Handbook of Augmented and Virtual Reality (eBook)

Sumit Badotra, Sarvesh Tanwar, Ajay Rana, Nidhi Sindhwani, Ramani Kannan (Herausgeber)

eBook Download: EPUB

2023
218 Seiten
De Gruyter (Verlag)
978-3-11-078531-9 (ISBN)

Infos

Dr. Sumit Badotra is an Assistant Professor in the School of Computer Science and Engineering, School of Computer Science and Engineering, Bennett University, Greater Noida, Uttar Pradesh, India. He has around 5+ years of teaching and research experience in Software Defined Networks (SDN). His general research interests are in the areas of Network Security and Computer Networks and specific research interests include Intrusion Detection,
Protection from Internet Attacks. During his PhD work he has also worked on a Project funded by DST Govt. of India as a Research fellow. More than 60 papers are published in SCI/Scopus/UGC approved journals and reputed national/international conferences/book chapters. In continuation to this he has published many patents in the relevant fields. He has attended many national level FDPs and workshops and acted as a resource person as well.
He is an active reviewer in various reputed journals. Currently he is exploring Intrusion Detection in Cloud based Web servers using SDN.

Dr. Sarvesh Tanwar is an Associate Professor at Amity Institute of Information Technology (AIIT), Amity
University, Noida. She heads the AUN Blockchain & Data Security Research Lab. With over 15 years of teaching and research experience, her research areas include Public Key Infrastructure (PKI), Cryptography,
Blockchain and Cyber Security. She has published more than 100 research papers in international journals
and conferences. Dr. Tanwar is currently guiding six Ph. D. scholars and has supervised one Ph. D. scholar
and five M.Tech research scholars. She has filed 21 patents (15 published) and two copyrights in the relevant field. She is senior member of IEEE, a Life-Member of the Cryptology Research Society of India (CRSI),
the Indian Institute of Statistics, Kolkata, India and member of International Association of Computer Science and Information Technology (IACSIT) in Singapore. Dr. Tanwar serves as a reviewer in various reputed
journals.

Prof. (Dr.) Ajay Rana is Professor of Computer Science and Engineering, currently serving as Director General in Amity University Uttar Pradesh, Greater Noida. With over two decades of experience in academics and industry, Dr. Rana completed his M. Tech. and Ph. D. in Computer Science and Engineering from reputed institutes of India. He has 117 patents under his name in the field of IoT, Networks, and Sensors. He has published more than 306 research papers in reputed journals and international and national conferences, co-authored bine books and co-edited 45 conference proceedings. Eighteen students have completed their Ph. D. under his supervision and currently six students are working on their doctorates with his guidance.

Dr. Nidhi Sindhwani is currently working as an Amity Institute of Information Technology (AIIT), Amity University, Noida., India. She has done her Ph.D. (ECE) from Punjabi University, Patiala, Punjab, India. She has teaching experience of more than 15 years. She is a Life Member of Indian Society for Technical Education (ISTE) and Member of IEEE. She has published 3 book chapters in reputed books, 10 papers in Scopus/SCIE Indexed Journals and 4 Patents. She has presented various research papers in National & International Conferences. She has chaired a Session in two International Conferences. Her research areas include Wireless Communication, Image Processing, Optimization, Machine Learning, IoT etc.

Dr.Ramani Kannan is currently working as Senior Lecturer, Center for Smart Grid Energy Research, Institute of Autonomous system. University Teknologi PETRONAS (UTP), Malaysia. Dr. Kanan completed Ph.D. (Power Electronics and Drives) from Anna University, India in 2012, M.E. (Power Electronics and Drives) from Anna University, India in 2006, B.E (Electronics and Communication) from Bharathiyar University, India in 2004. He has more than 15 years of experience in prestigious educational institutes. Dr. Kannan has published more than 130 papers in various reputed national and international journals and conferences. He is the editor, co-editor, guest editor and reviewer of various books including Springer Nature, Elsevier etc. He has received award for best presenter in CENCON 2019, IEEE Conference on Energy Conversion (CENCON 2019) Indonesia.

Textauszug

1 Supplementing the Markerless AR with machine learning: Methods and approaches

Gunseerat Kaur

Department of Computer Science and Engineering, Lovely Professional University, India

Abstract

Augmented reality enhances real-world entities with the addition of relevant digital information. This interactive and engaging technology is incorporating itself into wider areas of technical applications. Although it is still in its evolving stage, it has garnered positive responses from audiences who interact with AR-based systems. Industries like gaming, healthcare, and education are benefiting from various augmented reality implementations. The main focus of using AR is to produce more effective interaction with the physical real world as a template and render a separate scenario above it. Initially, a marker-based system was created to project information from a marked region or image with specific characteristics, leading to multiple marketing and retail services adopting this feature. In these applications, a particular marker is sorted through view and detected to project the AR-related information, which further triggers the application’ response towards the marker. The baseline strategy includes image capture, processing for markers, tracking the results, and final rendering. However, to create more innovative forms of augmented reality, this concept was merged with machine learning to improve on the aspects that do not revolve around only particular markers. Markerless AR features superimposing graphics on the basis of locations, contour, or projection. This led to the exploration of various machine learning-based algorithms to improve the decision-making accuracy for AR applications. The methodology includes understanding the real environment and fabricating designs accordingly without the need to look for triggering markers. This chapter enlists various markerless AR techniques that use machine learning methods to increase the capability and coherence. The focus is to identify approaches used from machine learning algorithms to amplify efficacy of augmented reality applications using markerless services.

