• KIISE Transactions on Computing Practices
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• v.20 no.12
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• pp.670-675
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• 2014

Graphical user interfaces (GUI) in modern software deliver information visually, and a well-designed interface can provide information to the use in an organized and intuitive manner while poorly-designed interfaces can cause visual inconvenience and confusion. In order to effectively deliver information to the user, visual attention should be placed on a prominent location in the image. This paper introduces a method based on a human visual system (HVS) that can improve Metro-style GUIs by reducing a user's workload to visually find information. Our method is designed with spatial mapping and color mapping for buttons in the Metro-style GUI. Also we define a metric for Metro-style GUI effectiveness, including an optimization algorithm. The results show that our method improves the performance of visual search tasks in a Metro-style GUI.

• Journal of the Ergonomics Society of Korea
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• v.19 no.3
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• pp.13-21
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• 2000

Men perceive and react around him through the five senses. Also men give rise to the human sensibility and maintain his emotion. This study doesn't limit working environment to VDT environment, but considers the universal working environment acquiring information by eyesight stimulation. In case of forming an abnormal veiling reflection we consider the form ; a vertical(25%, 50%, 75%)and a horizontal(25%, 50%, 75%). The results from the subjective evaluation are analyzed by SD(Semantic Differentical methodology)of 5 point scale for visibility and nuisance when an abnormal veiling reflection forms on target. In addition, the results of the objective evaluation are suggested by measuring and analyzing EEG of bio-signal for visual sensitivity. The results of this study can apply to basic data which create a guideline of a visual operation. In particular, it can be designed as an illumination environment concerning an ergonomic factor on visual operations, mental stress such as a visual inspection operation, visual information search operation, etc. As a result, we can expect to reduce the visual nuisance and contribute to the improvement of the performance and the uplift of the competitive power.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.2C
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• pp.141-146
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• 2003

In this paper, we propose an adaptive search range decision algorithm for motion vector estimation in video coding. The performance of general motion estimation method in video coding mechanism is evaluated with respect to the motion vector accuracy and the complexity, which is trade-off. The proposed algorithm that plays as a role of pre-processing for motion vector estimation determines the motion search range by the local statistics of motion vector of neighboring blocks, resulting in more than 60(%) reduction of the computational cost without the loss of visual quality. Experimental results show the capability of the proposed algorithm.

• Science of Emotion and Sensibility
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• v.17 no.2
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• pp.55-62
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• 2014

In this study, a questionnaire was developed to assess error search and correction tasks, and an analysis was performed on the accuracy of the tasks and the time required for their completion in order to identify the effects of LED light source illumination on visual performance according to changes in a predicted mean vote(PMV) thermal environment. In addition, a subjective evaluation was performed by conducting a survey on the level of visual fatigue experienced during the tasks. In the experiment, four types of PMV thermal environments were established according to PMV values in the temperature range of $(17{\pm}1-29{\pm}1)^{\circ}C$ and the humidity range of $(50{\pm}5-60{\pm}5)%$, and the LED light source illumination was divided into three types: 400lx, 700lx, and 1000lx. The experimental results confirmed that the accuracy of the error search(LED p value=0.058, PMV*LED p value=0.083) and correction tasks and the time required(LED p value=0.004, PMV p value=0.000) for their completion were affected by changes in both the PMV thermal environment and the LED light source illumination, whereas a significant difference in visual fatigue was observed only in the PMV thermal environment(p value=0.003).

• Journal of Korean Society for Quality Management
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• v.20 no.2
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• pp.109-117
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• 1992

An inspection task largely can be seen as a job divided up into a series of visual search and classification subtasks. In these subtasks, an Inspector must performs to compare the standard references proposed in visual environments and recalled in his memory with the visual stimuli to be inspected. It means that the judgement of similarity should be demanded on inspection tasks. Therefore, the inspector's ability for the judgement of similarity and the difference similarity between inspection materials are important factors to effect on performances in inspection tasks. In this paper, to analysis the effect of these factors on inspection time, an inspection task is designed and suggested by means of computer simulator. Especially, the skin conductance responses(SCR) of subjects are measured to evaluate the complexity of tasks due to the difference of similarity between materials. In the results of experiment, the more similar or different the difference of similarity between materials is, the shorter the inspection time is because of the reduction of task complexity. And, When the inspector's cognition for similarity between materials is consistanct, the inpsection time is improved. Concludingly, the consistency of reponses for similarity judgement becomes a measurement to present the performance levels. And the information of inspection time that due to the difference of similarity between materials must be considered in planning and scheduling inspection tasks.

