• Journal of KIISE:Databases
• /
• v.30 no.4
• /
• pp.362-371
• /
• 2003

In this paper, we propose an optimal interest point extraction method to support shape-base image classification and indexing for image database by applying a dynamic threshold that reflects the characteristics of the shape contour. The threshold is determined dynamically by comparing the contour length ratio of the original shape and the approximated polygon while the algorithm is running. Because our algorithm considers the characteristics of the shape contour, it can minimize the number of interest points. For n points of the contour, the proposed algorithm has O(nlogn) computational cost on an average to extract the number of m optimal interest points. Experiments were performed on the 70 synthetic shapes of 7 different contour types and 1100 fish shapes. It shows the average optimization ratio up to 0.92 and has 14% improvement, compared to the fixed threshold method. The shape features extracted from our proposed method can be used for shape-based image classification, indexing, and similarity search via normalization.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 1999.11a
• /
• pp.416-419
• /
• 1999

We Present a technique for recognizing facial expressions from image sequences. The technique uses muscle-based feature models for tracking facial features. Since the feature models are constructed with a small number of parameters and are deformable in the limited range and directions, each search space for a feature can be limited. The technique estimates muscular contractile degrees for classifying six principal facial express expressions. The contractile vectors are obtained from the deformations of facial muscle models. Similarities are defined between those vectors and representative vectors of principal expressions and are used for determining facial expressions.

• Annual Conference of KIPS
• /
• 2000.10a
• /
• pp.695-698
• /
• 2000

본 논문에서는 이미지 데이터 컬러 속성을 기반으로 한 영상 검색 방법을 제안한다. 두 이미지 사이의 유사성을 측정하기 위하여 컬러 히스토그램의 분포 특성을 이미지 데이터베이스 영상과질의 영상에서 계산하여 유사도를 결정하도록 설계하였다. 두 영상의 유사도를 측정하기 위해 두영상의 R, G, B 히스토그램에 대해서 같은 값에 대한 빈도 수의 차를 거리로 측정한 후, 구해진 거리의 차를 비교한 방법과 히스토그램의 분포 곡선을 이루는 방정식을 구한 수 있도록 곡선 정합을 한 후에 두 영상의 컬러 특징 속성에 관한 특징 값의 추출을 위해서 다항식 보간법에 의한 방정식을 이용한 방법을 소개한다. 공간 데이터베이스 시스템에서 질의에 대한 효율적인 처리를 위해 R-Tree와 최대 점을 이용하여 영상을 검색한다.

• Journal of Broadcast Engineering
• /
• v.22 no.6
• /
• pp.713-723
• /
• 2017

Since the panoramic image can overcome the limitation of the viewing angle of the camera and have a wide field of view, it has been studied effectively in the fields of computer vision and stereo camera. In order to generate a panoramic image, stitching images taken by a plurality of general cameras instead of using a wide-angle camera, which is distorted, is widely used because it can reduce image distortion. The image stitching technique creates descriptors of feature points extracted from multiple images, compares the similarities of feature points, and links them together into one image. Each feature point has several hundreds of dimensions of information, and data processing time increases as more images are stitched. In particular, when a panorama is generated on the basis of an image photographed by a plurality of unspecified cameras with respect to an object, the extraction processing time of the overlapping feature points for similar images becomes longer. In this paper, we propose a preprocessing process to efficiently process stitching based on an image obtained from a number of unspecified cameras for one object or environment. In this way, the data processing time can be reduced by pre-grouping images based on camera sensor information and reducing the number of images to be stitched at one time. Later, stitching is done hierarchically to create one large panorama. Through the grouping preprocessing proposed in this paper, we confirmed that the stitching time for a large number of images is greatly reduced by experimental results.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.46 no.6
• /
• pp.58-67
• /
• 2009

A correspondence matching is one of the important tasks in computer vision, and it is not easy to find corresponding points in variable environment where a scale, rotation, view point and illumination are changed. A SURF(Speeded Up Robust Features) algorithm have been widely used to solve the problem of the correspondence matching because it is faster than SIFT(Scale Invariant Feature Transform) with closely maintaining the matching performance. However, because SURF considers only gray image and local geometric information, it is difficult to match corresponding points on the image where similar local patterns are scattered. In order to solve this problem, this paper proposes an extended SURF algorithm that uses the invariant color and global geometric information. The proposed algorithm can improves the matching performance since the color information and global geometric information is used to discriminate similar patterns. In this paper, the superiority of the proposed algorithm is proved by experiments that it is compared with conventional methods on the image where an illumination and a view point are changed and similar patterns exist.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.5
• /
• pp.1-10
• /
• 2024

Attackers tend to use similar vulnerabilities when finding their next target IT assets. They also continuously search for new attack targets. Therefore, it is essential to find the potential targets of attackers in advance. Our method proposes a novel approach for efficient vulnerable asset management and zero-day response. In this paper, we propose the ability to detect the IT assets that are potentially infected by the recently discovered vulnerability based on clustering and similarity results. As the experiment results, 86% of all collected assets are clustered within the same clustering. In addition, as a result of conducting a similarity calculation experiment by randomly selecting vulnerable assets, assets using the same OS and service were listed.

