A full comparison of different techniques for marker-less augmented reality.
\r\n\tThis book will aim at serving as a complete and updated reference for a broad audience, including, students, orthotics, optometrist and ophthalmologist. The book will describe in detail general myopia features as well the most recent diagnostic techniques (e.g. OCT and visual field) which occupy a more and more relevant position in early myopia complications detection. It will explore the connection between myopia and other, popular disorders such as glaucoma, choroidal neovascularization, and retinal detachment: highly myopic eyes tend to have a retina and choroid thinner than normal and, then, the assessment of myopic eyes is far from being a simple task even with the most advanced imaging techniques. In the light of such observations, the book will give a special mention to pharmacological and surgical treatments currently available along with rehabilitation procedures and optical devices.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"41ca0f616bfa2745783b652b87ebedc3",bookSignature:"Prof. Felicia M. Ferreri",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/8705.jpg",keywords:"Myopia of prematurity, Retinal detachment, Intraocular pressure, Myopia and glaucoma, Myopic macular degeneration, Ocular motility,Visual rehabilitation, Clinical treatment, Retinal sensitivity, Visual Electrophysiology response, Visual Field, Ultrasonic Biometry",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"December 3rd 2018",dateEndSecondStepPublish:"December 24th 2018",dateEndThirdStepPublish:"February 22nd 2019",dateEndFourthStepPublish:"May 13th 2019",dateEndFifthStepPublish:"July 12th 2019",remainingDaysToSecondStep:"2 years",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"32442",title:"Prof.",name:"Felicia M.",middleName:null,surname:"Ferreri",slug:"felicia-m.-ferreri",fullName:"Felicia M. Ferreri",profilePictureURL:"https://mts.intechopen.com/storage/users/32442/images/system/32442.png",biography:"Felicia M. Ferreri graduated summa cum laude from University of Messina, Italy in 1998 and completed her ophthalmology residency at the Policlinico Universitario, Messina in 2002. She was interned at San Raffaele Hospital in Milan (Corneal Section) and at Hospital Careggi in Florence (pediatric ophthalmology diseases). She spent research periods in Seville ('Virginio del Rocio' hospital), Madrid ('San Carlos' hospital), Manchester ('The 'Bolton Hospital') and Rio de Janiero (Universidade Fluminense).\r\nShe served as co-investigator for many national and international clinical trials. Since 2002, she is an Assistant Professor in Ophthalmology at the University of Messina. Her research interests are in the areas of glaucoma, neuro-ophthalmology, pediatric ophthalmology, and cataract. She authored more than 50 scientific papers.",institutionString:"University of Messina",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Messina",institutionURL:null,country:{name:"Italy"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"191",title:"Ophthalmology",slug:"medicine-ophthalmology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"280415",firstName:"Josip",lastName:"Knapic",middleName:null,title:"Mr.",imageUrl:"https://mts.intechopen.com/storage/users/280415/images/8050_n.jpg",email:"josip@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copy-editing and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. 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Lipid peroxidation including the generation of reactive oxygen species (ROS), transforming growth factor-β, and tumor necrosis factor-α can be implicated as a cause of hepatic fibrosis.
Damage of any etiology, such as infection with hepatitis C virus (HCV) or hepatitis B virus (HBV), heavy alcohol intake, and iron overload, to hepatocytes can produce oxygen-derived free radicals and other ROS derived from lipid peroxidative processes. Persistent production of ROS constitutes a general feature of a sustained inflammatory response and liver injury, once antioxidant mechanisms have been depleted. The major source of ROS production in hepatocytes is NADH and NADPH oxidases localized in mitochondria (Figure 1). NADH and NADPH oxidases leak ROS as part of its operation. Kupffer cells (hepatic resident macrophages), infiltrating inflammatory cells such as macrophages and neutrophils, and HSCs also produce ROS in the injured liver.
ROS include the free radicals superoxide (O2-) and hydroxyl radical (HO-) and non-radicals such as hydrogen peroxide (H2O2). A number of reactive nitrogen species including nitric oxide (NO) and peroxynitrite (ONOO-) are also ROS. Superoxide production is mediated mainly by NADH oxidase. Hydrogen peroxide is more stable and membrane permeable in comparison to other ROS. Thus, hydrogen peroxide plays an important role in the intracellular signaling under physiological conditions. With respect to pathological actions, ROS participate in the development of liver disease. In this regard, hydrogen peroxide is converted into the hydroxyl radical, a harmful and highly reactive ROS, in the presence of transition metals such as iron (Figure 1). The hydroxyl radical is able to induce not only lipid peroxidation in the structure of membrane phospholipids, which results in irreversible modifications of cell membrane structure and function (membrane injury), but DNA cleavage (DNA injury) as well. Such a chain of events due to increased ROS production exceeding cellular antioxidant defense systems are called oxidative stress, inducing cell death.
Malondialdehyde (MDA) and 4-hydroxynonenal (HNE) (Figure 1), end products of lipid peroxidation, are discharged from destroyed hepatocytes into the space of Disse (Figure 2). Cells are well equipped to neutralize the effects of ROS by virtue of a series of the antioxidant protective systems, including superoxide dismutase (SOD), glutathione peroxidase, glutathione (GSH), and thioredoxin. Upon oxidation, GSH forms glutathione disulfide (GSSG).
Oxidative stress and hepatocyte damage (Shimizu et al., 2012). A primary source of reactive oxygen species (ROS) production is mitochondrial NADPH/NADH oxidase. Hydrogen peroxide (H2O2) is converted to a highly reactive ROS, the hydroxyl radical, in the presence of transition metals such as iron (+Fe) and copper. The hydroxyl radical induces DNA cleavage and lipid peroxidation in the structure of membrane phospholipids, leading to cell death and discharge of products of lipid peroxidation, malondialdehyde (MDA) and 4-hydroxynonenal (HNE) into the space of Disse. Cells have comprehensive antioxidant protective systems, including SOD, glutathione peroxidase and glutathione (GSH). Upon oxidation, GSH forms glutathione disulfide (GSSG).
