• Journal of the Ergonomics Society of Korea
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• v.36 no.3
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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.

• Journal of Educational Research in Mathematics
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• v.27 no.4
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• pp.791-810
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• 2017

This study explored the possibility of using visual models in teaching proportional reasoning based on the review of previous studies. Many studies on proportional reasoning emphasize that students tend to simply apply formal procedures without understanding the meaning behind them and that using visual models may be an alternative to help students develop informal strategies and proportional reasoning. Given these, we re-constructed and implemented the unit of a textbook to teach sixth graders proportional reasoning with ratio table, double number line, and double tape diagram. The results of this study showed that such visual models helped students understand the meaning of proportion, explore the properties of proportion, and solve proportional problems. However, several difficulties that students experienced in using the visual models led us to suggest cautionary notes when to teach proportional reasoning with visual models. As such, this study is expected to provide empirical information for textbook developers and teachers who teach proportional reasoning with visual models.

• Journal of the Korean Society of Clothing and Textiles
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• v.34 no.9
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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.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2009.11a
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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.

• Korean Institute of Interior Design Journal
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• v.22 no.1
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.29 no.2
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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.

• Journal of Information Technology Services
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• v.16 no.1
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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.

• The Journal of Korea Robotics Society
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• v.14 no.2
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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.

• The Journal of the Convergence on Culture Technology
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• v.4 no.3
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• pp.179-184
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• 2018

The purpose of this study was to investigate changes to visual expressions in apartment sales ads in newspapers since the 2000s. For this purpose, the study compared and examined advertisements in 2010~2018 for their content. Apartment sales ads in the newspapers of Busan were analyzed according to the types of visual expressions and advertisement models. The brands(national and local brands) were categorized according to the types of visual expressions(copywriting-centric, visual-centric, and mixed ones) and whether an advertisement model was used or not(used and not used). The findings show that visual-centric ads were most used in apartment sales ads by national brands in 2010~2011 and recorded a higher usage rate by local brands during the same period. In 2017~2018, mixed forms of visual expressions were used more in the ads. The percentage of visual-centric ads made a considerable decrease among local brands. As for the use of advertisement models, most of the brands used one in 2010~2011, but advertisement models almost disappeared in apartment sales ads in 2017~2018. These differences were more prominent among local brands. The findings indicate that there were differences in expressive strategies in apartment sales ads among brands. The findings are expected to provide useful practical implications for the visual expression strategies of apartment sales ads.

• Communications for Statistical Applications and Methods
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• v.29 no.2
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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.