Enriching Natural Monument with User-Generated Mobile Augmented
                    Reality Mashup

Shin, Choonsung; Hong, Sunghee; Yoon, Hyoseok

doi:10.33851/jmis.2020.7.1.25

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…The crit and the research options we intend to rank are placed of the VIKOR decision matrix of criteria has a quantita entered, but if it has a qualitative aspect, this process i to 5 or 1 to 9. The Decision Matrix Normalization: The normalization process is conducted by using Equation (5). In order to normalize each value, we divide by the square root of the sum of the values of each standard column.…”

Section: The Vikor Methodsmentioning

confidence: 99%

“…The number of studies that evaluated the AR potential is increasing in various fields, which include tourism [4][5][6][16][17][18][19][20], healthcare [21], and industry [22], so more attention has been given recently to developing personalized AR systems. The research related to the personalized AR tourist system is addressed in three parts in this section, which are the AR tourism systems, personalization based on demographic information, and big data in AR systems.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Although they evaluated the personalized AR system in the learning domain, they did not apply user demographics in the personalization process. Choonsung Shin et al [5] introduced a mobile augmented reality for cultural heritage places. They explained how to add the user-generated multimedia content to the AR.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Sci. 2021, 11, 6047 2 of 25 field [4,5], data modeling [2], and big data [6,7] integration, and personalizing the information visualization according to the user preferences [8,9]. With the personalization process of developed AR systems, the user preferences were extracted by either the user inputs or by mining the user history.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

et al. 2021

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A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

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Section: The Vikor Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

et al. 2021

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An Approach Based on Linked Open Data and Augmented Reality for Cultural Heritage Content-Based Information Retrieval

Rinaldi

Russo

Tommasino

2022

Lecture Notes in Computer Science

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SCIFI: 3D face reconstruction via smartphone screen lighting

2021

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3D face reconstruction based on smartphones has a wide range of applications, such as face recognition, liveness detection, face animation, etc. To realize this function, these smartphones have been deployed with specialized hardware modules (infrared dot projector or depth sensor). However, it will inevitably increase the production cost of smartphones and the operation difficulty of users. In this article, we propose a smartphone screen illumination-based face reconstructIon (SCIFI) framework, which only relies on the front camera and screen lighting. Specifically, we have investigated the calibrated planar lighting to achieve the fine-grained textures in Lambertian-based reconstruction. Further, we introduce the face landmark to align multiple photographs, which aims to adjust the position mismatch caused by hand jitter. Moreover, we propose two different methods to eliminate outlier normals based on the characteristics of human face. Extensive experiments based on different environments (dark and bright), different lighting pattern (4-zones and 9-zones), and different testing subjects have validated the effectiveness and robustness of SCIFI in reconstructing the 3D face surface with a favorable surface shape as well as micro facial texture.

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Enriching Natural Monument with User-Generated Mobile Augmented Reality Mashup

Cited by 4 publications

References 20 publications

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

Personalized Augmented Reality Based Tourism System: Big Data and User Demographic Contexts

An Approach Based on Linked Open Data and Augmented Reality for Cultural Heritage Content-Based Information Retrieval

SCIFI: 3D face reconstruction via smartphone screen lighting

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