A single liver injury eventually results in an almost complete resolution, but the persistence of the original insult causes a prolonged activation of tissue repair mechanisms, thereby leading to hepatic fibrosis rather than to effective tissue repair. Hepatic fibrosis, or the excessive collagen deposition in the liver (see next section), is associated with oxidative stimuli and cell death. Cell death is a consequence of severe liver damage that occurs in many patients with chronic liver disease, regardless of the etiology such as HCV/HBV infection, heavy alcohol intake, and iron overload.
At the cellular levels, origin of hepatic fibrosis is initiated by the damage of hepatocytes, followed by the accumulation of neutrophils and macrophages including Kupffer cells on the sites of injury and inflammation in the liver. When hepatocytes are continuously damaged, leading to cell death, production of extracellular matrix proteins such as collagens predominates over hepatocellular regeneration. Overproduced collagens are deposited in injured areas instead of destroyed hepatocytes. In other words, hepatic fibrosis is fibrous scarring of the liver in which excessive collagens build up along with the duration and extent of persistence of liver injury. Hepatic fibrosis itself causes no symptoms but can lead to the end-stage cirrhosis. In cirrhosis the failure to properly replace destroyed hepatocytes and the excessive collagen deposition to distort blood flow through the liver (portal hypertension) result in severe liver dysfunction. Cirrhosis is an important host-related risk factor for the development of hepatocellular carcinoma (HCC) in chronic hepatitis C and B, as well as a major factor predicting a poor response to interferon-based antiviral therapy in chronic hepatitis C. Staging of chronic liver disease by assessment of hepatic fibrosis always is a major function of prognostic interpretation of individual data including liver biopsy. Of the commonly used staging systems, the METAVIR fibrosis score has been widely used (Huwart et al., 2008). The stages are determined by both the quantity and location of the fibrosis. With this score, F0 represents no fibrosis; F1 (mild fibrosis), portal fibrosis without septa; F2 (moderate fibrosis), portal fibrous and few septa; F3 (severe fibrosis), numerous septa without cirrhosis; and F4, cirrhosis (Figure 3), the tissue is eventually composed of nodules surrounded completely by fibrosis.
Schema of the sinusoidal wall of the liver (Shimizu et al., 2012). Schematic representation of hepatic stellate cells (HSCs) was based on the studies by Wake (Wake, 1999). Kupffer cells (hepatic resident macrophages) rest on fenestrated endothelial cells. HSCs are located in the space of Disse in close contact with endothelial cells and hepatocytes, functioning as the primary retinoid storage area. Collagen fibrils course through the space of Disse between endothelial cells and the cords of hepatocytes.
Normal liver has a connective tissue matrix which includes collagen type IV (non-fibrillary), glycoproteins such as fibronectin and laminin, and proteoglycans such as heparan sulphate. These comprise the low density basement membrane in the space of Disse. Following liver injury there is a 3- to 8-fold increase in the extracellular matrix which is of a high density interstitial type, containing fibril-forming collagens (types 1 and III) as well as fibronectin, hyaluronic acid and proteoglycans. Collagen types 1 and III are major components of the extracellular matrix, which is principally produced by cells known as HSCs. HSCs are located in the space of Disse in close contact with hepatocytes and sinusoidal endothelial cells (Figure 2). Their three-dimensional structure consists of the cell body and several long and branching cytoplasmic processes (Wake, 1999). In the resting liver, HSCs have intracellular droplets containing retinoids. Retinoids refer to a group of chemical compound associated with vitamin A. HSCs contain approximately 50-80% of the whole body stores of retinoids (Blomhoff et al., 1990). In contrast, in the injured liver, HSCs are regarded as the primary target cells for inflammatory and oxidative stimuli, and they are proliferated, enlarged and transformed into myofibroblast-like cells. These HSCs are referred to as activated cells and are responsible for the overproduction of collagens during hepatic fibrosis to cirrhosis. This activation is accompanied by a loss of cellular retinoids, and the synthesis of α-smooth muscle actin (α-SMA), and large quantities of the major components of the extracellular matrix including collagen types I, III, and IV, fibronectin, laminin and proteoglycans. α-SMA is produced by activated HSCs (myofibroblast-like cells) but not by resting (quiescent) HSCs, thereby a marker of HSC activation. Moreover, activated HSCs produce ROS and transforming growth factor-β (TGF-β) (Figure 4). TGF-β is a major fibrogenic cytokine, regulating the production, degradation and accumulation of the extracellular matrix in hepatic fibrosis. TGF-β expression correlates with the extent of hepatic fibrosis (Castilla et al., 1991). This cytokine induces its own expression in activated HSCs, thereby creating a self-perpetuating cycle of events, referred to as an autocrine loop. TGF-β is also released in a paracrine manner from Kupffer cells, endothelial cells, and infiltrating inflammatory cells following liver injury. Similarly, ROS are produced by activated HSCs in response to ROS released from adjacent cells such as destroyed hepatocytes and activated Kupffer cells.
Stages of hepatic fibrosis in chronic hepatitis according to the five stages (0-4) of the METAVIR scoring system (1994). With this score, F0 represents no fibrosis; F1 (mild fibrosis), fibrous expansion of portal areas without septa; F2 (moderate fibrosis), fibrous septa extend to form bridges between adjacent vascular structures, both portal to portal and portal to central, occasional bridges; F3 (severe fibrosis), numerous bridges or septa without cirrhosis; and F4 (cirrhosis), the tissue is eventually composed of nodules surrounded completely by fibrosis.
During liver injury, HSCs are proliferated, enlarged and transformed into myofibroblast-like cells (Shimizu, 2001). These activated HSCs produce large quantities of collagens, α-smooth muscle actin (α-SMA), ROS, and transforming growth factor-β (TGF-β), and lose cellular retinoids.
HSCs are activated mainly by ROS, products of lipid peroxidation (MDA and HNE) (Lee et al., 1995; Parola et al., 1993), and TGF-β, which are released from destroyed hepatocytes, activated Kupffer cells and infiltrating macrophages and neutrophils in the injured liver (Figure 5). In addition to ROS, exogenous TGF-β increases the production of ROS, particularly hydrogen peroxide, by HSCs, whereas the addition of hydrogen peroxide induces ROS and TGF-β production and secretion by HSCs (De Bleser et al., 1999). This so-called autocrine loop of ROS by HSCs is regarded as mechanism corresponding to the autocrine loop of TGF-β which HSCs produce in response to this cytokine with an increased collagen expression in the injured liver (Itagaki et al., 2005).
