• 패션비즈니스
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• 제8권6호
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• pp.103-112
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• 2004

A Politician Appearance Acceptance Model (PAAM model) was formed and designed based on an analysis of how the electorate would accept a female politician. The PAAM model evaluated factors which influenced the voter's view of the female politician based on appearance. Causative factors were assessed that impacted acceptance based on appearance and analyzed whether voting was influenced by the appearance image; appearance image preferences for a female politician included the classic, dramatic, romantic and natural images. Through validations, the appearance image and competency had a causative factor that contributed to the acceptance of the politician image. The Classic Image demonstrated the strongest and most important image among the appearance images. As voters were more interested in the appearance image of a female politician, more emphasis and weight was on the appearance image during the voting selection process.

• 한국멀티미디어학회논문지
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• 제18권3호
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• pp.349-358
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• 2015

Simulating color appearance of makeup effect is an important issue in computer graphics as well as cosmetic industry. Most of previous works on makeup simulation are simple color blending to mimic the cosmetic effects. Some of previous works employed Kubelka-Munk model to accurately simulate the layering effect of cosmetics. However, the simulation limited on single point, and the rest of area are still computed by simple color blending utilizing the color of the single point simulation. This paper presents an image-based method to compute the color appearance effect of makeup application using per-pixel Kubelka-Munk model. Unlike the previous methods, it is possible to compute per-pixel application thickness as well as optical property of cosmetics. The computed thickness pattern can be used in makeup simulation for a more realistic makeup simulation.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제5권1호
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• pp.43-50
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• 2016

본 논문에서는 ASM(Active Shape Model) 특징점(Landmark)을 이용하여 정밀한 얼굴영역을 획득하고, 외형기반 접근법으로 표정을 인식하는 방법에 대하여 제안한다. 외형기반 표정인식은 EHMM(Embedded Hidden Markov Model) 및 이진패턴 히스토그램 특징과 SVM(Support Vector Machine)을 사용하는 알고리즘으로 구성되며, 제안 방법의 성능평가는 공인 CK 데이터베이스와 JAFFE 데이터베이스를 이용하여 수행되었다. 더불어, 성능비교는 기존의 눈 거리 기반의 얼굴 정규화 방법과 비교를 통하여 수행되었고, 또한 ASM 전체 특징점 및 변형된 특징을 SVM으로 인식하는 기하학적 표정인식 방법론과 성능비교를 수행하였다. 실험 결과, 제안 방법은 거리기반 얼굴정규화 영상을 사용한 방법보다 CK 데이터베이스 및 JAFFE 데이터베이스 경우, 최대 6.39%와 7.98%의 성능향상을 보였다. 또한, 제안 방법은 기하학적 특징점을 사용한 방법보다 높은 인식 성능을 보였으며, 이로부터 제안하는 표정인식 방법의 효용성을 확인하였다.

• 한국의류학회지
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• 제29권8호
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• pp.1136-1145
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• 2005

The objective of this study was to determine factors affecting sun protective behavior and intention to buy UV protective clothing among outdoor sport persons in Korea. A self-administered questionnaire was distributed to mountain climbers, bikers and in-line skaters with a convenience sampling method. Two theoretical frameworks, health belief(HB) model and diffusion theory(DT) were used for the study. Based on HB model, sun protection behaviors(SPBs) were associated with cancer perception, perceived benefits, behavioral barriers and cues to actions based on the HB model. Based on DT model, intention to buy (ITB) was determined by an individual's perceived attributes of UV protective shirt. Appearance concern variables were added to the extended HB model. The extended DT model was proposed by adding the variables in the HB model and variables of appearance concern. Multiple regression analysis was applied. Results were as follows. First, perceived benefits, behavioral barriers, cues to action, and all three appearance concern variables as well as gender and age were significant determinants of SPB for Korean outdoor consumers. Second, relative advantage, compatibility, friability, behavioral barriers and cues to action with some appearance concern variables were significant in affecting intention to buy UV protective clothes. Extended HB model and extended DT model were useful to understand SPBs of Korean outdoor consumers.

