• Journal of Advanced Navigation Technology
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• v.22 no.6
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• pp.491-499
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• 2018

Runway visual range (RVR), one of the important indicators of aircraft takeoff and landing, is affected by meteorological conditions such as temperature, humidity, etc. It is important to estimate the RVR at the time of arrival in advance. This study estimated the RVR of the local airport after 1 hour by upgrading the RVR estimation model using the proposed deep learning network. To this end, the advancement of the estimation model was carried out by changing the time interval of the meteorological data (temperature, humidity, wind speed, RVR) as input value and the linear conversion of the results. The proposed method generates estimation model based on the past measured meteorological data and estimates the RVR after 1 hour and confirms its validity by comparing with measured RVR after 1 hour. The proposed estimation model could be used for the RVR after 1 hour as reference in small airports in regions which do not forecast the RVR.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.12
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• pp.3383-3397
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• 2023

Scene graphs serve as semantic abstractions of images and play a crucial role in enhancing visual comprehension and reasoning. However, the performance of Scene Graph Generation is often compromised when working with biased data in real-world situations. While many existing systems focus on a single stage of learning for both feature extraction and classification, some employ Class-Balancing strategies, such as Re-weighting, Data Resampling, and Transfer Learning from head to tail. In this paper, we propose a novel approach that decouples the feature extraction and classification phases of the scene graph generation process. For feature extraction, we leverage a transformer-based architecture and design an adaptive calibration function specifically for predicate classification. This function enables us to dynamically adjust the classification scores for each predicate category. Additionally, we introduce a Distribution Alignment technique that effectively balances the class distribution after the feature extraction phase reaches a stable state, thereby facilitating the retraining of the classification head. Importantly, our Distribution Alignment strategy is model-independent and does not require additional supervision, making it applicable to a wide range of SGG models. Using the scene graph diagnostic toolkit on Visual Genome and several popular models, we achieved significant improvements over the previous state-of-the-art methods with our model. Compared to the TDE model, our model improved mR@100 by 70.5% for PredCls, by 84.0% for SGCls, and by 97.6% for SGDet tasks.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.7
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• pp.157-168
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• 2014

We propose an novel artificial visual attention model that is capable of automatic detection and segmentation of saliency region on natural images in this paper. The proposed model is based on human visual perceptions in biological vision and contains there are main contributions. Firstly, we propose a novel framework of artificial visual attention model based on the opponent process theory using intensity and color features, and an entropy filter is designed to perceive salient regions considering the amount of information from intensity and color feature channels. The entropy filter is able to detect and segment salient regions in high segmentation accuracy and precision. Lastly, we also propose an adaptive combination method to generate a final saliency map. This method estimates scores about intensity and color conspicuous maps from each perception model and combines the conspicuous maps with weight derived from scores. In evaluation of saliency map by ROC analysis, the AUC of proposed model as 0.9256 approximately improved 15% whereas the AUC of previous state-of-the-art models as 0.7824. And in evaluation of salient region segmentation, the F-beta of proposed model as 0.7325 approximately improved 22% whereas the F-beta of previous state-of-the-art models.

• Proceedings of the Korea Water Resources Association Conference
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• 1998.05a
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• pp.239-244
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• 1998

• Journal of Korean Society of Rural Planning
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• v.7 no.1 s.13
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• pp.37-48
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• 2001

The purpose of this study is to suggest the landscape potential index for visualizing landscape information in the conservation of hilly landscape in urban fringe. For the visual and quantitative approach to topological landscape assessment, numerical entity data of DEM(digital elevation model) were processed with CAD-based utilities that we developed and were mainly focused on analysis of visibility and visual sensitivity. Some results, with reference in assessing greenbelt area of Eodeung Mt. in Gwangju, proved to be considerable in the landscape assessment of suburban hilly landscapes. 1) Since the viewpoints and viewpoint fields were critical to landscape structure, randomized 194 points(spatially 500m interval) were applied to assessing the generalized visual sensitivity, we called. Because there were similar patterns of distribution comparing to those by 56 points and 18 Points given appropriately, it could be more efficient by a few viewpoints which located widely. 2) Regressional function was derived to represent the relationships between probabilities of visibility frequency and the topological factors(topological dominance, landform complexity and relational aspect) of target field. 3) Visibility scores of each viewpoint were be calculated by summing the visual sensitivity indices within a scene. The scores to the upper part including ridge line have been more representative to overall distributions of visual sensitivities. Also, with sum of deviations of sensitivity indices from each single point's specific index to the weighting values of view points could be estimated rotationally. 4) The deviational distributions of visual sensitivity classes in the topological unit of target field were proved to represent the visual vulnerability of the landform. 5) Landscape potential indices combined with the visual sensitivity and the DGN(degree of green naturality) were proposed as visualized landscape information distributed by topological unit.