Activation of HSCs. HSCs are activated by such factors as ROS, lipid peroxidation products (MDA and HNE), and TGF-β released when adjacent cells including hepatocyte, Kupffer cells, and endothelial cells are injured. ROS and TGF-β are also produced by HSCs in response to exogenous ROS and TGF-β in an autocrine manner.
Other important factors for HSC activation are platelet-derived growth factor (PDGF) released from platelets, and endothelin-1 from endothelial cells. PDGF is the most potent mitogen. HSCs congregate in the area of injury, through proliferation and migration from elsewhere, in response to the release of PDGF and monocyte chemotactic peptide-1 (MCP-1). MCP-1 is produced by activated Kupffer cells and infiltrating macrophages and neutrophils. The number of activated HSCs also increases after liver injury (Enzan et al., 1994).
At the molecular levels, HSCs express the genes which encode for enzymes such as matrix metalloproteinase (MMP)-1 (interstitial collagenase) (Casini et al., 1994), which digests native fibrillar collagen types I and III, and MMP-2 (Milani et al., 1994), which digests denatured collagen types I and III and native collagen type IV, as well as tissue inhibitors of MMPs (TIMP)-1 and TIMP-2 (Iredale et al., 1992). Imbalance between matrix synthesis and degradation plays a major role in hepatic fibrosis (Shimizu, 2001). Matrix degradation depends upon the balance between MMPs, TIMPs and converting enzymes (MT1-MMP and stromelysin) (Li and Friedman, 1999). Collagen types I and III constitute the main framework of the so-called “fibrillar matrix”. The space of Disse is a virtual space constituted by an extracellular matrix network composed of collagen type IV and non-collagenous components such as laminin. The large majority of collagen types III and IV, and laminin are synthesized by HSCs and endothelial cells, whereas all cell types synthesize small amounts of collagen type I. During hepatic fibrosis, however, HSCs become the major extracellular matrix producing cell type, with a predominant production of collagen type I (Maher and McGuire, 1990). In the resting liver, a balance between matrix synthesis and degradation exists, whereas, in the injured liver, the balance is disrupted. The net result of the changes during hepatocyte damage is increased degradation of the normal basement membrane collagen, and reduced degradation of interstitial-type collagens. The latter can be explained by increased TIMP-1 and TIMP-2 expressions relative to MMP-1. The degradative portion of the remodeling process is coordinated by MMPs and TIMPs.
Origin of hepatic fibrosis is initiated by the damage of hepatocytes, resulting in the recruitment of inflammatory cells and platelets, and activation of kupffer cells, with subsequent release of cytokines and growth factors. HSCs are the primary target cells for these inflammatory and oxidative stimuli, because during hepatic fibrosis, HSCs undergo an activation process to a myofibroblast-like cell, which represents the major matrix-producing cell. In the injured liver, hydrogen peroxide seems to act as a second messenger to regulate signaling events including mitogen activated protein kinase (MAPK) activation. The MAPK family includes three major subgroups, extracellular signal regulated kinase (ERK), p38 MAPK (p38), and c-Jun N-terminal kinase/stress activated protein kinase (JNK). MAPK participates in the intracellular signaling to: (1) induce the gene expression of redox sensitive transcription factors, such as activator protein-1 (AP-1) and nuclear factor κB (NF-κB) (Pinkus et al., 1996), (2) stimulate apoptosis (Clement and Pervaiz, 1999), and (3) modulate cell proliferation (Lundberg et al., 2000). ERK and JNK lie upstream of AP-1. JNK and p38 activation are more important in stress responses such as inflammation, which can also activate NF-κB. AP-1 and NF-κB induce the expression of multiple genes involved in inflammation and oxidative stress response, cell death and fibrosis, including proinflammatory cytokines such as tumor necrosis factor-α (TNF-α), interleukin-1 and interleukin-6 and growth factors such as PDGF and TGF-β. TGF-β is a major fibrogenic cytokine, acting as a paracrine and autocrine (from HSCs) mediator as already noted. TGF-β triggers and activates the proliferation, enlargement and transformation of HSCs, but it exerts its inhibitory effect on hepatocyte proliferation (Nakamura et al., 1985).
Since many cytokines exert growth factor like activity, in addition to their specific proinflammatory effects, the distinction between cytokines and growth factors is somewhat artificial. No growth factor or cytokine acts independently. The injured liver, predominantly Kupffer cells and infiltrating macrophages and neutrophils, produces TNF-α, interleukin-1 and interleukin-6. These proinflammatory cytokines may also also inhibit hepatic regeneration. In particular, TNF-α plays a dichotomous role in the liver, where it not only induces hepatocyte proliferation and liver regeneration but also acts as a mediator of cell death (Schwabe and Brenner, 2006). During TNF-α-induced apoptosis in hepatocytes, hydrogen peroxide is an important mediator of cell death (Bohler et al., 2000).
In liver injury of hepatitis virus infection, transgenic mice expressing HBsAg exhibit the generation of oxidative stress and DNA damage, leading to the progression of hepatic fibrosis and carcinogenesis (Hagen et al., 1994; Nakamoto et al., 2004). In addition, HBV X protein changes the mitochondrial transmembrane potential and increases ROS production in the liver (Waris et al., 2001). Moreover, structural and non-structural (NS) proteins of HCV are involved in the production of ROS in an infected liver. HCV core protein is associated with increased ROS, decreased intracellular and/or mitochondrial glutathione content, and increased levels of lipid peroxidation products (Moriya et al., 2001). Glutathione is an antioxidant. NS3 protein of HCV activates NADPH oxidase in Kupffer cells to increase production of ROS, which can exert oxidative stress on nearby cells (Thoren et al., 2004).
