scholarly journals Intelligent Splicing Method of Virtual Reality Lingnan Cultural Heritage Panorama Based on Automatic Machine Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Yao Fu ◽  
Tingting Guo ◽  
Xingfang Zhao
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Cultural Heritage ◽  
Color Difference ◽  
Training Image ◽  
Automatic Machine ◽  
Panoramic Images ◽  
Multiple Regions ◽  
Application Fields ◽  
The Impact

With the increasing expansion of virtual reality application fields and the complexity of application content, the demand for real-time rendering of realistic graphics has increased sharply. This research mainly discusses the intelligent mosaic method of virtual reality Lingnan cultural heritage panorama based on automatic machine learning. In order to effectively make up for the impact of the insufficiency of the collection process on the quality of the final panoramic image of Lingnan cultural heritage, it is necessary to minimize the irregular rotation of the camera and collect images according to the overlapping area between adjacent images of appropriate size. In order to make Lingnan cultural heritage panoramic images have better visual effects, it is necessary to preprocess the images before image registration and fusion. Image preprocessing mainly includes image denoising and image projection transformation. In this study, cylindrical projection is used to construct the panorama of Lingnan cultural heritage. For each Lingnan cultural heritage training image, we first perform image segmentation to obtain multiple regions and extract the visual features of each region. We use automatic machine learning models to train the visual feature set and use the bagging method to generate different training subsets. In order to generate each component classifier, we determine the overlap area of the two images according to the matched SIFT feature points and determine the best stitching line during the implementation of stitching. In this paper, the number of pixels in the first row of the overlapping area is used to determine the candidate stitching line column, and the best stitching line position should be determined in consideration of the smallest color difference in the stitching area and the most similar texture on both sides. This article uses a Java Applet-based approach to realize virtual roaming of viewing panoramic images of Lingnan cultural heritage in IE browser. The highest accuracy of SIFT is 82.22%, and the lowest recognition time is 0.01 s. This research will promote the development of Lingnan cultural heritage.

scholarly journals GEOMETRIC FEATURES ANALYSIS FOR THE CLASSIFICATION OF CULTURAL HERITAGE POINT CLOUDS

2019 ◽  
Vol XLII-2/W15 ◽  
pp. 541-548 ◽  
Author(s):  
E. Grilli ◽  
E. M. Farella ◽  
A. Torresani ◽  
F. Remondino
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cultural Heritage ◽  
Point Clouds ◽  
Geometric Features ◽  
3D Point Clouds ◽  
3D Data ◽  
3D Documentation ◽  
The Impact

<p><strong>Abstract.</strong> In the last years, the application of artificial intelligence (Machine Learning and Deep Learning methods) for the classification of 3D point clouds has become an important task in modern 3D documentation and modelling applications. The identification of proper geometric and radiometric features becomes fundamental to classify 2D/3D data correctly. While many studies have been conducted in the geospatial field, the cultural heritage sector is still partly unexplored. In this paper we analyse the efficacy of the geometric covariance features as a support for the classification of Cultural Heritage point clouds. To analyse the impact of the different features calculated on spherical neighbourhoods at various radius sizes, we present results obtained on four different heritage case studies using different features configurations.</p>

scholarly journals How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review

10.2196/11643 ◽  
2019 ◽  
Vol 6 (12) ◽  
pp. e11643 ◽  
Author(s):  
Florian Ferreri ◽  
Alexis Bourla ◽  
Charles-Siegfried Peretti ◽  
Tomoyuki Segawa ◽  
Nemat Jaafari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Obsessive Compulsive Disorder ◽  
New Technologies ◽  
Assessment Tools ◽  
Clinical Practices ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Pubmed Database ◽  
The Impact

Background New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. Objective The impact of new technologies on health professionals’ practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. Methods We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. Results We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. Conclusions The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

Cultural heritage monument complex monitoring data analyses using machine learning algorithms

