• 대한인간공학회지
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• 제36권3호
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• pp.255-265
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• 2017

Objective: This study aims to survey visual search performance models to assess and predict individual's visual tasks in everyday life and industrial sites. Background: Visual search is one of the most frequently performed and critical activities in everyday life and works. Visual search performance models are needed when designing or assessing the visual tasks. Method: This study was mainly based on survey of literatures related to ergonomics relevant journals and web surfing. In the survey, the keywords of visual search, visual search performance, visual search model, etc. were used. Results: On the basis of the purposes, developing methods and results of the models, this study categorized visual search performance models into six groups: probability-based models, SATO models, visual lobe-based models, computer vision models, neutral network-based models and detection time models. Major models by the categories were presented with their advantages and disadvantages. More models adopted the accuracy among two factors of accuracy and speed characterizing visual tasks as dependent variables. Conclusion: This study reviewed and summarized various visual search performance models. Application: The results would be used as a reference or tool when assessing the visual tasks.

• 대한수학교육학회지:수학교육학연구
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• 제27권4호
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• pp.791-810
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• 2017

본 연구는 비례 추론에서 형식적 절차의 기계적 사용에 대한 비판과 이에 대한 대안으로 제시되는 시각적 모델의 사용에 대한 연구를 바탕으로 비례 추론 수업에서 시각적 모델의 활용 가능성을 탐색했다. 이를 위해 6학년 2학기 비례식과 비례배분 단원을 비표, 이중수직선, 이중테이프 모델을 활용한 수업으로 구성하여 한 학급에 적용하였다. 그 결과 시각적 모델이 비례의 의미를 이해하고 비의 성질 및 비례식의 성질을 발견하는 데, 그리고 비례식 문제 및 비례배분 문제를 해결하는 데 중요한 역할을 할 수 있음을 알 수 있었다. 또한 이러한 시각적 모델을 활용하는 데 학생들이 겪는 어려움과 이를 지도할 때 유의할 점이 있음을 확인하였다. 이를 통해 시각적 모델을 적극적으로 활용한 교과서 개발 및 비례 추론 수업에 대한 지도 방안을 마련하는 데 시사점을 제공할 수 있기를 기대한다.

• 한국의류학회지
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• 제34권9호
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• pp.1567-1580
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• 2010

This study investigates the effects of visual texture on color emotion and establishes prediction models for color emotion by both physical color properties and visual texture characteristics. A variety of fabrics including silk, cotton, and flax were colored by digital textile printing according to chromatic hue and tone combinations that are evaluated in terms of color emotion. Subjective visual texture ratings are also obtained for gray-colored same fabrics to those used in color emotion tests. As a result, fabric clusters by visual texture factors showed significant differences in color emotion factors that are primarily affected by physical color properties. Finally prediction models for color emotion factors by both physical color properties and visual texture clusters were established, which has a potential to be used to explain color emotion according to the visual texture characteristics of fabrics.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2009년도 추계학술대회
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• pp.54-57
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• 2009

This study was aimed to investigate the effects of visual texture on color emotion and to establish prediction models for color emotion by both physical color properties and visual texture characteristics. A variety of fabrics were printed by digital printer according to hue and tone combinations. Subjective sensation was evaluated in terms of visual texture for fabrics printed in gray whereas color emotion for those in chromatically printed. As results, fabric clusters by visual texture showed significant differences in color emotion factors and the differences were clearer for grayish tone fabrics. Prediction models for color emotion factors by both physical color properties and visual texture clusters were proposed as for all fabrics and grayish ones, respectively.

• 한국실내디자인학회논문집
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• 제22권1호
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• pp.105-114
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• 2013

This study investigated the application and the change of various architectural models by analyzing expression viewpoint media, which were applied to the visual information of digitalized 3D contemporary architectural models. The purpose of this study was to specify how modern architects have changed 3D architectural models to conceptual, logical, and formational visual information in the process of design. This study discovered a framework of analyses by theoretically investigating a relationship between expression media and expression change in the process of visualizing architectural models. Using the framework of analyses, this study analyzed how the expression viewpoints of architectural model information have been changed and applied. The transformation media of the visual information of digitalized 3D architectural models can be classified into conceptual, analytical, and formational information: 1) Contemporary architects used author-centered subjective viewpoints to express architectural concepts, which were generated in the process of their design. They selected a perspective viewpoint and a bird's eye view in order to present their architectural concepts and to depict them with one architectural model by expanding the visual scope of conceptual information. 2) Contemporary architects adopted observer-centered objective bird's eye view expression media to effectively present their architectural information to building owners and viewers. They used transformal media, which integrate architectural information into 3D and change it to different scales, in order to express their architecture logically. 3) Contemporary architects delivered model information about the generation and change of forms by expressing the image of a project from an author-centered viewpoint, instead of objectively defining formational information. They explained the generation principle of architectural forms via transformal media which develop and rotate an architectural model.