Hepatic fibrosis is a complex dynamic process which is mediated by death of hepatocytes and activation of HSCs. Lipid peroxidation including the generation of ROS, TGF-β, and TNF-α can be implicated as a cause of hepatic fibrosis. HSCs are regarded as the primary target cells for inflammatory stimuli, and produce extracellular matrix components. HSCs are activated by such factors as ROS, lipid peroxidation products (MDA and HNE), and TGF-β released when adjacent cells including hepatocyte, Kupffer cells, and endothelial cells are injured. ROS and TGF-β are also produced by HSCs in response to exogenous ROS and TGF-β in an autocrine manner. During TNF-α-induced death in hepatocytes, ROS is an important mediator of cell death. The most common cause of hepatic fibrosis is currently chronic HCV/HBV infection.
Understanding the basic mechanisms underlying the ROS-mediated fibrogenesis provides valuable information on the search for effective antifibrogenic therapies.
Augmented reality is a technology in which user’s view of a real scene is augmented with extra virtual information. Augmented reality (AR) registration of virtual objects is required having an accurate tracking or camera pose estimation, but tracking is one of the key technical challenges of AR system [1]. AR has many conceivable applications in a wide range of fields such as education, construction, public health, manufacturing and entertainment. With the increased computational speed and advancement of particular computer technology, augmented reality applications become possible in multidisciplinary fields, for example, education, simulation, entertainment, medical and games. Researches related to augmented reality (AR) and virtual reality (VR) have shown significant growth with the development of interactive computer technology and sophisticated 3D modelling packages [2]. Virtual heritage is considered one of the important fields in the computer-based interactive technologies in virtual reality [3]. It created visual representation of monument, artefacts, building and culture to present openly to global audiences. However, virtual heritage becomes as a platform for promoting the education process, motivating and understanding particular events and historical elements for the use of students and researchers. Augmented reality techniques can be classified into two main categories: vision-based AR and location-based AR [4, 5]. Location-based AR uses the capability of a specific device to determine its position in the world, for example, GPS and then the retrieval relevant information to that location. Then, this information is superimposed into the output of their device’s camera to permit a more natural data presentation compared only using the map alone. Vision-based AR particularly depends on processing the data that is extracted from the images or video frames that have been taken by the device. This kind of AR includes a number of techniques that lend significantly from computer vision to the range, where research progress in AR relies on the progress of the latter [6]. Lately, augmented reality technology has become an accepted technology among scientific community and even public, which is used for merging of real and virtual objects, and mixed it into the real-world environment. However, this technology is used in virtual heritage to improve the visitor experience of a cultural heritage site, as well as, the possibility to present the ancient-ruined building without any damage. In this chapter, we have presented a survey of marker-less AR. This survey is based on the state-of-the-art related to marker-less AR such as indoor marker-less AR, outdoor marker-less AR, real-time solutions to the tracking problem, real-time registration and cultural heritage in AR. Section 3 introduces the marker-based AR and Section 4 is allocated to marker-less AR, while Section 5 presents the researches related to cultural heritage in augmented reality.
\nSection 6 reveals the issues with virtual heritage in augmented reality and Section 7 presents 3D reconstruction techniques for cultural heritage. Section 8 is all about location in AR. The work is concluded by section 9 which is the conclusion.
\nAugmented reality is considered as one of the modern technologies that blends virtual objects into the real world. Augmented reality (AR) can be simply defined as a live and integrate direct or indirect view of a physical, real-world environment. It is real-time data that elements are augmented by computer-generated virtual content, for example, sound, video, graphics, or GPS data [7, 8]. Augmented reality is considered as an area in which 3D virtual objects are completely integrated into a 3D real environment in real time. An AR environment supplements the real-world with virtual objects, which are generated by using computer that appear to coexist in the same space as the real world [9]. In another words, augmented reality can be defined as the interactivity of humans with virtual objects that is located in the real environment in order to help the user in executing a task in a physical setting. AR is one of the significant forms of mixed reality (MR), in which real and virtual objects are mixed and showed in a single display in the same time and location as shown in Figure 1. Augmented reality seems like fiction because it creates interactive interfaces that specify the delusion that physical and virtual worlds are connected together and that users can physically cross from one to the other [10].
\nReality-virtuality continuums [23].
AR does not replace reality, as the virtual reality (VR); it complements real environment with digital information, virtual and computer-generated graphics and/or virtual objects as shown in Figure 2. However, users navigate in VR by using a computer simulated or imaginary environment called a virtual environment, preventing the real environment. In this environment, all users’ senses are controlled using a computer and immersed in a simulated environment [11] as shown in Figure 3.
\nA real environment view is augmented with digital information.
A user navigating in a virtual environment of VR.
Marker-based AR uses markers (A two-dimensional pre-defined screen) that are placed in the scene and within the field of vision of the camera in order to help guide the camera pose estimation process [8]. The markers are frequently indicating to as fiducial markers because their position and orientation relative to the scenery are steady. The markers are always planar makers and commonly have powerful feature, for example, long edges, as well as, corners among black and white regions. In this technique, AR puts a powerful emphasis on the design of the marker. One of the most common kinds of marker design is square because the feature of square will allow for accurate localisation of the markers by using its four corner points [12]. Marker-based AR uses computer vision techniques in order to calculate the position and orientation of the camera relative to the marker. The virtual 3D objects can be overlaid accurately on the markers as shown in Figure 4. It has a primary operational principle: capture the video input from the camera, add 3D graphics to the scene and show the augmented frames as a video stream [13].
\nMarker-based AR [14].
Marker-less AR is completely different from marker-based AR because it does not depend on the artificial markers in order to reveal outstanding features in the scene. Marker-less AR systems work to integrate virtual objects into a 3D real environment in real-time, promote user’s perception of and interaction with the real world [15]. Marker-less AR works by revealing features that are easily available from the natural objects in the scene, as well as, try to create a model or map from the scenery in order to represent the world as it is displayed by the camera.