2020 ◽  
Author(s):  
David Kvavadze ◽  
Giorgi Basilaia ◽  
Tea Munchava ◽  
Giorgi Laluashvili ◽  
Mikheil Elashvili
Keyword(s):  
Machine Learning ◽  
Cultural Heritage ◽  
Technological Development ◽  
Complex Structure ◽  
Active Tectonics ◽  
Machine Learning Algorithms ◽  
Critical Conditions ◽  
Runoff Water ◽  
Complex Monitoring ◽  
The Impact

&lt;p&gt;Cultural heritage monuments, that were created by mankind for centuries are scattered throughout the world. Most of them are experiencing impacts coming from nature and humans each year that result in damage and changing their common state. Many of the monuments are facing critical conditions and require diagnostics, study and planning and management of conservation/rehabilitation works. Due to the impact of environmental factors such as temperature, humidity, precipitation, the existence of complex structure of cracks, infiltrated water and runoff water streams, together with active tectonics in the region, Uplistsikhe and Vardzia rock-cut city monuments located in Georgia face problems and permanent destruction.&lt;/p&gt;&lt;p&gt;We have developed continuous monitoring systems that are installed in Vardzia and Uplistsikhe.&lt;/p&gt;&lt;p&gt;These systems are generating large amounts of data and it is almost impossible to analyze this data using conventional methods. In parallel with technological development, it is now possible to analyze big data using machine learning. We decided to use machine learning to address our problem. This approach gave us some interesting results. We were able to detect correlations between different sensors, see anomalies in data that gave us some clues about hazard zones. Additionally&amp;#160; models and predictions about the monument's condition were made.&lt;/p&gt;&lt;p&gt;Our work shows that machine learning could be used to estimate conditions make predictions about monuments state.&lt;/p&gt;&lt;p&gt;This work was supported by Shota Rustaveli National Science Foundation of Georgia (SRNS) [Grant Number fr17_90]&lt;/p&gt;

scholarly journals The impact of visual media on social cognitive concept: The case of cultural object Ru (Korea)

2019 ◽  
Vol 10 (21) ◽  
pp. 80
Author(s):  
Hyuk-Jin Lee
Keyword(s):  
Virtual Reality ◽  
Cultural Heritage ◽  
Physical Object ◽  
Point Of View ◽  
Human Cognition ◽  
Philosophical Background ◽  
The Impact ◽  
Phenomenological Point

<div data-canvas-width="266.11469461802045">There have been very few studies explaining the theoretica l basis on the importance of visible cultural heritage. This study provides the philosophical background of this topic based on a phenomenological point of view and</div><div data-canvas-width="408.0393972989316">explains the significant impact on social members’ cognition. The case of Ru, one of the traditional Korean building types, is introduced as a representative example; how its concept has been defined, changed, and forgotten in Korean culture. The importance of having a correct understanding of how cognition is composed of different types of experiences of cultural heritage is further explained. In this context, the importance of semantic mode and pictorial mode classified by Husserl is argued as the most powerful medium in human cognition based on phenomenological analysis. In this respect, the important role of Virtual Reality (VR) was highlighted. Considering the pace of recent technology and researches, breaking the barrier between experiencing the physical object and the VR may be a matter of time. Phenomenological classification of cultural heritage, which was designed for explaining all the types of cultural heritage, is introduced. The importance of developing a valid VR model and its role in cultural studies is emphasized via the phenomenological classification of cultural heritage. Finally, the balance of the inductive and deductive approach in a cultural study is suggested for more prolific and balanced achievements.</div><div data-canvas-width="408.0393972989316"> </div><div data-canvas-width="408.0393972989316"><p>Highlights:</p><ul><li>This article  provides  the  philosophical  background  of  the  importance  of visible  cultural  heritage  based  on the phenomenological point of view.</li><li>Significant impact on social members’ cognition of the  visible  cultural  heritage  is  discussed  in the  case  of  the traditional Korean building.</li><li>In this respect, the important role of Virtual Reality technology is highlighted.</li></ul><p> </p></div>

scholarly journals How New Technologies Can Improve Prediction, Assessment, and Intervention in Obsessive-Compulsive Disorder (e-OCD): Review (Preprint)

