• The Research Journal of the Costume Culture
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• v.21 no.3
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• pp.401-412
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• 2013

The purpose of this study was to investigate the effects of female applicant's facial attractiveness and feminine-masculine clothing image on job performance evaluation and hiring decision. The research design of study consisted of 3(facial attractiveness high, middle, low)${\times}$2(feminine and masculine clothing image) factorial design. The subject consisted of 243 persons whose occupation were mid-sized companies' administrator in Gwangju and Seoul City. The data were analyzed by factor analysis, Duncan test, ANOVA, t-test. The results of this study were as follows. First, three factors emerged to account for the job performance evaluation. These factors were given the titles of task performance, cooperation and self-management factors. Second, applicant's facial attractiveness exerted significant positive effect on self-management and significant negative effect on cooperation. Third, applicant's facial attractiveness exerted significant effect on hiring decision. Finally, the interaction effect of female applicant's facial attractiveness and feminine-masculine clothing image on job performance evaluation and hiring decision were not significant.

• International Journal of Advanced Culture Technology
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• v.5 no.1
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• pp.76-82
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• 2017

An image hash, a discriminative and robust summary of an image, should be robust against quality-preserving signal processing steps, while being pairwise independent for perceptually different inputs. In order to improve the hash matching performance, this paper proposes a probabilistic dissimilarity matching. Instead of extracting the binary hash from the query image, we compute the probability that the intermediate hash vector of the query image belongs to each quantization bin, which is referred to as soft quantization binning. The probability is used as a weight in comparing the binary hash of the query with that stored in a database. A performance evaluation over sets of image distortions shows that the proposed probabilistic matching method effectively improves the hash matching performance as compared with the conventional Hamming distance.

• Journal of Distribution Science
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• v.17 no.11
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• pp.35-45
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• 2019

Purpose: The purpose of this study is to suggest strategic implications about empirical analysis with mediation effects of corporate image in terms of that relations between logistics culture safety, safety compliance and logistics performance of logistics companies. Research design, data and methodology: The structure and method of this research is organized with, first establishing hypothesis and research model based on previous study related to safety culture, safety compliance, corporate image, logistics safety and logistics performance, which has been carried out survey questionnaire to those who got involved in logistics businesses. Results: It is well justified that safety culture and safety compliance have significantly influenced to logistics performance as well as corporate images that is also revealed to have positive impact to logistics performance. With results verifying into mediation effects of corporate image, it is found that corporate image has partial mediation effects between logistics safety culture and logistics performance, and corporate image has full mediation effects between logistics safety compliance and logistics performance. Conclusions: In conclusion, it is strongly asked to make an aggressive efforts to safety compliance with necessity for spread of safety culture in level of enterprise. Planning the strategy and its implementation is required to secure safety in logistics process because both logistics performance and corporate image has positive influences by logistics safety.

• Smart Structures and Systems
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• v.29 no.1
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• pp.207-220
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• 2022

Structural health monitoring (SHM) plays a vital role in the maintenance and operation of constructions. In recent years, autonomous inspection has received considerable attention because conventional monitoring methods are inefficient and expensive to some extent. To develop autonomous inspection, a potential approach of crack identification is needed to locate defects. Therefore, this study exploits two deep learning-based segmentation models, DeepLabv3+ and Mask R-CNN, for crack segmentation because these two segmentation models can outperform other similar models on public datasets. Additionally, impacts of label quality on model performance are explored to obtain an empirical guideline on the preparation of image datasets. The influence of image cropping and label refining are also investigated, and different strategies are applied to the dataset, resulting in six alternated datasets. By conducting experiments with these datasets, the highest mean Intersection-over-Union (mIoU), 75%, is achieved by Mask R-CNN. The rise in the percentage of annotations by image cropping improves model performance while the label refining has opposite effects on the two models. As the label refining results in fewer error annotations of cracks, this modification enhances the performance of DeepLabv3+. Instead, the performance of Mask R-CNN decreases because fragmented annotations may mistake an instance as multiple instances. To sum up, both DeepLabv3+ and Mask R-CNN are capable of crack identification, and an empirical guideline on the data preparation is presented to strengthen identification successfulness via image cropping and label refining.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.01a
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• pp.76-79
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• 2009