• 한국안전학회지
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• 제24권3호
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• pp.96-101
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• 2009

In this study, a risk-appearance frequency evaluation model for railway level-crossing accidents is developed with the frequency estimation based on the accident history. It follows the worldwide common safety management approach and reflects the operation conditions and accident properties of the domestic railway system. The risk appearance frequency evaluation process contains a development of accident scenarios by defining the system configurations and functions, and a frequency estimation of hazardous events based on the accident history. The developed model is verified with the accident history during 5 years('03-'07) for 3 hazardous events: 'Being trapped in level crossing(Hl)', 'Crossing during warning signal(H2)' and 'Breaking through/detouring the barrier(H3)'. This risk appearance frequency evaluation model will be combined with a consequence evaluation model so as to offer full risk assessment for the railway accident. The accident risk assessment will contribute to improving the safety management of the railway system.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제6권1호
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• pp.39-46
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• 2006

We propose an global color model based method for tracking motions of multiple human using a networked multiple-camera system in intelligent space as a human-robot coexistent system. An intelligent space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of intelligent space as well. One of the main goals of intelligent space is to assist humans and to do different services for them. In order to be capable of doing that, intelligent space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and intelligent space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in intelligent space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• 한국산업융합학회 논문집
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• 제22권1호
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• pp.39-45
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• 2019

We propose an active color model based method for tracking motions of multiple human using a networked multiple-camera system in IoT space as a human-robot coexistent system. An IoT space is a space where many intelligent devices, such as computers and sensors(color CCD cameras for example), are distributed. Human beings can be a part of IoT space as well. One of the main goals of IoT space is to assist humans and to do different services for them. In order to be capable of doing that, IoT space must be able to do different human related tasks. One of them is to identify and track multiple objects seamlessly. In the environment where many camera modules are distributed on network, it is important to identify object in order to track it, because different cameras may be needed as object moves throughout the space and IoT space should determine the appropriate one. This paper describes appearance based unknown object tracking with the distributed vision system in IoT space. First, we discuss how object color information is obtained and how the color appearance based model is constructed from this data. Then, we discuss the global color model based on the local color information. The process of learning within global model and the experimental results are also presented.

• 스마트미디어저널
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• 제6권3호
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• pp.49-56
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• 2017

Images and Videos that include the human face contain a lot of information. Therefore, accurately extracting human face is a very important issue in the field of computer vision. However, in real life, human faces have various shapes and textures. To adapt to these variations, A model-based approach is one of the best ways in which unknown data can be represented by the model in which it is built. However, the model-based approach has its weaknesses when the motion between two frames is big, it can be either a sudden change of pose or moving with fast speed. In this paper, we propose an enhanced human face-tracking model. This approach included human face detection and motion estimation using Cascaded Convolutional Neural Networks, and continuous human face tracking and modeling correction steps using the Active Appearance Model. A proposed system detects human face in the first input frame and initializes the models. On later frames, Cascaded CNN face detection is used to estimate the target motion such as location or pose before applying the old model and fit new target.

• 한국의류학회지
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• 제21권5호
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• pp.807-820
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• 1997

This exploratory research was intended to develop and test a slacks pattern based upon conic model. Data came from measurements of photograph of three subjects. Silhouette of slacks was close fit on waist and hip and loose under hip level. This three-dimensional form was modeled with truncated cones. This conic model was truncated by plane of the waist level, the abdomen level, the hip level, the crotch level and the ankle level parallel to the floor. Two models that have differences in back part of the model were tested. Drafted patterns from two models were taken for each subject. Drafted experimental pattern was operated for slacks pattern. The first step was to make sideseam. Parts over crotch level were moved to both sides. And then to make waistdarts. Experimental pattern had two darts each in front and back. Each experimental slacks was evaluated by sensory test to appearance and comfort by five judges. The results can be summarized as follows. 1) Appearance and comfort of experimental pattern were judged to be satisfactory. Especially these patterns were fitted we18 in waist darts front and back. So we evaluated that was proper pattern for slacks. 2) Model 2 was better in appearance than model 1. But Model 1 was more comfortable in non significant difference. This was supposed to be resulted from fit more closely of model 2. 3) considering preceding results, this geometric model based upon truncated cone was applicable for slacks pattern.

• International journal of advanced smart convergence
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• 제4권2호
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• pp.20-28
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• 2015

In this paper, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame with posture variation and camera view point adaptation by employing the non-adaptive random projections that preserve the structure of the image feature space of objects. The existing online tracking algorithms update models with features from recent video frames and the numerous issues remain to be addressed despite on the improvement in tracking. The data-dependent adaptive appearance models often encounter the drift problems because the online algorithms does not get the required amount of data for online learning. So, we propose an effective tracking algorithm with an appearance model based on features extracted from a video frame.