• Korean Journal of Optics and Photonics
• /
• v.11 no.2
• /
• pp.85-90
• /
• 2000

The image quality of CRT displays is descnbed by the physical characteristics such as luminance, contrast, address ability, viewing distance, and so on. However, the final evaluation of rmage quality is deteITIlllled by not phYSICal data, but subjective perceived Image quality. Therefore the correlation is compared the score of subjective image qualIty accompli~hed by several persons with calculated SQRI (SQuate Root Integral) value uSlllg 4 kInds of CRT monitors. And the influence of tile physical chatac1eristics for subjective Image quality is analyzed on the basis of the validity of SQRI method as a metric for the evaluation of subjective image quality. ality.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.7 no.2
• /
• pp.63-68
• /
• 1989

This study was to develope the methmatic prediction model of accuracy for the convergent photographs using nonmetric camera in close range photogrammetry. By analyzing positioning error on object distance and convergent angles, the validity of the new formulae for prediction of accuracy were proved. Rational design of camera systems and convergent angles according to accuracy demands in plane and height were developed using these formulae.

• Journal of Information Processing Systems
• /
• v.13 no.5
• /
• pp.1345-1357
• /
• 2017

A prototype selection method chooses a small set of training points from a whole set of class data. As the data size increases, the selected prototypes play a significant role in covering class regions and learning a discriminate rule. This paper discusses the methods for selecting prototypes in a classification framework. We formulate a prototype selection problem into a set covering optimization problem in which the sets are composed with distance metric and predefined classes. The formulation of our problem makes us draw attention only to prototypes per class, not considering the other class points. A training point becomes a prototype by checking the number of neighbors and whether it is preselected. In this setting, we propose a greedy algorithm which chooses the most relevant points for preserving the class dominant regions. The proposed method is simple to implement, does not have parameters to adapt, and achieves better or comparable results on both artificial and real-world problems.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.05a
• /
• pp.56-59
• /
• 2019

분산 해시 테이블은 {Key-Value} 형태의 해시 테이블을 시스템 내 노드들이 나누어 가지는 분산 시스템이다. 분산 해시 테이블 중 Kademlia는 Binary Tree 구조를 통해 노드 확장성을 가지고 XOR Metric을 이용하여 빠른 노드 탐색으로 다양한 분야에서 활용되고 있다. 하지만 노드 탐색 시 실제 상황을 배제하고 논리적인 거리만을 고려하여 라우팅 경로를 설정한다는 문제점을 가진다. 본 연구에서는 이 문제를 해결하기 위해 노드 탐색 시 노드 간의 대역폭과 물리적 거리를 고려하여 라우팅 테이블을 생성하는 Kademlia의 효율적인 노드 탐색 기법을 제안한다. 기존의 Kademlia와 유사한 수치의 Lookup Success Ratio와 Lookup Overhead Rates를 보이지만, End-to-End Delay가 감소한 것을 시뮬레이션을 통해 확인하였다.

• Journal of the Semiconductor & Display Technology
• /
• v.17 no.1
• /
• pp.71-75
• /
• 2018

Image enhancement is a necessary end essential step after taking a picture with a digital camera. Many different photo software packages attempt to automate this process with various auto enhancement techniques. This paper provides and implements a system that can learn a user's preferences and apply the preferences into the process of image enhancement. Five major components are applied to the implemented system, which are computing a distance metric, finding a training set, finding an optimal parameter set, training and finally enhancing the input image. To estimate the validity of the method, we carried out user studies, and the fact that the implemented system was preferred over the method without learning user preferences.

