• 한국정보통신학회논문지
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• 제18권1호
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• pp.142-148
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• 2014

실시간으로 입력되는 영상으로부터 이동객체의 움직임 특징을 이용하여 움직임 분석에 적합한 디스크립터를 제안한다. 배경과 이동객체를 분리하기 위하여 배경학습을 행한다. 연속적으로 추출된 이동객체의 1차 모멘트를 이용하여 각 객체별로 이동 궤적을 추출한다. 연결 리스트를 이용하여 객체별로 추출된 1차 모멘트를 관리한다. 디스크립터는 격자 형태로 미리 지정된 9개의 지점 근방에 포함되는 이동객체의 1차 모멘트 좌표와 객체가 화면에 출현하는 시작 프레임 번호, 화면에서 사라지는 마지막 프레임 번호로 구성된다. 제안하는 전역 및 지역 특징 융합 디스크립터에 의한 비디오 검색은 둘 중 하나의 특징을 사용하는 기존의 방법에 비하여 효과적임을 확인하였다.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2012년도 추계학술대회
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• pp.898-902
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• 2012

실시간으로 입력되는 영상으로부터 이동객체의 움직임 특징을 이용하여 움직임 분석에 적합한 디스크립터를 제안한다. 배경과 이동객체를 분리하기 위하여 배경학습을 행한다. 연속적으로 추출된 이동객체의 1차 모멘트를 이용하여 각 객체별로 이동 궤적을 추출한다. 연결 리스트를 이용하여 객체별로 추출된 1차 모멘트를 관리한다. 디스크립터는 격자 형태로 미리 지정된 9개의 지점 근방에 포함되는 이동객체의 1차 모멘트 좌표와 객체가 화면에 출현하는 시작 프레임 번호, 화면에서 사라지는 마지막 프레임 번호로 구성된다. 제안하는 전역 및 지역 특징융합 디스크립터에 의한 비디오 검색은 둘 중 하나의 특징을 사용하는 기존의 방법에 비하여 효과적임을 확인하였다.

• 한국측량학회지
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• 제30권6_2호
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• pp.665-672
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• 2012

Amid rapidly increasing imagery inputs and their volume in a remote sensing imagery database, Content-Based Image Retrieval (CBIR) is an effective tool to search for an image feature or image content of interest a user wants to retrieve. It seeks to capture salient features from a 'query' image, and then to locate other instances of image region having similar features elsewhere in the image database. For a CBIR approach that uses texture as a primary feature primitive, designing a texture descriptor to better represent image contents is a key to improve CBIR results. For this purpose, an extended feature vector combining the Gabor filter and co-occurrence histogram method is suggested and evaluated for quantitywise and qualitywise retrieval performance criterion. For the better CBIR performance, assessing similarity between high dimensional feature vectors is also a challenging issue. Therefore a number of distance metrics (i.e. L1 and L2 norm) is tried to measure closeness between two feature vectors, and its impact on retrieval result is analyzed. In this paper, experimental results are presented with several CBIR samples. The current results show that 1) the overall retrieval quantity and quality is improved by combining two types of feature vectors, 2) some feature is better retrieved by a specific feature vector, and 3) retrieval result quality (i.e. ranking of retrieved image tiles) is sensitive to an adopted similarity metric when the extended feature vector is employed.

• ETRI Journal
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• 제34권4호
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• pp.572-582
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• 2012

The discrete Gaussian-Hermite moment (DGHM) is a global feature representation method that can be applied to square images. We propose a modified DGHM (MDGHM) method and an MDGHM-based scale-invariant feature transform (MDGHM-SIFT) descriptor. In the MDGHM, we devise a movable mask to represent the local features of a non-square image. The complete set of non-square image features are then represented by the summation of all MDGHMs. We also propose to apply an accumulated MDGHM using multi-order derivatives to obtain distinguishable feature information in the third stage of the SIFT. Finally, we calculate an MDGHM-based magnitude and an MDGHM-based orientation using the accumulated MDGHM. We carry out experiments using the proposed method with six kinds of deformations. The results show that the proposed method can be applied to non-square images without any image truncation and that it significantly outperforms the matching accuracy of other SIFT algorithms.

