• The Korean Journal of Franchise Management
• /
• v.14 no.4
• /
• pp.51-65
• /
• 2023

Purpose: As social interest in environmental issues increases, pro-environmental initiatives are becoming more active in many industry sectors. This study explores how a firm's perceived green brand image affects consumer loyalty through brand trust and attachment. Research design, data, and methodology: The data of 363 respondents aged 20 to 59 who purchased the franchise lunchbox in the last three months were analyzed using SPSS 25.0 and SmartPLS 4.0. Result: Green brand image affects consumer loyalty through cognitive trust, affective trust, and brand attachment. Regarding serial mediations, cognitive trust affects brand attachment only through affective trust and, in turn, consumer loyalty. Conclusions: This study employs the hierarchy of effects theory to explore the role of the perceived green image of the franchise lunchbox brand in prompting consumer loyalty through brand trust and attachment. The eco-friendly initiatives are imperative in establishing a green brand image, given their critical roles in generating consumer brand trust and attachment as well as consumer loyalty in the franchise lunchbox industry. The franchise lunchbox firms should implement environmental initiatives and effectively communicate and actively inform these initiatives to raise perceptions of green brand image and build cognitive brand trust.

• Proceedings of the KSRS Conference
• /
• 1999.11a
• /
• pp.411-420
• /
• 1999

In geology, lineament features have been used to identify geological events, and many of scientists have been developed the algorithm that can be applied with the computer to recognize the lineaments. We choose several edge detection filter, line detection filters and Hough transform to detect an edge, line, and to vectorize the extracted lineament features, respectively. firstly the edge detection filter using a first-order derivative is applied to the original image In this step, rough lineament image is created Secondly, line detection filter is used to refine the previous image for further processing, where the wrong detected lines are, to some extents, excluded by using the variance of the pixel values that is composed of each line Thirdly, the thinning process is carried out to control the thickness of the line. At last, we use the Hough transform to convert the raster image to the vector one. A Landsat image is selected to extract lineament features. The result shows the lineament well regardless of directions. However, the degree of extraction of linear feature depends on the values of parameters and patterns of filters, therefore the development of new filter and the reduction of the number of parameter are required for the further study.

• Journal of Environmental Science International
• /
• v.28 no.12
• /
• pp.1101-1110
• /
• 2019

This study investigated Post-Occupancy Evaluation (POE) of cherry blossom trails 'Cherry Road' in Daegu Catholic Univ. campus, at Gyeonsan-city, Korea. The evaluation focused on image preference and satisfaction of users i.e., students, using questionnaire surveys. A total 201 questionnaire samples were analyzed and most of the respondents were in the age group of 20. Frequency analysis was conducted on demographics, use behavior, reliability, and means. Factor analysis and multiple regression analysis were conducted for user satisfaction and image preference. Over 80% of visitors came with companions during daytime. The most common motives for use were strolling and walking, event and meeting, passing. For user satisfaction the mean scores were highest for landscape beauty (4.22), image improvement (4.14), campus image (4.08). Night lighting facility received the lowest score (3.32). Factor analysis concerning user satisfaction was categorized into environment-human behavior and physical factors. Multiple regression analysis showed that the overall satisfaction of user was significantly influenced by five independent variables: 'harmonious' (β=.214), 'night lighting facility' (β=.173), 'landscape beauty' (β=.208), 'lawn care' (β=.154), and 'walking trails' (β=.123). The mean scores of image variables were highest for 'beautiful' (5.81), 'bright' (5.67), and 'open' (5.64). The lowest scores was for 'quiet' (4.47). Exploratory factor analysis led to three factors being categorized: aesthetics, comforts, and simplicity. Result of multiple regression analysis indicated that the preference of space image was significantly influenced by five variables: 'bright' (β=.397), 'refreshing' (β=.211), 'cool' (β=.219), 'clean' (β=.182), and 'natural' (β=.-142). Hence, Cherry Road has a high level of user satisfaction and image evaluation, which is interpreted as having various cultural events and value for students on campus. To improve the satisfaction of Cherry Road in the future, it is necessary to secure night lighting, to manage trash cans, and to secure rest space.

• Proceedings of the KSRS Conference
• /
• 2008.10a
• /
• pp.72-75
• /
• 2008

Image segmentation techniques becoming increasingly important in the field of remote sensing image analysis in areas such as object oriented image classification. This paper presents a new method for image segmentation in High Resolution Remote Sensing Image based on Seeded Region Growing (SRG) and Edge Information. Firstly, multi-spectral edge detection was done using an entropy operator in pan-sharpened QuickBird imagery. Then, the initial seeds were automatically selected from the obtained edge map. After automatic selection of significant seeds, an initial segmentation was achieved by applying SRG. Finally the region merging process, using region adjacency graph (RAG), was carried out to get the final segmentation result. Experimental results demonstrated that the proposed method has good potential for application in the segmentation of high resolution satellite images.