\nThere are two main methods to camera pose estimation techniques called relative orientation and planar homography. Relative orientation is an approach that used to calculate the position and orientation of a camera relative to another from correspondences between five or more ray pairs. A ray pair can be defined as the vectors that arise from a fixed and visible point in the scenery to the camera centre positions [16]. Use the aspect of AR process that recruit computer vision algorithms, called feature detection and tracking, and then suggest a method to improve the subsequent process to output a best camera pose estimate [16]. A localised feature descriptor is used for the matching of salient feature points belonging to the present camera frame with those extracted from the reference frames. Camera pose can be estimated relative to it, however, the calculated 3D pose parameters can be used in order to render virtual objects into the real world [17]. Frikha et al. are proposed real-time monocular piece wise planar SLAM method using the planar scene assumption. Planar structures have used for mapping process in order to allow rendering virtual objects in a meaningful way, as well as improving the camera pose resolution in addition to the quality of 3-D reconstruction of the environment by adding restrictions on 3-D points, and settings in the optimisation process [18]. An energy function based on epipolar geometry has been developed in order to estimate intrinsic camera parameters during camera zooming [19]. Intrinsic camera parameters at each zoom value are calibrated, in order to obtain an accurate camera parameter estimation. The intrinsic camera parameters changes depending on the zoom value that are modelled [19].
\nAugmented reality is the real-time incorporation of the virtual and physical worlds into a new environment, where digital information is registered with real-world elements in a coherent method. One of the big challenges when working in outdoor AR is the registration of the virtual elements in the real-world environment, where it is not realistic to prepare every building with visual markers. This issue is certainly much more accurate when dealing with outdoor augmented reality. Most augmented reality applications are taking the benefit of backpack systems with head-worn displays [20] or handheld devices [21] in order to compose real-worlds’ views with digital information. Sophisticated hardware contains tracking devices, for example, GPS and gyroscope, which can be used to determine the position in the physical world.
\nBateau Ivre [22] project have presented on the Seine River in order to make a considerable audience conscious of the possible developments of augmented reality through an artistic installation in an outdoor environment. The installation can be seen from a ship by a huge number of audience without specified equipment, through night-time video-projection on the River banks. The augmentation of the physical world is implemented using real-time image processing for live special effects, for example, contouring, particles and non-realistic rendering. The technical objective of the project was to immerge the audience into a non-realistic view of the River banks that would be different from traditional tours that highlight the main features of Paris’ classical architecture. The implemented software is used in standard algorithms for particular effects to a live video stream and then re-projected these effects on the captured scenes to merge the real world with its modified image [23].
\nHowever, Sato et al. [5] have developed a novel marker-less AR system that uses local feature-based image registration and structure from motion (SfM) technology. The proposed system has some advantages, such as it supports free movement, less limitations, less efforts, as well as lower cost for outdoor AR applications. For the verification of the developed system, it has been applied to a renovation design project. One of the main advantages of the system is that it does not require particular equipment, for example, sensors for geometric registration between augmentations and the real world because the system uses sensor-based registration. Furthermore, the system does not need artificial markers, which reduce user’s flexibility [5]. The accuracy of system’s registration and tracking for this research is not enough for AR.
\nA development of a 3D map oriented handheld AR system has been presented by Chen et al. [24]. The system achieves geometric consistency by using a 3D map in order to obtain position data instead of using GPS, which provides low position information accuracy, especially in urban areas. In addition, the system features a gyroscope sensor to obtain posture data, as well as a video camera that used to capture live video of the present surroundings. All these components are installed in a smartphone and can be used to assess urban landscape. The authors have used the evaluation of registration accuracy in order to simulate an urban landscape from a short- to a long-range scale. The proposed AR system allows users to simulate a landscape from multiple viewpoints in addition to long-distance simultaneously, as well as walking around the viewpoint fields using just a smartphone [24]. The proposed system has the optical integrity and occlusion problem of the 3D-AR system when simulating urban landscape.
\nIn addition, Chen et al. [25] presented tracking natural features in an agricultural scene. The main objective of the system is to perform marker-less AR techniques in order to assist in the visualisation of robotic helicopter-related tasks. By creating a virtual marker under a known initial configuration of the robotic helicopter, camera and the ground plane, the system is able to continuously track the camera pose using the natural features of the image sequence to execute augmentation of virtual objects. A simulation using a mock-up model of an agriculture farm have developed to evaluate the performance of the marker-less AR system. The experiment results showed that there are a number of improvements, which need to be taken in consideration before distributing the system in actual flight. The intermittent movement of the virtual marker vertices must be reduced in order to obtain better camera pose estimation. A feature recovery algorithm is one of the most important techniques for scaling the marker-less AR system to operate outdoors on the robotic helicopter [25]. This technique is trembling in the virtual marker vertices. Therefore, camera pose estimation accuracy is low.