• 대한산업공학회지
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• 제29권2호
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• pp.165-171
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• 2003

Visual search in an unstructured search field is a fruitful research area for computational modeling. Search models that describe relationship between search time and probability of target detection have been used for prediction of human search performance and provision of ideal goals for search training. Until recently, however, most of models were focused on detecting a single target in a search field, although, in practice, a search field includes multiple targets and search models for multiple targets may differ from search models for a single target. This study proposed a random search model for multiple targets, generalizing a random search model for a single target which is the most typical search model. To test this model, human search data were collected and compared with the model. This model well predicted human performance in visual search for multiple targets. This paper also proposed how to determine optimal stopping time in multiple-target search.

• 한국IT서비스학회지
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• 제16권1호
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• pp.55-72
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• 2017

Many of the studies of visual attention that are currently underway are seeking ways to make application systems that can be used in practice, and obtained good results using not only simulated images but also real-world images. However, despite that previous studies of selective visual attention are models intended to implement the human vision, few experiments verified the models with actual humans and there is no standardized data nor standardized experimental method for actual images. Therefore, in this paper, we propose a new performance evaluation techniques necessary for evaluation of visual attention systems. We developed an evaluation method for evaluating the performance of the visual attention system through comparison with the results of the human experiments on visual attention. Human experiments on visual attention is an experiments where human beings are instinctively aware of the unconscious when images are given to humans. So it can be useful for evaluating performance of the bottom-up attention system. Also we propose a new selective attention system that guides the user to effectively detect ROI regions by using spatial and temporal features adaptively selected according to the input image. We evaluated the performance of proposed visual attention system through the developed performance evaluation method, and we could confirm that the results of the visual attention system are similar to those of the human visual attention.

• 로봇학회논문지
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• 제14권2호
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• pp.114-121
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• 2019

This paper presents a new benchmark system for visual odometry (VO) and monocular depth estimation (MDE). As deep learning has become a key technology in computer vision, many researchers are trying to apply deep learning to VO and MDE. Just a couple of years ago, they were independently studied in a supervised way, but now they are coupled and trained together in an unsupervised way. However, before designing fancy models and losses, we have to customize datasets to use them for training and testing. After training, the model has to be compared with the existing models, which is also a huge burden. The benchmark provides input dataset ready-to-use for VO and MDE research in 'tfrecords' format and output dataset that includes model checkpoints and inference results of the existing models. It also provides various tools for data formatting, training, and evaluation. In the experiments, the exsiting models were evaluated to verify their performances presented in the corresponding papers and we found that the evaluation result is inferior to the presented performances.

• 문화기술의 융합
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• 제4권3호
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• pp.179-184
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• 2018

본 연구는 2000년대 이후 신문광고에 나타난 아파트 분양 광고의 시각적 표현 변화에 대해 살펴보고자 2010년~2018년도의 광고물을 대상으로 내용을 비교하여 조사하였다. 부산지역 신문광고를 대상으로 아파트 분양 광고의 시각적 표현 유형 및 광고 모델을 분석하였다. 브랜드(전국 브랜드, 지역 브랜드)에 따라 시각적 표현 유형(카피 중심, 비주얼 중심, 혼합 형식)과 광고 모델 사용 여부(사용, 비사용)로 분류하여 조사하였다. 연구결과, 2010년~2011년도의 아파트 분양 광고에서는 전체 브랜드에서 비주얼 중심 광고가 가장 많이 사용되었고, 지역 브랜드에서 더 높은 이용도가 나타났다. 이에 반해 2017년~2018년도에서는 혼합 형식의 표현이 높아진 것으로 시각적 표현에 변화가 나타났다. 특히 지역 브랜드에서 비주얼 중심 광고의 비중이 현저히 낮아졌다. 광고 모델 사용에 관해서 살펴보면, 2010년~2011년도 대부분의 브랜드에서 광고 모델을 사용하였으나, 2017년~2018년도에는 아파트 분양 광고에서 광고 모델은 거의 사라진 것으로 나타났다. 특히 지역 브랜드에서 차이점이 더 크게 나타났다. 이러한 결과는 아파트 분양 광고의 표현 전략 차이가 브랜드 간에 존재하는 것으로 해석된다. 이와 같은 연구 결과들은 아파트 분양광고의 시각적 표현 전략에 유용한 실무적 시사점을 제공할 것으로 기대한다.

• Communications for Statistical Applications and Methods
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• 제29권2호
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• pp.161-176
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• 2022

There has been active research in image classification using deep learning convolutional neural network (CNN) models. ImageNet large-scale visual recognition challenge (ILSVRC) (2010-2017) was one of the most important competitions that boosted the development of efficient deep learning algorithms. This paper introduces and compares six monumental models that achieved high prediction accuracy in ILSVRC. First, we provide a review of the models to illustrate their unique structure and characteristics of the models. We then compare those models under a unified framework. For this reason, additional devices that are not crucial to the structure are excluded. Four popular data sets with different characteristics are then considered to measure the prediction accuracy. By investigating the characteristics of the data sets and the models being compared, we provide some insight into the architectural features of the models.