• 한국정보통신학회논문지
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• 제14권2호
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• pp.469-478
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• 2010

본 논문은 3차원 발 자세를 추정하기 위한 효과적 형상 기술자를 제안하였다. 처리 시간을 단축시키기 위하여 특수 제작된 3차원 발 모형을 2차원 투영하여 발 형상 데이터베이스를 구축하고, 3차원 자세 요약정보를 메타 정보로 추가한 2.5차원 영상 데이터베이스를 구성하였다. 그리고 특징 공간 크기가 작고 다른 형상 기술자에 비하여 자세 추정 성능이 뛰어난 수정된 Centroid Contour Distance를 제안하였다. 제안된 기술자의 성능을 분석하기 위하여, 검색 정확도와 시공간 복잡도를 계산하고 기존의 방식들과 비교하였다. 실험 결과를 통하여 제안된 기술자는 특징 추출 시간과 자세 추정 정확도면에서 기존의 방식들보다 효과적인 것으로 나타났다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제7권7호
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• pp.1690-1704
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• 2013

An affine invariant descriptor is proposed, which is able to well represent the affine covariant regions. Estimating main orientation is still problematic in many existing method, such as SIFT (scale invariant feature transform) and SURF (speeded up robust features). Instead of aligning the estimated main orientation, in this paper ellipse orientation is directly used. According to ellipse orientation, affine covariant regions are firstly divided into 4 sub-regions with equal angles. Since affine covariant regions are divided from the ellipse orientation, the divided sub-regions are rotation invariant regardless the rotation, if any, of ellipse. Meanwhile, the affine covariant regions are normalized into a circular region. In the end, the gradients of pixels in the circular region are calculated and the partition-based descriptor is created by using the gradients. Compared with the existing descriptors including MROGH, SIFT, GLOH, PCA-SIFT and spin images, the proposed descriptor demonstrates superior performance according to extensive experiments.

• 반도체디스플레이기술학회지
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• 제12권4호
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• pp.33-38
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• 2013

Image retrieval is one of the most exciting and fastest growing research fields in the area of multimedia technology. As the amount of digital contents continues to grow users are experiencing increasing difficulty in finding specific images in their image libraries. This paper proposes an efficient image descriptor which uses a local color and texture in the non-overlapped block images. To evaluate the performance of the proposed method, we assessed the retrieval efficiency in terms of ANMRR with common image dataset. The experimental trials revealed that the proposed algorithm exhibited a significant improvement in ANMRR, compared to Dominant Color Descriptor and Edge Histogram Descriptor.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제8권12호
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• pp.4513-4532
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• 2014

Motivated by the Weber's Law, this paper proposes an efficient and robust shape descriptor based on the perceptual stimulus model, called Weber's Law Shape Descriptor (WLSD). It is based on the theory that human perception of a pattern depends not only on the change of stimulus intensity, but also on the original stimulus intensity. Invariant to scale and rotation is the intrinsic properties of WLSD. As a global shape descriptor, WLSD has far lower computation complexity while is as discriminative as state-of-art shape descriptors. Experimental results demonstrate the strong capability of the proposed method in handling shape retrieval.

• International Journal of CAD/CAM
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• 제8권1호
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• pp.69-72
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• 2009

In this paper, we present a new shape descriptor, which is named Poisson equation-Fourier-Mellin moment Descriptor. We solve the Poisson equation in the shape area, and use the solution to get feature function, which are then integrated using Fourier-Mellin moment to represent the shape. This method develops the Poisson equation-geometric moment Descriptor proposed by Lena Gorelick, and keeps both advantages of Poisson equation-geometric moment and Fourier-Mellin moment. It is proved better than Poisson equation-geometric moment Descriptor in shape recognition and classification experiments.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권1호
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• pp.347-370
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• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.