Direction and Location Are Not Sufficient for Navigating in Nonrigid Environments: An Empirical Study in Augmented Reality

2007 ◽  
Vol 16 (6) ◽  
pp. 584-602 ◽  
Author(s):  
Caroline G. L. Cao ◽  
Paul Milgram
Keyword(s):  
Augmented Reality ◽  
Spatial Orientation ◽  
Spatial Information ◽  
Human Colon ◽  
Shape Information ◽  
Navigational Aid ◽  
Speed Up ◽  
Location Judgment ◽  
Direction Information ◽  
Rigid Environment

Nonrigid environments, such as the human colon, present unique challenges in maintaining spatial orientation during navigation. This paper presents a design concept for presenting spatial information in an augmented reality (AR) display, together with results of an experiment conducted to evaluate the relative usefulness of three types of spatial information for supporting navigation and spatial orientation in a nonrigid environment. Sixteen untrained subjects performed a simulated colonoscopy procedure, using rigid and nonrigid colon models and six different AR displays comprising various combinations of direction, location, and shape information related to the scope inside the colon. Results showed that, unlike navigating in rigid environments, subjects took 44% longer to navigate the nonrigid environment and were less efficient, and suggested that it may be useful to train aspiring endoscopists in an equivalent rigid environment initially. A navigational aid presenting shape information was more beneficial than location or direction information for navigating in the nonrigid environment. Even though the AR navigational aid display did not speed up travel time, navigation efficiency and confidence in direction and location judgment for all subjects were improved. Subjectively, subjects preferred having shape information, in addition to position and direction information, in the navigational aid.

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

2021 ◽  
Vol 11 (13) ◽  
pp. 6047
Author(s):  
Soheil Rezaee ◽  
Abolghasem Sadeghi-Niaraki ◽  
Maryam Shakeri ◽  
Soo-Mi Choi
Keyword(s):  
Big Data ◽  
Augmented Reality ◽  
User Study ◽  
Spatial Information ◽  
Ease Of Use ◽  
Tourist Attraction ◽  
Gender And Education ◽  
Time Distance ◽  
Data Source ◽  
The Right

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%.

DEVELOPMENT OF AN APPLICATION WITH AN AUGMENTED REALITY FUNCTION TO SPEED UP PASSENGER SERVICE ON THE EXAMPLE OF PULKOVO AIRPORT

2021 ◽  
Vol 2 (28) ◽  
pp. 85-96
Author(s):  
A. E. Evtushenko ◽  
◽  
M. A. Kropaneva ◽  
Keyword(s):  
Information Technology ◽  
Augmented Reality ◽  
Key Words ◽  
Information Technologies ◽  
Technical Support ◽  
Air Transport ◽  
Passenger Transport ◽  
The World ◽  
Speed Up

This article offers a prototype of an application for smartphones, aimed at improving services and increasing the speed of passenger service on the example of Pulkovo Airport. The software helps to improve the information and multimedia and technical support of the airport. The existing information technologies and the experience of their application in various airports of the world are considered. Key words: air transport, airport, passenger service, air passenger transport, St. Petersburg, application, information technology.

scholarly journals Novel Method of Semantic Segmentation Applicable to Augmented Reality

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1737 ◽  
Author(s):  
Tae-young Ko ◽  
Seung-ho Lee
Keyword(s):  
Augmented Reality ◽  
Spatial Information ◽  
Ground Truth ◽  
Semantic Segmentation ◽  
Frame Rate ◽  
Feature Maps ◽  
Residual Network ◽  
Feature Map ◽  
Pascal Voc ◽  
Novel Method

This paper proposes a novel method of semantic segmentation, consisting of modified dilated residual network, atrous pyramid pooling module, and backpropagation, that is applicable to augmented reality (AR). In the proposed method, the modified dilated residual network extracts a feature map from the original images and maintains spatial information. The atrous pyramid pooling module places convolutions in parallel and layers feature maps in a pyramid shape to extract objects occupying small areas in the image; these are converted into one channel using a 1 × 1 convolution. Backpropagation compares the semantic segmentation obtained through convolution from the final feature map with the ground truth provided by a database. Losses can be reduced by applying backpropagation to the modified dilated residual network to change the weighting. The proposed method was compared with other methods on the Cityscapes and PASCAL VOC 2012 databases. The proposed method achieved accuracies of 82.8 and 89.8 mean intersection over union (mIOU) and frame rates of 61 and 64.3 frames per second (fps) for the Cityscapes and PASCAL VOC 2012 databases, respectively. These results prove the applicability of the proposed method for implementing natural AR applications at actual speeds because the frame rate is greater than 60 fps.

A Novel Algorithm for Image Data Segmentation

2014 ◽  
Vol 971-973 ◽  
pp. 1499-1503
Author(s):  
Zuo Wei Huang ◽  
Shu Guang Wu ◽  
Tao Xin Zhang
Keyword(s):  
Spatial Information ◽  
Rapid Development ◽  
Image Data ◽  
Segmentation Method ◽  
Mountainous Areas ◽  
Shape Information ◽  
Data Segmentation ◽  
Merging Procedure ◽  
Spatial Information Technology ◽  
Novel Algorithm

With the rapid development of spatial information technology and the increasingly artificial intelligence knowledge, MAS plays a more and more important role in conducting image segmentation.Considering the shortcomings of current segmentation method,a new algorithm based on MAS theory is proposed, It combines spectral and shape information in region merging.employer a number of agents to control the merging procedure in different regions and make the global merging control more optimal by utilizing the advantages of MAS,The results show that the algorithm is very effective for image segmentation both in urban and mountainous areas.