\nVirtual heritage in AR can be defined as an interactive computer-based technology, which can be used to achieve visual reconstruction, assist scholars and educators of traditional entities, for example, buildings, artefacts and culture [26]. This technology is used to maintain delicate historical buildings from natural disasters and sabotage [27]. In order to create a virtual heritage, there are seven main design principles, which must be taken into account such as high geometric accuracy, high level of automation capture for all details, low cost, photorealism, flexibility, portability and model size efficiency [28]. Cultural heritage layers are proposed to visualise historic media such as drawings, paintings and photographs of buildings and historic scenes seamlessly superimposed on real environment through video see through using X3D [29]. The registration of the virtual objects in the video images is done by using a robust 6DOF tracking framework depending on two technologies that work simultaneously: randomised trees are used for initialization step and a frame-to-frame tracking phase based on KLT. This technique achieved simple, cheap and sustainable development augmented reality applications in the area of the cultural heritage depending on industry standards [30]. The main idea of this research is to use current historic media from archives and superimpose them seamlessly on reality at the suitable place. These local layers are context sensitively telling the location’s history and give the impression of a virtual time trip. The results of the application showed in the area of cultural heritage, where the system runs on an Ultra Mobile PC (Sony Vaio UX) with 15 frames/sec. Only the reality filters and the 2D overlays can be selected by the application developer or online by the user [29]. This application is very simple and presented just 2D overlays, as well as the detection of the filter is done manually. Augmented reality for historical tourism using mobile devices has been proposed by Bres et al. [30]. The core of the proposed system is related to a marker-less outdoor augmented reality solution. This technique is based on scale invariant feature transform (SIFT) features for localisation and integration of 3D models into video. These features are used to project a digital model of the building facades of the square in order to get 3D coordinates for each feature point. The algorithms executed are responsible to calculate the camera pose for frame of a video from 3D-2D point correspondences among features that extracted from the current video frame and points in the reference dataset. The algorithms were successfully evaluated on video films of city squares. Despite they do not yet work in real-time, they are able to correct pose estimation and projection of artificial data into the scene. The algorithms automatically recover any loss of track. The research showed that the possibility of SIFT features are purely used for image-based marker-less outdoor augmented reality applications [30]. This research presented a simple mobile application that used to augment a small 3D image. HeladivaAR [31] proposed to reconstruct the historical and cultural heritage of Sri Lanka. HeladivaAR is a mobile phone application that used to show a reconstructed 3D model of these ancient ruins as they were in their initial state. In addition to use of AR technology, the application has used the mobile phone camera to determine and track the remaining ruins of the historical place and reconstructs the 3D model on it and then displays on the application interface. This application used different aspects to reconstruct the cultural heritage building such as image processing, 3D modelling, tracker identification using Android platform, historical books and reconstruct ruined sites. By using of AR, the real scene is enhanced by interactive multimedia information in order to increase the experience of the user, who can recover this information by a user-easy interface through their mobile phone. In education field, virtual heritage becomes a platform of learning, motivating and understanding of particular events and historical elements for students and researchers. This research provides a better understanding of Sri Lankan cultural heritage and allows users to gain interactive knowledge on archaeological facts of ancient kingdoms [31]. However, this research has several limitations. The first one is the application can apply only to android-based augmented reality devices; it cannot apply for the ISO-based operating system devices. The second limitation is the quality of the application that is based on the mobile device because it is not a desktop application. The last limitation is the application developed for Android 3.0 or above. The versions below may encounter rendering problems when running.
\nIndrawan developed marker-less augmented reality utilising gyroscope in order to demonstrate the position of Dewata Nawa Sanga [32]. This application is designed to learn, understand and recognise the properties of Dewata Nao Sanga by using a gyroscope. The sensor works to achieve the object of the deities in the coordinates to be identified, as well as, it is worked to provide information about Dewata Nawa Sanga along alongside and informative 3D animation. This research evaluates the usefulness, functionality of the application, in addition to the impact of the AR Dewata Nawa Sanga application that can motivate its users. The result of usability and satisfaction questionnaire value showed that the percentage average is 84.8%. It illustrates that the application is very useful for the participants to have knowledge about Dewata Nawa Sanga as well as very satisfied to use [32].
\nKolivand and El Rhalibi presented a new technique to augment a realistic virtual building in real environments to be observed live through an AR camera [23]. There are some outdoor components when augmented a realistic building, for example, the sun position, shadows, sky illumination and virtual traditional animated characters. It is augmented in real environments at the place of real historical buildings, or desirable locations, at different times of the day and different days of the year [2]. The authors have presented some new ideas in the case of virtual heritage. First of all is modelling the 3D model of Portuguese Malacca. A structured real-time system is provided to trace the sun position, by using Julian dating, and Perez sky model is used for modelling sky colour, have presented in order to create outdoor illumination. A semi-soft shadow algorithm has been implemented to support the realism of outdoor augmented reality systems. A simple camera setup system has used to present Marker-less AR. The final system can be installed on head mounted display (HMD) or in the proposed device called ReVitAge to show the realistic reconstructed virtual heritage buildings, taking into account the main components of outdoor illumination [23].
\nThere are four main issues related to the virtual heritage in augmented reality. These issues are registration, reconstruction orientation, tracking and location.
\nRegistration is one of the most significant issues in virtual heritage AR systems and currently subtracts some restrictions to different AR applications. Registration indicates the accurate compatibility of augmented objects with real environments [23]. Any AR system without an accurate registration leads to unsuccessful mixed environments because of the results of the defect wrong. The registration process is the overlay of virtual objects onto a real scene by using information that have extracted from the scene. Especially, this information is the feature points that extracted from the real scene using some tracking techniques. There are two categories of registration techniques, sensor-based and computer vision-based techniques. In sensor-based technique, there is a need to calibrate the external sensors, but the available sensors equipment’s are either huge or expensive, or lack satisfactory levels of accuracy. Computer vision-based methods techniques work to avert calibration of external sensors, as well as offer the possibility for accurate tracking without huge and costly equipment. It can be categorised as two main types depending on camera calibration requirements [33]. The first kind does not require any calibration of camera parameters in advance, which includes the use of a known 3D calibration object. However, the second type is assuming that the intrinsic camera parameters are pre-calibrated. This is a common assumption in most of the existing AR systems.
\nThere are several researches that work to develop the registration of the virtual elements in the real-world environments. These researches will be explained in the following section.
\nGao et al. [6] introduced a new technique to improve the stabilisation and the accuracy of marker-less registration in AR. Based on three-dimensional map information generated by visual simultaneous localisation-SLAM. The proposed technique allows tracking and registration of virtual objects in order to ensure a stable in addition of real-time performance of marker-less AR applications. The stability of the system can be performed by integrating the Hough voting algorithm with the repeated Closest Points (ICP) technique. The proposed technique is faster than the standard methods. In addition, it is able to achieve more accurate registration results when compared with the previous techniques. The experimental results showed that the proposed technique can efficiently repress the virtual object jittering, as well as a higher tracking accuracy with good performance [6]. This technique can identify only one object for each recognition. Kanade-Lucas-Tomasi (KLT) natural feature tracker and the reconstruction technology is presented by Pang et al. [33]. KLT tracker technique is used to track the identical feature points in two control images. The authors presented three key stages in the proposed technique. The first stage is the affine reconstruction. In this stage, two control images from the video sequence are chosen and the KLT tracker is used for the extraction of the natural feature points. After that, the Affine Coordinate System (ACS) is defined by using these natural feature points. The user is responsible to select four planar points for setting the Euclidean WCS in two control images, respectively, and then the affine coordinates of the specific points are reconstructed by using the affine reconstruction method. While, the second stage is re-projection. Compute the corresponding affine re-projection matrix in the live video frame by using the natural feature points that have been tracked by the KLT technique. The image projections of the selected points are predestined in the live video sequence by using the affine re-projection matrix. However, the third stage is the camera extrinsic parameters such as camera pose, which are predestined in terms of the four selected points achieved in the second stage. Eventually, the virtual objects can be rendered on the real scene by using the graphics pipeline techniques such as OpenGL. The experiment results showed some improvement compared to the previous work [33]. The main limitation of this research is that the user has to manually determine the four points in the initialization stage, as well as, the authors do not consider tracking the feature points.
