• 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 the Ergonomics Society of Korea
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• v.28 no.3
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• pp.55-62
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• 2009

This study is concerned with predicting human search performance using a visual lobe. The most previous studies on human performance in visual search have been limited to a single-target search. This study extended the visual search research to multiple-target search including targets of different types as well as targets of same types. A model for predicting visual search performance was proposed and the model was validated by human search data. Additionally, this study found that human subjects always did not use a constant ratio of the whole visual lobe size for each type of targets in visual search process. The more conspicuous the target is, the more ratio of the whole visual lobe size human subjects use. The model that can predict human performance in multiple-target search may facilitate visual inspection plan in manufacturing.

• 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 the Ergonomics Society of Korea
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• v.31 no.6
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• pp.705-713
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• 2012

Objective: The aim of this study is to predict human visual target search using ACT-R cognitive architecture in real scene images. Background: Human uses both the method of bottom-up and top-down process at the same time using characteristics of image itself and knowledge about images. Modeling of human visual search also needs to include both processes. Method: In this study, visual target object search performance in real scene images was analyzed comparing experimental data and result of ACT-R model. 10 students participated in this experiment and the model was simulated ten times. This experiment was conducted in two conditions, indoor images and outdoor images. The ACT-R model considering the first saccade region through calculating the saliency map and spatial layout was established. Proposed model in this study used the guide of visual search and adopted visual search strategies according to the guide. Results: In the analysis results, no significant difference on performance time between model prediction and empirical data was found. Conclusion: The proposed ACT-R model is able to predict the human visual search process in real scene images using salience map and spatial layout. Application: This study is useful in conducting model-based evaluation in visual search, particularly in real images. Also, this study is able to adopt in diverse image processing program such as helper of the visually impaired.

• Journal of the Ergonomics Society of Korea
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• v.16 no.2
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• pp.29-38
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• 1997

The research on the factors which effect on legibility is mainly utilized as the basic data of selecting the standard guideline of VDT screen. But the research on Korean is scarcer than that of English. Furthermore, it is unreasonable to apply the results of the foreign language to Korean, beause of the difference between the typography of English and that of Korean. Therefore, more systematic and ergonomic research of the Korean typography on VDT screen is needed. In this paper, an experimental study on search speed and error rate is designed and performed according to different Korean letter size and font on search task with VDT. The experimental screen based on popular Ming and Gothic style is made up of total 12 artificial screens, each 6 different font size. As the criteria of the performance, searching speed(s.s.) and error rate (e) are selected, and CFF value is measured to evaluate user's visual fatigue. The results of experiment in font show that the Korean Gothic style is superior to the Korean Ming style in user's visual performance. The letter size that gives user the optimal performance ranges from the visual angle 39.8' to 55.5' in Ming style, from the visual angle 39.8' to 52.6' in Gothic systle. In visual fatigue experiment, the better performance of letter size is, the less tired user feels. And the smaller letter size is, the more tired user feels. There is no relationship between font and user's visual fatigue.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.960-968
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• 2018

The mobile visual search service uses a query image to acquire linkage information through pre-constructed DB search. From the standpoint of this purpose, it would be more useful if you could perform a search on a web-based keyword search system instead of a pre-built DB search. In this paper, we propose a representative query extraction algorithm to be used as a keyword on a web-based search system. To do this, we use image classification labels generated by the CNN (Convolutional Neural Network) algorithm based on Deep Learning, which has a remarkable performance in image recognition. In the query extraction algorithm, dictionary meaningful words are extracted using Wikipedia, and hierarchical categories are constructed using WordNet. The performance of the proposed algorithm is evaluated by measuring the system response time.

• Journal of the Ergonomics Society of Korea
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• v.22 no.3
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• pp.27-44
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• 2003

An experiment was carried out to compare the suitability in visual tasks between flat and conventional (convex) cathode-ray tube (CRT) displays. The subjects performed visual search tasks during 2-h for detecting target words among distracters presented on the screen. The subjects' visual performance was evaluated with average time and number of errors made to complete the tasks. Visual fatigue after the search tasks was also evaluated in terms of degradations in accommodative power and subjective ratings. Difference was not found in task time between the two displays, but flat CRT showed a lower number of errors than conventional CRT. The difference in number of errors was statistically significant at 0=0.05. Although there was no difference between the displays in degradations of accommodative power, results from the subjective ratings showed that flat CRT yields less fatigue than conventional CRT. The results partially support the hypothesis that panel convexity of CRT displays has a significant effect on the performance and fatigue during visual tasks and thus flat CRT is the better display than conventional one.

• Journal of the Korea Society of Computer and Information
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• v.22 no.7
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• pp.47-54
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• 2017

Recently, correlation filter based trackers have shown excellent tracking performance and computational efficiency. In order to enhance tracking performance in the correlation filter based tracker, search area which is image patch for finding target must include target. In this paper, two methods to discriminatively represent target in the search area are proposed. Firstly, search area location is estimated using pyramidal Lucas-Kanade algorithm. By estimating search area location before filtering, fast motion target can be included in the search area. Secondly, we investigate multi-channel Local Edge Pattern(LEP) which is insensitive to illumination and noise variation. Qualitative and quantitative experiments are performed with eight dataset, which includes ground truth. In comparison with method without search area estimation, our approach retain tracking for the fast motion target. Additionally, the proposed multi-channel LEP improves discriminative performance compare to existing features.

• Proceedings of the ESK Conference
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• 1993.10a
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• pp.124-132
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• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.42
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• pp.39-47
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• 1997

There are four parameters (i.e. overall density, local density, grouping, layout complexity) to consider in designing screen of a visual display terminal. Among these, only the optimum level of overall density is known to be about 25~30% by some studies. Therefore, the present experiment is conducted to define the optimum levels of the other parameters to achieve the user's best performance in visual search task. The results are as follows; (1) The function related to the levels of local density and user's search times is shown to be U -shaped. When the level of local density is about 40%, the search time is shorter than those of any other levels. (2) In the experiment of grouping, user's performance is best when the number of group is 5, and the size of group does not exceed visual angle $5^{\circ}$ (0,088rad). (3) The user performance is improved as the layout becomes less complex.