• 방송공학회논문지
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• 제12권4호
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• pp.350-359
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• 2007

In this paper we present a simple, efficient method for detection of sharp digital images. Recently many digital cameras are equipped with various autofocusing functions to help users take well-focused pictures as easily as possible. However, acquired digital pictures can be further degraded by motion, limited contrast, and inappropriate amount of exposure, to name a few. In order to decide whether to process the image or not, or whether to delete it or not, reliable measure of image quality to detect sharp images from blurry ones is needed. This paper presents a blurriness/sharpness measure, and demonstrates its feasibility using extensive experiments. This method is fast and easy to implement, and accurate. Regardless of the detection accuracy, existing measures are computation-intensive. However, the proposed measure in this paper is not demanding in computation time. Needless to say, this measure can be used for various imaging applications including autofocusing and astigmatism correction.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2003년도 신호처리소사이어티 추계학술대회 논문집
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• pp.485-488
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• 2003

A conventional correlation target tracker is analysed with a simple mathematical approach. And, we will propose a correlation measure with selective attentional property in order to overcome the false-peak problem of the conventional methods. Various experimental results show that the proposed correlation measure is able to reduce considerably the probability of false-peaks degraded by the correlation between background images of a reference block and a distorted and noisy sensor input image.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.28-31
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• 2003

In the present work, a main purpose is to propose a fuzzy integral-based aggregation framework to complementarily combine partial information due to lack of completeness. Based on Choquet integral (CI) viewed as monotone expectation, we take into account complementary, non-interactive, and substitutive aggregations of different sources of defective information. A CI-based system representing upper, conventional, and lower expectations is designed far handling three aggregation attitudes towards uncertain information. In particular, based on Choquet integrals for belief measure, probability measure, and plausibility measure, CI$\_$bi/-, CI$\_$pr/ and CI$\_$pl/-aggregator are constructed, respectively. To illustrate a validity of proposed aggregation framework, multiple matching systems are developed by combining three simple individual template-matching systems and tested under various image variations. Finally, compared to individual matchers as well as other traditional multiple matchers in terms of an accuracy rate, it is shown that a proposed CI-aggregator system, {CI$\_$bl/-aggregator, CI$\_$pl/-aggregator, Cl$\_$pl/-aggregator}, is likely to offer a potential framework for either enhancing completeness or for resolving conflict or for reducing uncertainty of partial information.

• 제어로봇시스템학회논문지
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• 제8권4호
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• pp.333-337
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• 2002

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1412-1415
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• 2002

This paper describes a digital signature-based method for original and updated video authentication. The method uses multiple digital signatures in dealing with video data undergoing multiple change/updating. In addition, a feature based on neighbouring block similarity measure is applied to deal with certain image/video modification. The proposed method can cope with wide range of image/video tampering. It is suitable for practical use of video data, where updating may be performed by more than one legal parties. Experimental results are included with concluding remarks.

• 한국정보전자통신기술학회논문지
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• 제3권3호
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• pp.8-13
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• 2010

Shape from focus (SFF) 방법은 이미지의 초점화된 영상을 이용하여 삼차원의 형상을 복원하는 방법이다. 그동안 많은 SFF 방법들이 연구되어 왔지만 노이즈에 대한 문제점과 영상특성으로 인한 최적화되지 못한 문제점이 남아있었다. 그러므로 노이즈를 제거하기 위한 필터링과 최적화 알고리즘을 제안한다. 성능 평가를 위하여 통계적인 판별기준인 평균제곱근오차 (RMSE)와 상관관계 (correlation) 수치를 이용한다.

• Journal of Advanced Marine Engineering and Technology
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• 제28권4호
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• pp.611-617
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• 2004

Pixel classification is one of basic image processing issues. The general characteristics of the pixels belonging to various classes are discussed and the radical principles of pixel classification are given. At the same time. a pixel classification scheme based on image direction measure is proposed. As a typical application instance of pixel classification, an adaptive multi-level median filter is presented. An image can be classified into two types of areas by using the direction information measure, that is. smooth area and edge area. Single direction multi-level median filter is used in smooth area. and multi-direction multi-level median filter is taken in the other type of area. What's more. an adaptive mechanism is proposed to adjust the type of the filters and the size of filter window. As a result. we get a better trade-off between preserving details and noise filtering.

• International Journal of Internet, Broadcasting and Communication
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• 제7권1호
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• pp.36-41
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• 2015

This paper is proposed a wavelet-based MCDT(Mask Coefficient Differential and Threshold) method of image registration of Multi-images contaminated with visible image and infrared image. The method for ensure reliability of the image registration is to the increase statistical corelation as getting the common feature points between two images. The method of threshold the wavelet coefficients using derivatives of the wavelet coefficients of the detail subbands was proposed to effectively registration images with distortion. And it can define that the edge map. Particularly, in order to increase statistical corelation the method of the normalized mutual information. as similarity measure common feature between two images was selected. The proposed method is totally verified by comparing with the several other multi-image and the proposed image registration.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2013년도 춘계학술대회
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• pp.122-124
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• 2013

미는 복잡 속의 질서에 의해 얻어진 것이라고 하였다. 여러 가지 배색의 조합에 대하여 조화가 좋은 아름다움은 정량적으로 표시된다고 하였다. 미도가 클수록 배색은 아름다우며, 보통 비도가 0.5이상이면 아름다운 배색이라 할 수 있다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권4호
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• pp.1424-1440
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• 2015

It is a challenging problem to search the intended images from a large number of candidates. Content based image retrieval (CBIR) is the most promising way to tackle this problem, where the most important topic is to measure the similarity of images so as to cover the variance of shape, color, pose, illumination etc. While previous works made significant progresses, their adaption ability to dataset is not fully explored. In this paper, we propose a similarity learning method on the basis of probabilistic generative model, i.e., probabilistic latent semantic analysis (PLSA). It first derives Fisher kernel, a function over the parameters and variables, based on PLSA. Then, the parameters are determined through simultaneously maximizing the log likelihood function of PLSA and the retrieval performance over the training dataset. The main advantages of this work are twofold: (1) deriving similarity measure based on PLSA which fully exploits the data distribution and Bayes inference; (2) learning model parameters by maximizing the fitting of model to data and the retrieval performance simultaneously. The proposed method (PLSA-FK) is empirically evaluated over three datasets, and the results exhibit promising performance.