥과 교차점, 끝점을 추출하여 표현하였다. 특징 점들간의 관계와 거리값을 통해 가중치가 있는 그래프로 표현하고 이 값을 통해 유사도를 계산하였다. 실험에서는 식물도감에서 1000여개의 식물 잎 이미지를 추출하여 기존의 알고리즘인 Fourier Descriptor, CSSD, CCD, Moment Invariants, MPP와 비교하였다.

• Proceedings of the IEEK Conference
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• 2001.06d
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• pp.57-60
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• 2001

In this paper, we propose a new 3D object retrieval system using the shape information of 2D silhouette images. 2D images at different view points are derived from a 3D model and linked to the model. Shape feature of 2D image is extracted by a region-based descriptor. In the experiment, we compare the results of the proposed system with those of the system using curvature scale space(CSS) to show the efficiency of our system.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2010.07a
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• pp.40-41
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• 2010

실시간 파노라마 영상 합성 기술을 모바일 콘텐츠에 적용하기 위해서는 모바일 디바이스에 적합한 영상 합성 방식을 정의해야 한다. 특징점을 추출하여 연속적인 영상들 사이의 유사성을 찾아내는 방식 [1]은 소모되는 연산비용을 고려할 때 모바일 디바이스에 응용하기 부적합하다. 따라서 기존의 접근방식에 비해 소요되는 연산비용을 줄이고, 성능은 유지하는 기법이 필요하다. 본 논문에서는 특징점의 표현자(descriptor)를 생성하지 않고 특징점(feature point) 주변의 움직임을 추정(motion estimation)하여 연속적인 영상의 위치를 찾아내는 기법을 제안한다.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2011.11a
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• pp.161-164
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• 2011

영상 매칭은 컴퓨터 비전에서 기초적인 기술로써 영상 추적, 물체인식 등 다양한 분양에서 많이 사용되고 있다. 하지만 스케일, 시점변화, 조명 변화에 강인한 매칭점을 찾는 것은 어려운 일이다. 이러한 문제점을 보완하기 위해 SURF(Scale Invariant Feature Transform), SIFT(Speed up Robust Features) 등의 알고리즘이 제안 되었지만, 여전히 조명변화에 불안정하고 정확하지 못한 성능을 보인다. 본 논문에서는 이러한 조명변화에 대한 문제점을 해결하기 위해 히스토그램 평활화를 이용하여 영상을 보정 후, SURF를 통한 영상 매칭을 하였다. 열악한 조명환경 내에서 촬영된 영상에서 SURF를 이용하여 표현자(Descriptor)를 생성 할 때 특징점이 잘 추출되지 않는 문제점을 해결하기 위하여 히스토그램 평활화를 이용하였고, 보정 후 특징점 개수가 많이 증가하는 것을 보여 확인하였다. 기존의 SURF와 개량된 SURF를 조명이 서로 다른 영상간의 매칭 성능을 비교함으로써 제안한 알고리즘의 우수성을 확인하였다

• Annual Conference of KIPS
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• 2012.11a
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• pp.626-628
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• 2012

최근 이미지에서 특징점을 추출하고 이를 활용하는 분야로 이미지 스티칭에 대한 연구가 활발하게 진행되고 있다. 이미지 스티칭에서는 특징점을 추출 및 정합이 중요한 요소이다. 본 논문에서는 특징점 기술자의 차원을 효과적으로 감소시켜 정확하면서도 빠르게 정합점을 찾을 수 있는 효율적인 특징점 기술자 생성을 이용한 빠른 이미지 스티칭 기법을 제안한다. 실험 결과, 이미지 스티칭 속도가 기존의 알고리즘 보다 빠르면서도 향상된 스티칭 이미지를 생성할 수 있었다.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.2
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• pp.19-28
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• 2019

The purpose of this study is to examine the trends on machine learning and deep learning research in the published journals from the Web of Science Database. To achieve the study purpose, we used the abstracts of 20,664 articles published between 1990 and 2017, which include the word 'machine learning', 'deep learning', and 'artificial neural network' in their titles. Twenty major research topics were identified from topic modeling analysis and they were inclusive of classification accuracy, machine learning, optimization problem, time series model, temperature flow, engine variable, neuron layer, spectrum sample, image feature, strength property, extreme machine learning, control system, energy power, cancer patient, descriptor compound, fault diagnosis, soil map, concentration removal, protein gene, and job problem. The analysis of the time-series linear regression showed that all identified topics in machine learning research were 'hot' ones.

• Korean Journal of Remote Sensing
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• v.27 no.3
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• pp.359-367
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• 2011

A suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. The research on pan-sharpening algorithm in improving the accuracy of image classification has been reported. For a classification, preserving the spectral information is important. Other applications such as road detection depend on a sharp and detailed display of the scene. Various criteria applied to scenes with different characteristics should be used to compare the pan-sharpening methods. The pan-sharpening methods in our research comprise rather common techniques like Brovey, IHS(Intensity Hue Saturation) transform, and PCA(Principal Component Analysis), and more complex approaches, including wavelet transformation. The extraction of matching pairs was performed through SIFT descriptor and Canny edge detector. The experiments showed that pan-sharpening techniques for spatial enhancement were effective for extracting point and linear features. As a result of the validation it clearly emphasized that a suitable pan-sharpening method has to be chosen with respect to the used spectral characteristic of the multispectral bands and the intended application. In future it is necessary to design hybrid pan-sharpening for the updating of features and land-use class of a map.

• Journal of Korea Society of Digital Industry and Information Management
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• v.9 no.2
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• pp.111-119
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• 2013

Face recognition has recently become one of the most popular research areas in the fields of computer vision, machine learning, and pattern recognition because it spans numerous applications, such as access control, surveillance, security, credit-card verification, and criminal identification. In this paper, we propose a simple descriptor called an ECSP(Extended Center-Symmetric Pattern) for illumination-robust face recognition. The ECSP operator encodes the texture information of a local face region by emphasizing diagonal components of a previous CS-LBP(Center-Symmetric Local Binary Pattern). Here, the diagonal components are emphasized because facial textures along the diagonal direction contain much more information than those of other directions. The facial texture information of the ECSP operator is then used as the input image of an image covariance-based feature extraction algorithm such as 2D-PCA(Two-Dimensional Principal Component Analysis). Performance evaluation of the proposed approach was carried out using various binary pattern operators and recognition algorithms on the Yale B database. The experimental results demonstrated that the proposed approach achieved better recognition accuracy than other approaches, and we confirmed that the proposed approach is effective against illumination variation.