• Proceedings of the KSRS Conference
• /
• 2005.10a
• /
• pp.497-500
• /
• 2005

Research and development of remote sensing technique is necessary so that more accurate and extensive information may be obtained. To achieve this goal, the synthesized technique which integrates the high resolution optic and SAR image, and topographical information was examined to investigate the quantitative/qualitative characteristics of the Earth's surface environment. For this purpose, high-precision DEMs of Jeju-Island was generated and data fusion algorithm was developed in order to integrate the multi-spectral optic and polarimetric SAR image. Three dimensional land-cover and two dimensional soil moisture maps were generated conclusively so as to investigate the Earth's surface environments and extract the geophysical parameters.

• Proceedings of the KSRS Conference
• /
• 2007.10a
• /
• pp.238-242
• /
• 2007

Today's commercial high resolution satellite imagery such as that provided by IKONOS and QuickBird, offers the potential to extract useful spatial information for geographical database construction and GIS applications. Digital maps supply the most generally used GIS data probiding topography, road, and building information. Currently, the building information provided by digital maps is incompletely constructed for GIS applications due to planar position error and warped shape. We focus on extracting of the accurate building information including position, shape, and height to update the building information of the digital maps and GIS database. In this paper, we propose a new method of 3D building information extraction with a single high resolution satellite image and digital map. Co-registration between the QuickBird image and the 1:1,000 digital maps was carried out automatically using the RPC adjustment model and the building layer of the digital map was projected onto the image. The building roof boundaries were detected using the building layer from the digital map based on the satellite azimuth. The building shape could be modified using a snake algorithm. Then we measured the building height and traced the building bottom automatically using triangular vector structure (TVS) hypothesis. In order to evaluate the proposed method, we estimated accuracy of the extracted building information using LiDAR DSM.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.38 no.5
• /
• pp.477-486
• /
• 2020

Drone has been widely used for many applications ranging from amateur and leisure to professionals to get fast and accurate 3-D information of the surface of the interest. Most of commercial softwares developed for this purpose are performing automatic matching based on SIFT (Scale Invariant Feature Transform) or SURF (Speeded-Up Robust Features) using nadir-looking stereo image sets. Since, there are some situations where not only nadir and nadir-looking matching, but also nadir and oblique-looking matching is needed, the existing software for the latter case could not get good results. In this study, a matching experiment was performed to utilize images with differences in geometry. Nadir and oblique-looking images were acquired through drone for a total of 2 times. SIFT, SURF, which are feature point-based, and IMAS (Image Matching by Affine Simulation) matching techniques based on affine transformation were applied. The experiment was classified according to the identity of the geometry, and the presence or absence of a building was considered. Images with the same geometry could be matched through three matching techniques. However, for image sets with different geometry, only the IMAS method was successful with and without building areas. It was found that when performing matching for use of images with different geometry, the affine transformation-based matching technique should be applied.

• Computers and Concrete
• /
• v.33 no.5
• /
• pp.481-496
• /
• 2024

Internal erosion around pipes can lead to the failure of earth dams through various mechanisms. This study investigates the displacement patterns in earth dam models under three different failure modes due to internal erosion, using digital image correlation (DIC) methods. Three failure modes—erosion along a pipe (FM1), pipe leakage leading to soil erosion (FM2), and erosion in a pipe due to defects (FM3)—are analyzed using two- and three-dimensional image- processing techniques. The internal displacement of the cross-sectional area and the surface displacement of the downstream slope in the dam models are monitored using an image acquisition system. Physical model tests reveal that FM1 exhibits significant displacement on the upper surface of the downstream slope, FM2 shows focused displacement around the pipe defect, and FM3 demonstrates increased displacement on the upstream slope. The variations in internal and surface displacements with time depend on the segmented area and failure mode. Analyzing the relationships between internal and surface displacements using Pearson correlation coefficients reveals various displacement patterns for the segmented areas and failure modes. Therefore, the image-based characterization methods presented in this study may be useful for analyzing the displacement distribution and behavior of earth dams around pipes, and further, for understanding and predicting their failure mechanisms.

• International Journal of Contents
• /
• v.14 no.3
• /
• pp.17-26
• /
• 2018

To ensure safety of bridges, it is critical to inspect and assess physical and functional conditions regularly. Presently, most highway bridges in the U.S. are inspected visually. However, this method of inspection is often influenced by the bridge inspector's knowledge and experience. So, reliability and accuracy of inspection results may be problematic. To solve such problems, an extensive number of robotics systems and image processing techniques for bridge inspection methods have been proposed. These robotics systems and image processing techniques are used to measure various bridge conditions, such as apparent damage, displacement and dynamic characteristics. This paper provides a comprehensive review of robotics systems and image processing technologies used in bridge inspection.

• Korean Journal of Remote Sensing
• /
• v.25 no.3
• /
• pp.233-242
• /
• 2009

This paper presents two different types of supervised classifiers such as support vector machine (SVM) and spectral angle mapper (SAM). The Compact Airborne Spectrographic Imager (CASI) high resolution aerial image was classified with the above two classifier. The image was classified into eight land use /land cover classes. Accuracy assessment and Kappa statistics were estimated for SVM and SAM separately. The overall classification accuracy and Kappa statistics value of the SAM were 69.0% and 0.62 respectively, which were higher than those of SVM (62.5%, 0.54).