2018 ◽  
Author(s):  
Florian Ferreri ◽  
Alexis Bourla ◽  
Charles-Siegfried Peretti ◽  
Tomoyuki Segawa ◽  
Nemat Jaafari ◽  
...  
Keyword(s):  
Machine Learning ◽  
Virtual Reality ◽  
Obsessive Compulsive Disorder ◽  
New Technologies ◽  
Assessment Tools ◽  
Clinical Practices ◽  
Obsessive Compulsive ◽  
Compulsive Disorder ◽  
Pubmed Database ◽  
The Impact

BACKGROUND New technologies are set to profoundly change the way we understand and manage psychiatric disorders, including obsessive-compulsive disorder (OCD). Developments in imaging and biomarkers, along with medical informatics, may well allow for better assessments and interventions in the future. Recent advances in the concept of digital phenotype, which involves using computerized measurement tools to capture the characteristics of a given psychiatric disorder, is one paradigmatic example. OBJECTIVE The impact of new technologies on health professionals’ practice in OCD care remains to be determined. Recent developments could disrupt not just their clinical practices, but also their beliefs, ethics, and representations, even going so far as to question their professional culture. This study aimed to conduct an extensive review of new technologies in OCD. METHODS We conducted the review by looking for titles in the PubMed database up to December 2017 that contained the following terms: [Obsessive] AND [Smartphone] OR [phone] OR [Internet] OR [Device] OR [Wearable] OR [Mobile] OR [Machine learning] OR [Artificial] OR [Biofeedback] OR [Neurofeedback] OR [Momentary] OR [Computerized] OR [Heart rate variability] OR [actigraphy] OR [actimetry] OR [digital] OR [virtual reality] OR [Tele] OR [video]. RESULTS We analyzed 364 articles, of which 62 were included. Our review was divided into 3 parts: prediction, assessment (including diagnosis, screening, and monitoring), and intervention. CONCLUSIONS The review showed that the place of connected objects, machine learning, and remote monitoring has yet to be defined in OCD. Smartphone assessment apps and the Web Screening Questionnaire demonstrated good sensitivity and adequate specificity for detecting OCD symptoms when compared with a full-length structured clinical interview. The ecological momentary assessment procedure may also represent a worthy addition to the current suite of assessment tools. In the field of intervention, CBT supported by smartphone, internet, or computer may not be more effective than that delivered by a qualified practitioner, but it is easy to use, well accepted by patients, reproducible, and cost-effective. Finally, new technologies are enabling the development of new therapies, including biofeedback and virtual reality, which focus on the learning of coping skills. For them to be used, these tools must be properly explained and tailored to individual physician and patient profiles.

Forecasting US movies box office performances in Turkey using machine learning algorithms

2020 ◽  
Vol 39 (5) ◽  
pp. 6579-6590
Author(s):  
Sandy Çağlıyor ◽  
Başar Öztayşi ◽  
Selime Sezgin
Keyword(s):  
Machine Learning ◽  
Global Economy ◽  
Learning Algorithms ◽  
Forecast Model ◽  
Machine Learning Algorithms ◽  
Gradient Boosting ◽  
High Stakes ◽  
Box Office ◽  
Industry Forecast ◽  
The Impact

The motion picture industry is one of the largest industries worldwide and has significant importance in the global economy. Considering the high stakes and high risks in the industry, forecast models and decision support systems are gaining importance. Several attempts have been made to estimate the theatrical performance of a movie before or at the early stages of its release. Nevertheless, these models are mostly used for predicting domestic performances and the industry still struggles to predict box office performances in overseas markets. In this study, the aim is to design a forecast model using different machine learning algorithms to estimate the theatrical success of US movies in Turkey. From various sources, a dataset of 1559 movies is constructed. Firstly, independent variables are grouped as pre-release, distributor type, and international distribution based on their characteristic. The number of attendances is discretized into three classes. Four popular machine learning algorithms, artificial neural networks, decision tree regression and gradient boosting tree and random forest are employed, and the impact of each group is observed by compared by the performance models. Then the number of target classes is increased into five and eight and results are compared with the previously developed models in the literature.

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