• ETRI Journal
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• v.45 no.5
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• pp.795-810
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• 2023

Unmanned aerial vehicles (UAV) and ground vehicles (UGV) require advanced video analytics for various tasks, such as moving object detection and segmentation; this has led to increasing demands for these methods. We propose a zero-shot video object segmentation method specifically designed for UAV and UGV applications that focuses on the discovery of moving objects in challenging scenarios. This method employs a background memory model that enables training from sparse annotations along the time axis, utilizing temporal modeling of the background to detect moving objects effectively. The proposed method addresses the limitations of the existing state-of-the-art methods for detecting salient objects within images, regardless of their movements. In particular, our method achieved mean J and F values of 82.7 and 81.2 on the DAVIS'16, respectively. We also conducted extensive ablation studies that highlighted the contributions of various input compositions and combinations of datasets used for training. In future developments, we will integrate the proposed method with additional systems, such as tracking and obstacle avoidance functionalities.

• International Journal of Aeronautical and Space Sciences
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• v.10 no.1
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• pp.83-90
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• 2009

A new IRF (Impulse Response Function) analysis technique in high resolution SAR image is presented by taking into account the real clutter environment. In order to investigate the realistic effect of clutter background on the impulse response function of SAR image, an ideally generated impulse response function is superimposed with a large number of background clutter data which are extracted from the various regions of an actual SAR image. As a performance measure, PSLR (Peak Sidelobe Ratio) of the clutter-contained IRF is presented in the various groups of clutter background, and finally the results are compared with the stochastic model.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2023.05a
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• pp.127-128
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• 2023

The construction industry is increasingly adopting vision AI technologies to improve efficiency and safety management. However, the complex and dynamic nature of construction sites can pose challenges to the accuracy of vision AI models trained on datasets that do not consider the background. This study investigates the effect of background on object recognition for vision AI in construction sites by constructing a learning dataset and a test dataset with varying backgrounds. Frame scaffolding was chosen as the object of recognition due to its wide use, potential safety hazards, and difficulty in recognition. The experimental results showed that considering the background during model training significantly improved the accuracy of object recognition.

• Journal of the Institute of Electronics and Information Engineers
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• v.54 no.8
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• pp.67-80
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• 2017

This paper presents a hole-filling method using extrapolated spatio-temporal background information to obtain a synthesized view. A new temporal background model using non-overlapped patch based background codebook is introduced to extrapolate temporal background information In addition, a depth-map driven spatial local background estimation is addressed to define spatial background constraints that represent the lower and upper bounds of a background candidate. Background holes are filled by comparing the similarities between the temporal background information and the spatial background constraints. Additionally, a depth map-based ghost removal filter is described to solve the problem of the non-fit between a color image and the corresponding depth map of a virtual view after 3-D warping. Finally, an inpainting is applied to fill in the remaining holes with the priority function that includes a new depth term. The experimental results demonstrated that the proposed method led to results that promised subjective and objective improvement over the state-of-the-art methods.

• English Language & Literature Teaching
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• v.9 no.2
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• pp.213-241
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• 2003

As an attempt to characterize the English test section of CSAT (College Scholastic Ability Test) and to get some suggestions, this study raised the research questions, as 'What are the main factors that affect students' English test scores in CSAT, and how big influences do they have?' It has been hypothesized that among main factors are the L1 competence, represented by the Korean test scores in CSAT, background knowledge or intelligence, represented by the "total" scores in CSAT, and the two types of L2 knowledge (vocabulary and grammar on one hand and prosody m the other hand), measured by the test devised specially for this study. The individual effect of the L2 vocabulary and grammar (one kind of L2 knowledge) was 70%, that of background knowledge or intelligence 61%, that of the L1 competence 50%, and that of the L2 prosody knowledge (the other kind of L2 knowledge) 32%. According to the stepwise regression, the whole effect of these four factors was 74%. The findings suggest that first, although CSAT is based on the top-down model of comprehension, the bottom-up model of learning should be more emphasized in our English class. Also, since background knowledge or intelligence is the second most influential factor, the top-down model of learning that helps students learn to understand by activating their various schemata must also be very effective.