scholarly journals Mobile Augmented Reality for Low-End Devices Based on Planar Surface Recognition and Optimized Vertex Data Rendering

2021 ◽  
Vol 11 (18) ◽  
pp. 8750
Author(s):  
Styliani Verykokou ◽  
Argyro-Maria Boutsi ◽  
Charalabos Ioannidis
Keyword(s):  
Augmented Reality ◽  
Pose Estimation ◽  
Image Matching ◽  
Mobile Augmented Reality ◽  
Surface Recognition ◽  
Wide Range ◽  
Speed Up ◽  
Time Required ◽  
Fast Rendering ◽  
Textured Models

Mobile Augmented Reality (MAR) is designed to keep pace with high-end mobile computing and their powerful sensors. This evolution excludes users with low-end devices and network constraints. This article presents ModAR, a hybrid Android prototype that expands the MAR experience to the aforementioned target group. It combines feature-based image matching and pose estimation with fast rendering of 3D textured models. Planar objects of the real environment are used as pattern images for overlaying users’ meshes or the app’s default ones. Since ModAR is based on the OpenCV C++ library at Android NDK and OpenGL ES 2.0 graphics API, there are no dependencies on additional software, operating system version or model-specific hardware. The developed 3D graphics engine implements optimized vertex-data rendering with a combination of data grouping, synchronization, sub-texture compression and instancing for limited CPU/GPU resources and a single-threaded approach. It achieves up to 3 × speed-up compared to standard index rendering, and AR overlay of a 50 K vertices 3D model in less than 30 s. Several deployment scenarios on pose estimation demonstrate that the oriented FAST detector with an upper threshold of features per frame combined with the ORB descriptor yield best results in terms of robustness and efficiency, achieving a 90% reduction of image matching time compared to the time required by the AGAST detector and the BRISK descriptor, corresponding to pattern recognition accuracy of above 90% for a wide range of scale changes, regardless of any in-plane rotations and partial occlusions of the pattern.

scholarly journals Mobile augmented reality based indoor map for improving geo-visualization

2021 ◽  
Vol 7 ◽  
pp. e704
Author(s):  
Wei Ma ◽  
Shuai Zhang ◽  
Jincai Huang
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Semantic Information ◽  
Spatial Information ◽  
Spatial Relationship ◽  
Space Structure ◽  
Indoor Navigation ◽  
Method Error ◽  
The Real ◽  
Camera View

Unlike traditional visualization methods, augmented reality (AR) inserts virtual objects and information directly into digital representations of the real world, which makes these objects and data more easily understood and interactive. The integration of AR and GIS is a promising way to display spatial information in context. However, most existing AR-GIS applications only provide local spatial information in a fixed location, which is exposed to a set of problems, limited legibility, information clutter and the incomplete spatial relationships. In addition, the indoor space structure is complex and GPS is unavailable, so that indoor AR systems are further impeded by the limited capacity of these systems to detect and display location and semantic information. To address this problem, the localization technique for tracking the camera positions was fused by Bluetooth low energy (BLE) and pedestrian dead reckoning (PDR). The multi-sensor fusion-based algorithm employs a particle filter. Based on the direction and position of the phone, the spatial information is automatically registered onto a live camera view. The proposed algorithm extracts and matches a bounding box of the indoor map to a real world scene. Finally, the indoor map and semantic information were rendered into the real world, based on the real-time computed spatial relationship between the indoor map and live camera view. Experimental results demonstrate that the average positioning error of our approach is 1.47 m, and 80% of proposed method error is within approximately 1.8 m. The positioning result can effectively support that AR and indoor map fusion technique links rich indoor spatial information to real world scenes. The method is not only suitable for traditional tasks related to indoor navigation, but it is also promising method for crowdsourcing data collection and indoor map reconstruction.

scholarly journals PARALLEL IMPLEMENTATION OF MORPHOLOGICAL PROFILE BASED SPECTRAL-SPATIAL CLASSIFICATION SCHEME FOR HYPERSPECTRAL IMAGERY

2016 ◽  
Vol XLI-B7 ◽  
pp. 263-267
Author(s):  
B. Kumar ◽  
O. Dikshit
Keyword(s):  
Classification Accuracy ◽  
Graphics Processing Units ◽  
Spatial Information ◽  
Parallel Implementation ◽  
Hyperspectral Imagery ◽  
Hyperspectral Images ◽  
Important Concern ◽  
Speed Up ◽  
Graphics Processing ◽  
The Impact

Extended morphological profile (EMP) is a good technique for extracting spectral-spatial information from the images but large size of hyperspectral images is an important concern for creating EMPs. However, with the availability of modern multi-core processors and commodity parallel processing systems like graphics processing units (GPUs) at desktop level, parallel computing provides a viable option to significantly accelerate execution of such computations. In this paper, parallel implementation of an EMP based spectralspatial classification method for hyperspectral imagery is presented. The parallel implementation is done both on multi-core CPU and GPU. The impact of parallelization on speed up and classification accuracy is analyzed. For GPU, the implementation is done in compute unified device architecture (CUDA) C. The experiments are carried out on two well-known hyperspectral images. It is observed from the experimental results that GPU implementation provides a speed up of about 7 times, while parallel implementation on multi-core CPU resulted in speed up of about 3 times. It is also observed that parallel implementation has no adverse impact on the classification accuracy.

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