\nReconstruction is one of the basic processes in the AR. It refers to the construction of virtual objects in a similar way to replicate the original building [23]. Many cultural heritage applications require to reconstruct of real-world objects and scenes. Reconstruction process becomes increasingly common to use for modelling purpose of cultural heritage. This is fundamentally because of rapid development in laser-scanning techniques, 3D modelling, image-based modelling techniques, the power of the computer and virtual reality. The default objects appear on an appropriate model that covers the details of accurate enough is essential [23]. Objects must be exactly identical to the original ones which visitors can see clearly at the background of live videos. In addition, interest in objects’ shadows is an essential part of the reconstruction process. Real-time shadows are created in relation to the sun position in a specified location, date and time. Eventually, the influence of the sky lighting on the virtual building during the daytime is the last part of creating the realistic virtual heritage in AR systems. Most virtual reconstruction techniques are based mainly on 3D scanning techniques, in order to get the objects faithfully [34]. Figure 5 shows the reconstruction of the building and place it in the real environment.
\nRealistic reconstruction of cultural heritage.
Tracking is a substantial subject in a real-time augmented reality context. The key requirements for tracking are the high level of accuracy and low level of latency at a sensible cost. Objects’ tracking in the scene is defined as the amount of the pose between the camera and the objects. Virtual objects can be displayed into the scene using the pose. A local moving edges tracker have been used to provide real-time tracking of points normal to the object contours [27].
\nA new method for conception of vision-based augmented reality systems is presented by considering either 3D model-based tracking techniques or 3D model-free tracking approaches [1]. The method depends on decreasing the cost function expressed in the image and this decreasing is achieved via a visual serving control law. The main feature of a model-based method is that the information about the scene allows improvement of robustness and system’ performance by the ability for predicting hidden movement of the object and acts in order to reduce the effects of outlier data introduced in the tracking process [35]. It is occasionally necessary to achieve the pose computation with minimal constraining information on the viewed scene because 3D information is not readily available in certain circumstances. The algorithm has been tested on different image sequences and for diverse applications, which illustrate a real usability of this approach [1]. This research has several limitations. The first limitation is the system that relies on a course manual initialization on the very first image. The second limitation is the system that does not take spatiotemporal aspect of the tracking process in depth consideration. Robustness can also be treated from one time-step to another. A novel marker-less camera tracking system and user interaction methodology for augmented reality (AR) on unprepared table-top environments is presented by Lee et al. [36]. A real-time system architecture is presented to merge two kinds of feature tracking. Marker-less tracking method is initialised by a simple hand gesture using the Handy AR system that used to estimate a camera pose from a user’s outstretched hand. Detecting distinctive image features of the scene and tracking frame-to-frame by computing optical flow. The proposed system used distinctive image features for recognising the scene and to correct for accumulated tracking errors. For achieving real-time performance, multiple operations are processed in a synchronised multithreaded method: capturing a video frame, tracking features using optical flow, detecting distinctive invariant features and rendering an output frame. The speed and accuracy of hybrid feature tracking system have been evaluate and demonstrate a proof-of-concept application to enable AR in unprepared table-top environments, by using bare hands for interaction [36]. One of the significant limitation of this research is the system applied on 2D scene.
\nNovel interactive techniques for outdoor augmentation have presented to use a mobile device [37]. The system can be executed and perform real time on simple mini PC equipment. Feature tracking have been used for estimating camera motion when user turns the mobile device and examines the augmented scene. The authors have considered two scenarios. The first scenario is constantly applicable with any 3D model for ad hoc use without prior information or calibration process. The second scenario uses GPS for realising the viewing location and Google Earth KML files for locating the augmented object and its placement. This method, 3D object placed on Google Earth can be viewed on site without any addition data transformation steps. The systems have been tested with potential end users. The authors believe that the system is useful in diverse current real-life applications [35].
\nA model-based hybrid tracking system is proposed for outdoor AR applied for urban environments that allows accurate, real-time overlays for a handheld device [38]. The system merges different well-known techniques in order to provide a powerful experience that surpasses each of the individual components alone: an edge-based tracker that used for accurate localisation, gyroscope measurements to cope with fast motions, gravity measurements and magnetic field to avert drift and a rear store of reference frames with online frame chosen used to re-initialise automatically after dynamic occlusions or failures. A novel edge-based tracker distributes with the traditional edge model, and uses instead of a coarse, but textured, 3D model. This technique has several features [39]. The first feature is automatically disposing from scale-based detail, appearance-based edge signatures can be used to improve conformity and the models required are more usually available. The second feature is the system’s accuracy and robustness is pretending with comparisons to map-based ground truth data. The tracking system have the possibility to apply to other types of display such as head mounted displays using video see-through overlays, while optical see-through displays would demand further calibration of the HMD’s virtual camera with take in account of the video camera [38]. This system has the resulting asymmetry in the information display capabilities of the two environments (virtual and real-time environments). An integral natural feature-based tracking system is proposed to support the creation of AR applications that concentrated on the automotive sector [40]. An AR application was construct on top of the system to refer to the location of 3D coordinates in a specific environment. It can be applied to many various applications in cars, for example, a maintenance assistant, an intelligent manual and many more. The system is evaluated during the Volkswagen/ISMAR Tracking Challenge 2014, which designed to test state-of-the-art tracking technique based on requirements encountered in automotive industrial settings. Evaluation results illustrate that the system allowed users to correctly determine tasks points that involved tracking a revolving vehicle, tracking data on an integral vehicle and tracking with high accuracy. The evaluation of the system is allowed to understand the applicability boundaries of texture-based technique in the texture less automotive environment, a problem not addressed considerably in the literature [40]. This research has several limitations. The first limitation is low frame rate when the number of 3D key-points in the model is large. The second limitation is error accumulation when the entire vehicle is reconstructed in a single take. The third limitation is lack of temporal continuity, which may result for shivering; sensibility to extreme illumination conditions. The fourth limitation is accidental failures when cope with scenes that have minimum of texture information.