• Geosystem Engineering
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• v.21 no.5
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• pp.262-272
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• 2018

The U-statistic method is one of the most important structural methods to separate the anomaly from background. It considers the location of samples and carries out the statistical analysis of the data without judging from a geochemical point of view and tries to separate subpopulations and determine anomalous areas. In the present study, 3D U-statistic method has been applied for the first time through the three-dimensional (3D) modeling of an ore deposit. In order to achieve this purpose, 3D U-statistic is applied on the data (Fe grade) resulted from the drilling network in Baghak mine, central part of the Sangan iron mines (in Khorassan Razavi Province, Iran). Afterward, results from applying 3D U-statistic method are used for 3D modeling of the iron mineralization. Results show that the anomalous values are well separated from background so that the determined samples as anomalous are not dispersed and according to their positioning, denser areas of anomalous samples could be considered as anomaly areas. And also, final results (3D model of iron mineralization) show that output model using this method is compatible with designed model for mining operation. Moreover, seen that U-statistic method in addition for separating anomaly from background, could be very efficient for the 3D modeling of different ore type.

• The KIPS Transactions:PartB
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• v.17B no.6
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• pp.405-412
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• 2010

Previous background generation techniques showed bad performance in complex environments since they used only temporal contexts. To overcome this problem, in this paper, we propose a new background generation method which incorporates spatial as well as temporal contexts of the image. This enabled us to obtain 'clean' background image with no moving objects. In our proposed method, first we divided the sampled frame into m*n blocks in the video sequence and classified each block as either static or non-static. For blocks which are classified as non-static, we used MRF framework to model them in temporal and spatial contexts. MRF framework provides a convenient and consistent way of modeling context-dependent entities such as image pixels and correlated features. Experimental results show that our proposed method is more efficient than the traditional one.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.8
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• pp.1537-1545
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• 2016

In real time video sequence, object segmentation and tracking method are actively applied in various application tasks, such as surveillance system, mobile robots, augmented reality. This paper propose a robust object tracking method. The background models are constructed by learning the initial part of each video sequences. After that, the moving objects are detected via object segmentation by using background subtraction method. The region of detected objects are continuously tracked by using the HSV color histogram with particle filter. The proposed segmentation method is superior to average background model in term of moving object detection. In addition, the proposed tracking method provide a continuous tracking result even in the case that multiple objects are existed with similar color, and severe occlusion are occurred with multiple objects. The experiment results provided with 85.9 % of average object overlapping rate and 96.3% of average object tracking rate using two video sequences.

• Journal of the Korean Society of Laryngology, Phoniatrics and Logopedics
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• v.15 no.2
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• pp.112-117
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• 2004

Background and Objectives : This study is purposed to investigate the statistically significant discrimination model for predicting vocal fold nodule and polyp's lesional grade, with patients' background data and objective voice evaluation parameters. Materials and Method : The retrospective research was carried out at the Ewha Womans University Hospital. 122 patients' voice examination data had been selected, and lesion screening (Grade I, II, and III) was conducted by 2 ENT specialists, with each patient's vocal fold pictures achieved during the laryngoscopy examination. Results : The Lesional Grade Discrimination Model with which the lesional grade of vocal fold nodules and polyps could be predicted was derived by the ordinal logistic regression analysis (using SPSS 10.0). With this model the lesional grades of 73 out of 122 patients(59.8%) were correctly predicted to their formerly screened ones. Conclusion : This model applied the multivariate approach, which statistically combined these currently used parameters, Jitter, Shimmer, MFR, MPT, and patient's background data such as gender and dysphonia period. It might explain the status of benign lesion of vocal folds, and furthermore expect the physiological function of vocal folds.

• Journal of Fashion Business
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• v.18 no.2
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• pp.29-41
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• 2014

As the online shopping mall market faces severe competition, marketers have focused on visual factors that generate positive consumer responses in order to induce more consumers. Although product photos are crucial in the communication of product information as well as in the development of positive images of online shopping mall, there was little intention the effect of diverse types of product photo. In this study, three types of backgrounds (i.e., no background, indoor background, street background) are compared in order to explore whether consumer sensibility factors are different according to photo backgrounds. Moreover, it investigates the effect of sensibility toward photo backgrounds on consumer attitude. A total of 222 consumers participated in the experiments. As a result, six sensibility factors were generated from online model photos, including structure, upscale, uniqueness, interest, simpleness, and easiness factors. Among these factors, simpleness and structure showed the highest means; simpleness was the factor indicating the differences according to photo backgrounds. Sensibility factors affecting attitude toward online shopping malls were uniqueness in the case of no background, interest in the case of indoor background, and upscale in the case of street background photo.