• Korean Journal of Cognitive Science
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• v.32 no.3
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• pp.117-139
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• 2021

The way of change detection in which presence of a different item is determined between memory and test arrays with a brief in-between time interval resembles how visual search is done considering that the different item is searched upon the onset of a test array being compared against the items in memory. According to the resemblance, the present study examined whether varying the probability of change occurrence in a visual short-term memory-based change detection task can influence the aspect of response-decision making (i.e., change prevalence effect). The simple-feature change detection task in the study consisted of a set of four colored boxes followed by another set of four colored boxes between which the participants determined presence or absence of a color change from one box to the other. The change prevalence was varied to 20, 50, or 80% in terms of change occurrences in total trials, and their change detection errors, detection sensitivity, and their subsequent RTs were analyzed. The analyses revealed that as the change prevalence increased, false alarms became more frequent while misses became less frequent, along with delayed correct-rejection responses. The observed change prevalence effect looks very similar to the target prevalence effect varying according to probability of target occurrence in visual search tasks, indicating that the background principles deriving these two effects may resemble each other.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.3976-3990
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• 2022

Currently, cross-attention is widely used in Siamese trackers to replace traditional correlation operations for feature fusion between template and search region. The former can establish a similar relationship between the target and the search region better than the latter for robust visual object tracking. But existing trackers using cross-attention only focus on rich semantic information of high-level features, while ignoring the appearance information contained in low-level features, which makes trackers vulnerable to interference from similar objects. In this paper, we propose a Multi-level Cross-attention Siamese network(MCSiam) to aggregate the semantic information and appearance information at the same time. Specifically, a multi-level cross-attention module is designed to fuse the multi-layer features extracted from the backbone, which integrate different levels of the template and search region features, so that the rich appearance information and semantic information can be used to carry out the tracking task simultaneously. In addition, before cross-attention, a target-aware module is introduced to enhance the target feature and alleviate interference, which makes the multi-level cross-attention module more efficient to fuse the information of the target and the search region. We test the MCSiam on four tracking benchmarks and the result show that the proposed tracker achieves comparable performance to the state-of-the-art trackers.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.12
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• pp.485-496
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• 2018

So far, the deep learning, a field of artificial intelligence, has achieved remarkable results in solving problems from unstructured data. However, it is difficult to comprehensively judge situations like humans, and did not reach the level of intelligence that deduced their relations and predicted the next situation. Recently, deep neural networks show that artificial intelligence can possess powerful relational reasoning that is core intellectual ability of human being. In this paper, to analyze and observe the performance of Relation Networks (RN) among the neural networks for relational reasoning, two types of RN-based deep neural network models were constructed and compared with the baseline model. One is a visual question answering RN model using Sort-of-CLEVR and the other is a text-based question answering RN model using bAbI task. In order to maximize the performance of the RN-based model, various performance improvement experiments such as hyper parameters tuning have been proposed and performed. The effectiveness of the proposed performance improvement methods has been verified by applying to the visual QA RN model and the text-based QA RN model, and the new domain model using the dialogue-based LL dataset. As a result of the various experiments, it is found that the initial learning rate is a key factor in determining the performance of the model in both types of RN models. We have observed that the optimal initial learning rate setting found by the proposed random search method can improve the performance of the model up to 99.8%.

• The Journal of Korean Academy of Sensory Integration
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• v.8 no.1
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• pp.61-72
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• 2010

Objective : This study tried to identify evaluation tools and intervention approaches that have been used regarding visual motor skills of children, in order to suggest the best evaluation method and intervention strategy. Methods : This study employed a systemic review. Papers researched for the review were selected from the PubMed which is a web engine to search academic articles. For search, time period of publication was limited from January 2001 to Jun 2010, and key words used were "visual motor and occupational therapy", "visuomotor and occupational therapy", and "perception and motor and occupational therapy". 13 papers among total 161 findings were selected for data analysis. Results : Through literature review, followings were founded. For population targeted in studies, children with developmental disorder are majority(20.00%) and those with handwriting problems are another major group(13.33%). DTVP(33.33%) and VMI(26.67%) are most common tools used to evaluate visual motor skills. Most frequently used intervention method is developmental skill based program(58.82) and the second common method is sensory integration therapy and sensory-based intervention(23.53%). Regarding the effectiveness of occupational therapy for visual motor skill, positive evidences with statistical power take 72.73%. Conclusion : The results imply that occupational therapy is effective for visual motor skill in children. It is suggested that further studies are needed to encompass effectiveness of occupational therapy in terms of children's occupational performance.

• The KIPS Transactions:PartB
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• v.11B no.3
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• pp.327-336
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• 2004

For visual inspection system in real industrial environment, it is one of most important tasks to design fast and stable pattern matching algorithm. This paper presents an edge-based point correlation algorithm avoiding full search in visual inspection system. Conventional algorithms based on NGC(normalized gray-level correlation) have to overcome some difficulties for applying to automated inspection system in factory environment. First of all, NGC algorithms need high time complexity and thus high performance hardware to satisfy real-time process. In addition, lighting condition in realistic factory environments if not stable and therefore intensity variation from uncontrolled lights gives many roubles for applying directly NGC as pattern matching algorithm in this paper, we propose an algorithm to solve these problems from using thinned and binarized edge data and skipping full point search with edge-map analysis. A point correlation algorithm with the thinned edges is introduced with image pyramid technique to reduce the time complexity. Matching edges instead of using original gray-level pixel data overcomes NGC problems and pyramid of edges also provides fast and stable processing. All proposed methods are preyed from experiments using real images.