1.1 Introduction

As humans we are likely learning much from our surrounding environment, and our senses work together to procure us information that is processed, memorized, and stored in our minds. It is this highly valuable practice that initiates and creates a channel to extend our knowledge and zeal for learning or noticing the external environment. Augmented Reality changes this practice slightly by altering the method of interaction with the environment, converting conventional methods to superficial ones. It is this technical enhancement that has amended our perception of vicinity. The implementation of Augmented Reality involves the creation of a specious facade over the real world, thereby creating a separate vision for the actual environment. It creates an approximate or qualitative vision for the surroundings that often shows altered or customized perspectives. The idea of AR is to enhance its user’s point of view towards reality.

AR was initiated back in 1968 when Ian Sutherland created the first HMD (Head-mounted display) headset to increase the sensory perception of the world [1]. Since the 1970s, pivotal growth has been observed in establishing augmented reality as an effective technological establishment in a wide variety of applications. Several attempts have been made to incorporate the ideas of using AR into medicine, marketing, and education. Platforms using Augmented Reality have seen huge increments in users for reasons including growing number of internet users, availability of high-quality handheld devices, and less usage of extra equipment that is necessary. Any successful technical enhancement can grow within the user’s community when it is readily available to its end-users without any extra hardware installation requirements [2]. AR effects generation generally requires the device to be capable of capturing images of surroundings and processing them, which can be easily achieved through the installation of a plugin or application to superimpose upon objects. Neural networks are providing diverse applications used for enhancing the subjectivity of AR-based projections [3], [4], [5]. Since the inception of AR in 1960s, the craze for this technology was enhanced mainly through gaming applications. The first application launched back in 2008, for an advertisement but gradually, as Figure 1.1 shows, the diversity and growth of AR during past two decades have provided a medium to enhance curiosity among users.

Figure 1.1 Mainstreaming of Augmented reality [6].

This chapter introduces the Augmented Reality as a technology, followed by Section 1.2 which discusses the types of AR, with a focus on markerless. Section 1.3 highlights the relevance of machine learning in AR implementation, while Section 1.4 discusses the contributions of other scholars in this area. Section 1.5 provides an in-depth methodology to show where machine learning is used. Furthermore Section 1.6 provides insights into current case studies in AR based on machine learning technologies, and Section 1.7 presents the conclusion.

1.2 Motivation

Augmented Reality has surely piqued the curiosity of many researchers and has played an important role in creation of innovative practices involving it. Machine learning is a pivotal field that is continuously enhancing its properties and increasing its boundaries. There is a growing amount of literature that supports the benefits of incorporating these two powerful technologies, as the strength of AR can be improved and enhanced through well-developed and practiced methods of ML. Researching through these papers, the results suggest considerable efforts of joining these technologies. This chapter aims to shed light on some of these pivotal studies while investigating and understanding the intricate balance between AR and ML. One major challenge that poses a current research gap in this approach lies within the devices projecting AR models. As multiple instances use common smartphones that have limited resources for computation, issues such as latency, speed, and dynamics of rendering AR models are delayed due to longer computations. To date, only a few studies have investigated this aspect as well.

1.3 How have AI and blockchain transformed AR?

Classifying AR into various different categories depends on the type of hardware used or how the end result is projected. In accordance with the theme of this chapter, the classification paradigm is the basis of how AR understands the real world and projects the final rendering [7].

As Figure 1.2 shows, the main classification at this level is between Marked AR and Markerless AR. Marker-based AR is an approach where a target object is identified, and a pivot is used to situate the rendered object on that specific surface.

Figure 1.2 Types of augmented reality.

This is an initial approach that is generally found in smartphone cameras to project different filters. Identification of a point is relevant in this form of AR. [8] have compared on how CNN and SVM can be used to identify the spot to render AR, showing 96.7 % and 92.5 % respectively. Their application is designed to recognize and project the alphabets for children in a learning app. Figure 1.3 shows a person making a digital purchase through a QR-code.

Figure 1.3 A marker-based approach requires markers such as QR codes to trigger an event. Image credit: [9].

Marker-based AR is successful when the point of reference is well-defined and output is based around it. However, in situations where the marker is not detected it proves to be unfruitful [2]. This led to creation of Markerless AR, which relies on incoming data from various sensors to place a rendered output in context with the real world. Mainly, as listed in Table 1.1, a simultaneous localization and mapping criteria are used, which creates an estimate to place objects in the real world.

Table 1.1Comparisons between marker-less and marker-based AR [2], [8], [10].

Comparisons

Marker-based AR

Marker-less AR

Reference point

Needed

Not needed

Image Description

Pre-provided

Calculated

Sensors

Accelerometer not needed

Accelerometer, Gyroscope needed

...

Produkt-Details

• Erscheint lt. Verlag: 21.8.2023
• Reihe/Serie: Augmented and Virtual Reality
• Reihe/Serie: Augmented and Virtual Reality
• Reihe/Serie: ISSN
• Reihe/Serie: ISSN
• Zusatzinfo: 10 b/w and 69 col. ill., 12 b/w tbl.
• Sprache: englisch
• Themenwelt: Informatik ► Theorie / Studium ► Künstliche Intelligenz / Robotik
• Schlagworte: Artificial Intelligence • augmented reality • computer vision • Human-Machine Interaction • Virtual Reality
• ISBN-10: 3-11-078531-5 / 3110785315
• ISBN-13: 978-3-11-078531-9 / 9783110785319