• Journal of Korea Multimedia Society
• /
• v.22 no.1
• /
• pp.27-34
• /
• 2019

In this paper, a new type of lip feature is proposed as distance metric in CIELUV color system. The performance of the proposed feature was tested on face image database, Helen dataset from University of Illinois. The test processes consists of three steps. The first step is feature extraction and second step is principal component analysis for the optimal projection of a feature vector. The final step is Otsu's threshold for a two-class problem. The performance of the proposed feature was better than conventional features. Performance metrics for the evaluation are OverLap and Segmentation Error. Best performance for the proposed feature was OverLap of 65% and 59 % of segmentation error. Conventional methods shows 80~95% for OverLap and 5~15% of segmentation error usually. In conventional cases, the face database is well calibrated and adjusted with the same background and illumination for the scene. The Helen dataset used in this paper is not calibrated or adjusted at all. These images are gathered from internet and therefore, there are no calibration and adjustment.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2018.11a
• /
• pp.33-35
• /
• 2018

기존의 사람간의 유사성 측정 시스템은 적외선 빔이나 열 감지 영상 장치를 통해 측정하였다. 하지만 이와 같은 방법으로 측정하면 2명 이상의 객체를 분류해내는 기술은 제공하지 않는다. 이에 본 논문은 고정된 카메라를 이용하여 각 사람의 피부색과 옷차림 등의 RGB 정보를 이용한 사람 유사성 측정 기법을 제안한다. RGB카메라 영상을 통하여 객체의 RGB 히스토그램을 얻은 후 각 객체에 대해 Bhattacharyya metric, Cosine similarity, Jensen difference, Euclidean distance로 histogram similarity를 계산하여 객체 추적 및 유사성 측정을 통해 객체를 분류한다. 제안된 시스템은 C/C++를 기반으로 구현하여, 유사성 측정 성능을 평가하였다.

• International Journal of Computer Science & Network Security
• /
• v.21 no.5
• /
• pp.211-216
• /
• 2021

This study intended to test the structure of the latent factor of a subjective happiness scale and the stability of invariance across groups of students' classifications (gender and students' status). In the large, non-clinical sample (619), students completed the subjective happiness scale. The (CFA) confirmatory factor analysis was used to investigate the factor-structure of the measure, and multiple-group confirmatory factor analysis (MGCFA) model was used to test the stability of invariance across groups of students classifications. The findings of the CFA indicated support for the original one-factor model. Additional analyses of the MGCFA method support the measurement (configural, metric and strong) invariant and practical invariant components of this model. There was an invariant across gender. There was partially invariant across groups of students' statuses. The scale exists in both groups to assess the same concepts of (single and married), excluding Items 3 and 4. Given that this study is the first investigation for the structure of the subjective happiness scale.

• International Journal of Computer Science & Network Security
• /
• v.21 no.12
• /
• pp.149-156
• /
• 2021

This study intended to test the structure of the latent factor of an effectiveness scale and the stability of invariance across groups of students' classifications (gender and levels of education). In the large, non-clinical sample (850), students completed the effectiveness scale. The (CFA) confirmatory factor analysis was used to investigate the factor-structure of the measure, and multiple-group confirmatory factor analysis (MGCFA) model was used to test the stability of invariance across groups of students' classifications. The findings of the CFA indicated support for the original four-factor model. Additional analyses of the MGCFA method support the measurement (configural, metric and strong) invariant and practical invariant components of this model. There was an invariant across gender. There was partially invariant across groups of levels of education. The scale exists in groups of levels of education assess the same concepts of, excluding Items 15 and 10. Given that this study is the first investigation for the structure of the effectiveness scale.

• The Journal of the Acoustical Society of Korea
• /
• v.19 no.2
• /
• pp.40-45
• /
• 2000

In speech corpus generation and speech recognition, it is sometimes needed to segment the input speech data without any prior knowledge. A method to accomplish this kind of segmentation, often called as blind segmentation, or acoustic segmentation, is to find boundaries which minimize the Euclidean distances among the feature vectors of each segments. However, the use of this metric alone is prone to errors because of the fluctuations or variations of the feature vectors within a segment. In this paper, we introduce the principal component analysis method to take the trend of feature vectors into consideration, so that the proposed distance measure be the distance between feature vectors and their projected points on the principal components. The proposed distance measure is applied in the LBDP(level building dynamic programming) algorithm for an experimentation of continuous speech segmentation. The result was rather promising, resulting in 3-6% reduction in deletion rate compared to the pure Euclidean measure.