• 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 Institute of Landscape Architecture
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• v.26 no.3
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• pp.78-78
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• 1998

The purpose of this research is to develop a CAD-based program for data analysis of digital elevation model(DEM) on the aspect of landscape assessment. When handling DEM data as a visual simulation of topographic landscape, it is basic interest to analyze visible area and visualize visual sensitivity distributions. In reference with landscape assessment, more intuitive and interactive visualizing tools are needed, specially in area of visual approach. For adaptability to landscape assessment, algorithmic approaches to visibility analysis and concepts for visual sensitivity calculation in this study were based on processing techniques of entity data control functions used in AutoCAD drawing database. Also, for the purpose of quantitative analysis, grid-type 3DFACE entities were adopted as mesh unit of DEM structure. Developed programs are composed of main part named VSI written in AutoLISP and two of interface modules written in dialog control language(DCL0 for user-oriented interactive usage. Definitions of camera points(view points) and target points(or observed area) are available alternatively in combined methods of representing scenic landscape, scenery, and sequential landscape. In the case of scene landscape(single camera to fixed target point), only visibility analysis in available. And total visibility, frequency of cumulative visibility, and visual sensitivity analysis are available in other cases. Visual sensitivity was thought as view angle(3 dimensional observed visual area) and the strengths were classified in user defined level referring to statistical characteristics of distribution. Visibility analysis routine of the VSI was proved to be more effective in the accuracy and time comparing with similar modules of existing AutoCAD third utility.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.361-369
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• 2023

Achieving realistic visual quality while maintaining optimal real-time rendering performance is a major challenge in evolving computer graphics and interactive 3D applications. Normal mapping, as a core technology in 3D, has matured through continuous optimization and iteration. Hybrid normal mapping as a new mapping model has also made significant progress and has been applied in the 3D asset production pipeline. This study comprehensively explores the hybrid normal techniques, analyzing Linear Blending, Overlay Blending, Whiteout Blending, UDN Blending, and Reoriented Normal Mapping, and focuses on how the various hybrid normal techniques can be used to achieve rendering performance and visual fidelity. performance and visual fidelity. Under the consideration of computational efficiency, visual coherence, and adaptability in different 3D production scenes, we design comparative experiments to explore the optimal solutions of the hybrid normal techniques by analyzing and researching the code, the performance of different hybrid normal mapping in the engine, and analyzing and comparing the data. The purpose of the research and summary of the hybrid normal technology is to find out the most suitable choice for the mainstream workflow based on the objective reality. Provide an understanding of the hybrid normal mapping technique, so that practitioners can choose how to apply different hybrid normal techniques to the corresponding projects. The purpose of our research and summary of mixed normal technology is to find the most suitable choice for mainstream workflows based on objective reality. We summarized the hybrid normal mapping technology and experimentally obtained the advantages and disadvantages of different technologies, so that practitioners can choose to apply different hybrid normal mapping technologies to corresponding projects in a reasonable manner.

• Journal of Broadcast Engineering
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• v.20 no.6
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• pp.818-826
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• 2015

Recently, objective image quality assessment (IQA) methods that elaborately reflect the visual quality perception characteristics of human visual system (HVS) have actively been studied. Among those characteristics of HVS, luminance adaptation (LA) effect, indicating that HVS has different sensitivities depending on background luminance values to distortions, has widely been reflected into many existing IQA methods via Weber's law model. In this paper, we firstly reveal that the LA effect based on Weber's law model has inaccurately been reflected into the conventional IQA methods. To solve this problem, we firstly derive a new LA effect-based Local weight Function (LALF) that can elaborately reflect LA effect into IQA methods. We validate the effectiveness of our proposed LALF by applying LALF into SSIM (Structural SIMilarity) and PSNR methods. Experimental results show that the SSIM based on LALF yields remarkable performance improvement of 5% points compared to the original SSIM in terms of Spear rank order correlation coefficient between estimated visual quality values and measured subjective visual quality scores. Moreover, the PSNR (Peak to Signal Noise Ratio) based on LALF yields performance improvement of 2.5% points compared to the original PSNR.

• Proceedings of the KOSOMBE Conference
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• v.1991 no.05
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• pp.18-21
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• 1991

In this paper, the effect of background grey levels on the visual perception of target image displayed on CRT monitor has been investigated. The purpose of this study is to investigate the efficacy of CRT monitor as a display medium of image information especially in medical imaging field. Three sets of experiments have been performed in this study; the first was to measure the luminance response of CRT monitor and to find the best fitting equation, and the second was the psychophysical experiment measuring the threshold grey level difference between the target image and the background required for visual discrimination for various background grey levels, and the third was to develop a visual model that is predictable of the threshold grey level difference measured in the psychophysical experiment. The result of psycophysical experiment shows that the visual perception performance is significantly degraded in the range of grey levels lower than 50, which is turned out due to the low luminance change of CRT monitor in this range while human eye has been adapted to relatively bright ambient illumination.