This paper investigates the performance of a vehicular rear-view camera through quantifying the image quality based on several objective criteria from the ISO (International Organization for Standardization). In addition, various experimental environments are defined considering the conditions under which a rear-view camera may need to operate. The process for evaluating the performance of a rear-view camera is composed of five objective criteria: noise test, resolution test, OECF (opto-electronic conversion function) test, color characterization test, and pincushion and barrel distortion tests. The proposed image quality quantification method then expresses the results of each test as a single value, allowing easy evaluation. In experiments, the performance evaluation results are analyzed and compared with those for a regular digital camera.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.5
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• pp.674-677
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• 2020

The cultural background of users utilizing image search engines has a significant impact on the satisfaction of the search results. Therefore, it is important to analyze and understand the cultural context of images for more accurate image search. In this paper, we investigate how the cultural context of images can affect the performance of image classification. To this end, we first collected various types of images (e.g,. food, temple, etc.) with various cultural contexts (e.g., Korea, Japan, etc.) from web search engines. Afterwards, a deep transfer learning approach using VGG19 and MobileNetV2 pre-trained with ImageNet was adopted to learn the cultural features of the collected images. Through various experiments we show the performance of image classification can be differently affected according to the cultural context of images.

• Journal of information and communication convergence engineering
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• v.20 no.3
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• pp.195-203
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• 2022

Noise generated during image acquisition and transmission can negatively impact the results of image processing applications, and noise removal is typically a part of image preprocessing. Denoising techniques combined with nonlocal techniques have received significant attention in recent years, owing to the development of sophisticated hardware and image processing algorithms, much attention has been paid to; however, this approach is relatively poor for edge preservation of fine image details. To address this limitation, the current study combined a steering kernel technique with adaptive masks that can adjust the size according to the noise intensity of an image. The algorithm sets the steering weight based on a similarity comparison, allowing it to respond to edge components more effectively. The proposed algorithm was compared with existing denoising algorithms using quantitative evaluation and enlarged images. The proposed algorithm exhibited good general denoising performance and better performance in edge area processing than existing non-local techniques.

• Journal of Advanced Navigation Technology
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• v.16 no.6
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• pp.910-919
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• 2012

In this paper, we described development of on-board image processing system and its process and verified its performance through flight experiment. The image processing board has single ARM(Advanced Risk Machine) processor. We performed Embedded Linux Porting. Algorithm to be applied for object tracking is color-based image processing algorithm, it can be designed to track the object that has specific color on ground in real-time. To verify performance of the on-board image processing system, we performed flight test using the PNUAV, UAV developed by LAB. Also, we performed optimization of the image processing algorithm and kernel to improve real-time performance. Finally we confirmed that proposed system can track the blue-color object within four pixels error range consistently in the experiment.

• Journal of Korea Multimedia Society
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• v.20 no.1
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• pp.32-42
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• 2017

This paper proposes a new haze removal method for moving image sequence. Since the conventional dark channel prior haze removal method adjusts each color component separately in RGB color space, there can be severe color distortion in the haze removed output image. In order to resolve this problem, this paper proposes a new haze removal scheme that adjusts luminance and saturation components in HLS color space while retaining hue component. Also the conventional dark channel prior haze removal method is developed to obtain best haze removal performance for a single image. Therefore, if it is applied to a moving image sequence, the estimated parameter values change rapidly and the haze removed output image sequence shows unnatural glitter defects. To overcome this problem, a new parameter estimation method using Kalman filter is proposed for moving image sequence. Experimental results demonstrate that the haze removal performance of the proposed method is better than that of the conventional dark channel prior method.

• Journal of IKEEE
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• v.23 no.2
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• pp.395-402
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• 2019

In this paper, we implemented an Ultra-Wideband radar system using Stripmap Synthetic Apertrure Radar algorithm to recognize objects inside a box. Different window functions such as Hanning, Hamming, Kaiser, and Taylor functions to improve image recognition performance are applied and implemented to radar system. The Ultra-Wideband radar system with 3.1~4.8 GHz broadband and UWB antenna were implemented to recognize the conductor plate located inside 1m3 box. To obtain the image, we use the propagation data in the time domain according to the 1m movement distance and use the Range Doppler algorithm. The effect of different window functions to improve the recognition performance of the image are analyzed. From the compared results, we confirmed that the Kaiser window function can obtain a relatively good image.