• 대한산업공학회지
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• 제41권4호
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• pp.338-343
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• 2015

We suggest a method to configure feature functions of continuous conditional random field (C-CRF). Regression tree and similarity analysis are introduced to construct the first and second feature functions of C-CRF, respectively. Rules from the regression tree are transformed to logic functions. If a logic in the set of rules is true for a data then it returns the corresponding value of leaf node and zero, otherwise. We build an Euclidean similarity matrix to define neighborhood, which constitute the second feature function. Using two feature functions, we make a C-CRF model and an illustrate example is provided.

• 한국CDE학회논문집
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• 제16권1호
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• pp.21-30
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• 2011

Similarity assessment of a CAD model is one of important issues from the aspect of model re-using. In real practice, many new mechanical parts are designed by modifying existing ones. The reuse of part enables to save design time and efforts for the designers. Design time would be further reduced if there were an efficient way to search for existing similar designs. This paper proposes an efficient algorithm of similarity assessment for mechanical part model with design history embedded within the CAD model. Since it is possible to retrieve the design history and detailed-feature information using CAD API, we can obtain an accurate and reliable assessment result. For our purpose, our assessment algorithm can be divided by two: (1) we select suitable parts by comparing MSG (Model Signature Graph) extracted from a base feature of the required model; (2) detailed-features' similarities are assessed with their own attributes and reference structures. In addition, we also propose a indexing method for managing a model database in the last part of this article.

• 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.

• Asia Marketing Journal
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• 제17권1호
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• pp.97-121
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• 2015

In this research, we examine whether and why temporal distance influences evaluations of two different types of brand extensions: concept-based extensions, defined as extensions primarily based on the importance or relevance of brand concepts to extension products; and similarity-based extensions, defined as extensions primarily based on the amount of feature similarity at the product-category level. In Study 1, we test the hypothesis that concept-based extensions are evaluated more favorably when they are framed to launch in the distant rather than in the near future, whereas similaritybased extensions are evaluated more favorably when they are framed to launch in the near rather than in the distant future. In Study 2, we confirm that this time-dependent differential evaluation is driven by the difference in construal level between the bases of the two types of extensions - i.e., brand-concept consistency and product-category feature similarity. As such, we find that conceptbased extensions are evaluated more favorably under the abstract than concrete mindset, whereas similarity-based extensions are evaluated more favorably under the concrete than abstract mindset. In Study 3, we extend to the case for a broad brand (i.e., brands that market products across multiple categories), finding that making accessible a specific product category of a broad parent brand influences evaluations of near-future, but not distant-future, brand extensions. Combined together, our findings suggest that temporal distance influences brand extension evaluation through its effect on the importance placed on brand concepts and feature similarity. That is, consumers rely on different bases to evaluate brand extensions, depending on their perception of when the extensions take place and on under what mindset they are placed. This research makes theoretical contributions to the brand extension research by identifying one important determinant to brand extension evaluation and also uncovering its underlying dynamics. It also contributes to expanding the scope of the construal level theory by putting forth a novel interpretation of two bases of perceived fit in terms of construal level. Marketers who are about to launch and advertise brand extensions may benefit by considering temporal-distance information in determining what content to deliver about extensions in their communication efforts. Conceptual relation of a parent brand to extensions needs to be emphasized in the distant future, whereas feature similarity should be highlighted in the near future.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제4권3호
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• pp.322-326
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• 2004

This paper describes a new iris recognition method based on a shift-invariant wavelet sub-images. For the feature representation, we first preprocess an iris image for the compensation of the variation of the iris and for the easy implementation of the wavelet transform. Then, we decompose the preprocessed iris image into multiple subband images using a shift-invariant wavelet transform. For feature representation, we select a set of subband images, which have rich information for the classification of various iris patterns and robust to noises. In order to reduce the size of the feature vector, we quantize. each pixel of subband images using the Lloyd-Max quantization method Each feature element is represented by one of quantization levels, and a set of these feature element is the feature vector. When the quantization is very coarse, the quantized level does not have much information about the image pixel value. Therefore, we define a new similarity measure based on mutual information between two features. With this similarity measure, the size of the feature vector can be reduced without much degradation of performance. Experimentally, we show that the proposed method produced superb performance in iris recognition.

• 한국콘텐츠학회논문지
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• 제9권2호
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• pp.43-50
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• 2009

본 연구에서는 고유의 비디오 프레임의 특징 데이터를 추출하고 추출된 특징 데이터를 1차 신호로 생성하여 유사한 비디오 프레임 데이터를 검출하는 방법에 관하여 연구하였다. 비디오 간의 유사도 검출을 위하여 유사한 프레임간의 경계를 얻어낸 후 경계 범위 내에서 대표 프레임을 얻어낸다. 생성된 대표 프레임으로부터 blurring 된 프레임을 생성하고, DOG 값을 이용하여 특징 데이터를 추출한다. 이렇게 생성된 특징 데이터를 1차원 신호로 나열하고 콘텐츠 간 유사도를 비교한다. 실험 결과 잡음 첨가, 회전 변환, 크기 변환, 프레임 절삭, 프레임 제거 공격에 대해서도 유사도 수치 0.9 이상의 매우 강인한 특성을 나타냈다.