• Proceedings of the Korea Multimedia Society Conference
• /
• 2002.11b
• /
• pp.381-384
• /
• 2002

본 논문에서는 실험 영상으로부터 학습된 피부색상 정보를 이용하여 컬러 공간테이블을 생성한 후. 입력된 영상의 컬러와 공간정보를 학습된 피부색상 공간테이블로부터 비교, 분석하여 얼굴후보영역을 찾고자 하였다. 또한 추출된 후보영역의 레이블된 특징정보를 이용하여 지역적 특징을 찾아낸 후 얼굴 특징점의 위치에 따른 형태정보를 이용하여 신뢰할 수 있는 얼굴 영역을 검출하고자 하였다. 제안된 피부색상(Skin-tone)공간테이블은 변환하기 쉽고 계산이 빠른 RGB컬러 공간에서 실험, 평가되었으며, 실시간으로 입력된 영상의 정규화된 책상 값을 유사성 정도에 따라 레이블링하여 보다 빠른 얼굴 후보 영역의 검출과 검증을 할 수 있도록 하였다.

• Journal of the Institute of Electronics Engineers of Korea SP
• /
• v.44 no.4 s.316
• /
• pp.86-92
• /
• 2007

A discrete dynamic model for tracking feature points in 2D images is developed. Conventional snake approaches deform a contour to lock onto features of interest within an image by finding a minimum of its energy functional, composed of internal and external forces. The neighborhood around center snaxel is a space matrix, typically rectangular. The structure of the model proposed in this paper is a set of connected vertices. Energy model is designed for its local minima to comprise the set of alternative solutions available to active process. Block on tracking is one dimension, line type. Initial starting points are defined to the satisfaction of indent states, which is then automatically modified by an energy minimizing process. The track is influenced by curvature constraints, ascent/descent or upper/lower points. The advantages and effectiveness of this layer approach may also be applied to feature points tracking of digital image whose pixels have one directional properties with high autocorrelation between adjacent data lines, vertically or horizontally. The test image is the ultrasonic carotid artery image of human body, and we have verified its effect on intima/adventitia starting points tracking.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2016.06a
• /
• pp.340-343
• /
• 2016

현재 사용 되어지고 있는 대부분의 영상 합성 알고리즘들은 영상의 크기가 클수록 계산량이 많아지는 특징이 있다. 따라서 UHD 영상을 대상으로 영상 합성을 할 때 속도가 기존의 HD 영상에 비해 크게 느리다는 점과 특수 카메라 사용 혹은 카메라 위치 고정 등의 제한이 필요하다는 점의 단점들이 있다. 이에 본 논문에서는 스마트폰에서 콘텐츠를 촬영한 영상들을 이용하여 영상 분류, Map 생성, Rendering 의 과정을 통해 현재 사용 되어지고 있는 영상 합성 알고리즘과 비교하여 보다 빠르게 영상 합성을 할 수 있는 알고리즘을 제안한다. 촬영 시 EXIF 에 저장되는 GPS 정보를 이용하여 그룹화를 진행한 후 영상의 특징점 매칭을 통해 Map 을 생성하는 것이 본 논문에서 제안하는 알고리즘이다. 이를 위해 본 논문에서는 FAST 특징점 알고리즘과 FREAK 기술자 알고리즘을 사용하였으며 Rendering 을 통하여 해당 알고리즘을 검증 하였다.

• Journal of Navigation and Port Research
• /
• v.28 no.3
• /
• pp.185-191
• /
• 2004

The mean and Clustering are important methods of data mining, which is now widely applied to various multi-attributes problem However, feature weighting and feature selection are important in those methods bemuse features may differ in importance and such differences need to be considered in data mining with various multiful-attributes problem. In addition, in the event of arithmetic mean, which is inadequate to figure out the most fitted result for structure of evaluation with attributes that there are weighted and ranked. Moreover, it is hard to catch hold of a specific character for assume the form of user's group. In this paper. we propose a dispersion mean algorithm for evaluation of similarity measure based on the geometrical figure. In addition, it is applied to mean classified by user's group. One of the key issues to be considered in evaluation of the similarity measure is how to achieve objectiveness that it is not change over an item ranking in evaluation process.