• International Journal of Contents
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• 제13권4호
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• pp.70-79
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• 2017

Images are an important element in patents and many experts use images to analyze a patent or to check differences between patents. However, there is little research on image analysis for patents partly because image processing is an advanced technology and typically patent images consist of visual parts as well as of text and numbers. This study suggests two methods for using image processing; the Scale Invariant Feature Transform(SIFT) algorithm and Optical Character Recognition(OCR). The first method which works with SIFT uses image feature points. Through feature matching, it can be applied to calculate the similarity between documents containing these images. And in the second method, OCR is used to extract text from the images. By using numbers which are extracted from an image, it is possible to extract the corresponding related text within the text passages. Subsequently, document similarity can be calculated based on the extracted text. Through comparing the suggested methods and an existing method based only on text for calculating the similarity, the feasibility is achieved. Additionally, the correlation between both the similarity measures is low which shows that they capture different aspects of the patent content.

• 한국정보통신학회논문지
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• 제21권5호
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• pp.960-966
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• 2017

제안 논문에서는 의료영상 이미지를 입력받아 병변 추출이 가능한 알고리즘을 제안한다. 의료영상 이미지의 병변을 추출하기 위해 SIFT 알고리즘을 이용해 특징점들을 추출한다. 특징점의 강도를 높이기 위해 벡터 유사도를 이용해 입력 영상과 병변이미지를 정합하고 병변을 추출한다. 벡터 유사도 정합을 통해 빠르게 병변을 도출할 수 있다. 국소적인 특징점 쌍으로부터 방향 벡터를 생성하기 때문에 방향 자체는 국소적인 특징만을 나타내지만 두 영상 간에 존재하는 다른 벡터들 간의 유사도를 비교하고 전역적인 특징으로 확장될 수 있는 장점을 갖는다. 또한 병변 정합 오류율은 평균 1.02%, 처리속도는 특징점 강도 정보를 사용하지 않을 때보다 약 40%가 향상됨을 실험을 통해 보였다.

• 한국콘텐츠학회논문지
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• 제11권5호
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• pp.125-137
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• 2011

최근 휴대폰 카메라로 촬영한 영상을 입력으로 사용하는 시스템에 대한 연구가 활발히 이루어지고 있다. 본 논문에서는 와인라벨의 문자를 인식한 후, 데이터베이스내의 와인이미지들 중에서 입력 와인라벨 이미지와 유사한 순서대로 사용자에게 보여주는 시스템을 제안한다. 이미지의 유사도 계산을 위해 본 논문에서는 이미지의 각 영역별 대표색상, 텍스트 영역의 텍스트 색상과 배경색상, 그리고 특징점의 분포를 특징으로 사용한다. 이미지의 색상차를 계산하기 위해 RGB색상을 CIE-Lab색상으로 변환하여 사용하고, 특징점은 해리스코너 검출 알고리즘을 사용하여 추출한다. 각 셀의 대표 색상차와 텍스트 색상차 및 배경 색상차는 가중치를 적용하여 색상차 유사도를 계산하고 색상차 유사도와 특징점 분포 유사도를 정규화하여 최종 이미지 유사도를 구한다. 본 논문에서는 입력 이미지와 데이터베이스내의 이미지 간의 유사도를 계산하여 유사도 순으로 사용자에게 검색 결과를 보여줌으로써 검색 결과로부터 다시 최대 유사 와인라벨을 수동으로 찾는 노력을 줄일 수 있다.

• 한국멀티미디어학회논문지
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• 제16권9호
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• pp.1057-1066
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• 2013

손가락 관절문(FKP, finger-knuckle-print)을 이용한 개인 인증은 손가락 관절부에 나타나는 주름의 특징을 이용하는 것으로, 텍스처의 방향 정보가 중요한 특징이 된다. 본 논문에서는 SIFT 알고리즘을 이용하여 특징점들을 추출하고, 벡터 유사도 정합을 통해 FKP를 효과적으로 인증할 수 있는 방법을 제안하다. 벡터는 질의 영상에서 추출한 특징점과 이에 대응되는 참조 영상의 특징점을 연결하는 방향 벡터로 정의된다. 국소적인 특징점 쌍으로부터 방향 벡터를 생성하기 때문에 방향 벡터 자체는 국소적인 특징만을 나타내지만, 두 영상 간에 존재하는 다른 벡터들 간의 유사도를 비교함으로써 전역적인 특징으로 확장되는 장점이 있다. 실험결과 제안하는 방법은 기존의 방향코드를 이용한 다양한 방식에 비하여 우수한 성능을 나타내었다.