\nAR technologies have become increasingly popular. These techniques are not just practical for developers of AR system, but also to the scientific community. The standard approach to create a 3D model is to build it from scratch using tools such as the unity 3D programme, which provides building blocks in the form of primitive 3D shapes. Many new technologies aim to increase the level of automation and realism by beginning with the real images of the object or converting it to direct digitisation using a laser scanner [28].
\nThis technology includes vastly available devices, so the same system can handle a wide range of objects and scenes. These systems have the ability to create a realistic model, and those rely on photogrammetry have high geometric accuracy. This technique is usually used for geometric surfaces of architecture objects or for modelling precise terrain. It uses a mathematical model to capture 3D object information from 2D image dimensions or obtain 3D data using methods such as shading, texture, theory, contour and 2D edge gradient [41]. Deriving 3D measurements from images naturally requires that interest points be appearance in the image. Often, this is not potential, either because the area is hidden or covered behind an object or surface or because there is no mark, edge or visible feature to extract [28]. The main goal of image-based reconstruction is the ability to represent arbitrary geometry. For modelling complete geometric structures, it is usually necessary to remove the labour-intensive task through this approach [41]. The mechanism can also deal with the real-world effects that images take, but difficult to reproduce with the customary graphics techniques.
\n3D geometry information for an object can be captured directly by this technique [28]. The 3D measurement of images requires that interest points or edges be visible in the image, which is not constantly possible. Illumination or ambient light problems can impact the extraction process of such points and edges. Active sensors, for example, laser scanners have the ability to avert these restrictions by creating features on the surface using controlled light projection [41]. Many range sensors are produced organised points, in the form of an array or range image, appropriate for automatic modelling. However, texture information or colour can be attached from the scanner using colour channel or from separate digital camera [42, 43]. High-resolution colour textures that obtained from separate digital camera help to create of realistic 3D models. Generally, a single range image is insufficient to cover any object or structure [42]. The amount of necessary images rely on the shape of the object, the amount of self-locking and obstacles and the size of the object compared to the sensor range [41]. In order to wrap each aspect of the object, it is mostly required to perform multiple scans from various locations, which is commensurate to the size and shape of the object and occlusions. The alignment and groups of the various scans can affect the final accuracy of the 3D model, where each scanner has different range of resolution [28]. In addition, this technique can provide accurate and complete details with a high degree of automation for small and medium size objects, which reach the human size [42]. There are two major kinds of range sensors: triangular based and based on the principle of flight time [41]. Triangulation-based sensors are working dependent on project light in a known direction from a known position, as well as measure the direction of the returning light through its detected position. The measurement of accuracy depends on the triangle base relative to its height. Sensors based on the principle of flight time measured the delay between emitting and detecting reflected light on the surface, thus, accuracy does not quickly deteriorate as the range increases [44]. Time-of-flight sensors have the possibility to provide measurements in the kilometre range.
\nImage-based rendering used images as modelling and rendering primitives [28]. Image-based rendering uses images directly for creating new views for rendering without explicit geometrical representation. This technique is a significant mechanism for generating of virtual view, where certain objects and under particular camera motions and scene conditions. From the image input, this technique creates a new view of the 3D environment [41]. This technique has the feature of creating realistic virtual environments at speeds independent of scene complexity [42]. Image-based rendering depends on accurately knowing the camera positions to use automatic stereo matching, where the absence of geometry data, requires a major number of carefully spaced images to succeed [42]. Most of image-based rendering correspond to hybrids image-geometry, using means of the equal amount of geometry ranging from per-pixel depth to hundreds of polygons [45].
\nEach of indoor and outdoor sites offered many of similar challenges that must be processed to successfully implement AR systems, such as content acquisition [11], content storage and categorisation [46], tracking and calibration [47], marker placement, usability [48] and ergonomic issues [49]. Hence, there are various issues that must be taken into account in order to overcome by special internal or external sites.
\nAR’s previous applications for indoor cultural heritage sites have frequently taken the form of “virtual museums”. The visitors use AR technology to display objects that may not be accessible to them. This is because the great value or fragility of such objects, or the lack of space inside the museum or the physical object is existing in another museum [36]. One of the main issues that affect the design of AR systems for indoor sites are those of marker placement if using marker-based tracking, as well as ensuring the optimal use of the systems for all age groups and levels of computer literacy. In addition, it is substantial to make sure that hardware used is strong enough in order to support AR applications, and it is structurally robust if being lent to the public.
\nIt can be said that the development of AR systems for outdoor applications is more difficult than indoor applications. Realistic historical buildings in outdoor rendering AR systems require advanced effects such as shadows, lighting and the ability to detect the impact of sky dome illumination on virtual in addition to the real objects [23]. The environment and resources, such as lighting conditions and electrical energy, cannot be as tightly controlled, as well as hardware cannot normally be left outdoors. The use of mobile computer systems in outdoor AR generates several problems such as it is uncomfortable and heavy to wear, and it is very expensive if it is a wearable system combined with an HMD [11] Outdoor AR is a technology of executing augmented reality using outdoor GPS, compass, gyroscope sensor based on augmented reality technology. Unlike to indoor AR, outdoor AR is not subject to spatial restrictions. Indoor AR used a marker to ensure suitable synthesis of virtual object because it happens in relative narrow space, while outdoor AR used location information; it does not use any marker like in indoor system because it happens in relatively wide area [50]. Often the lack of ideal conditions means that marker-based tracking systems cannot be used, leading to rely on other techniques, for example, GPS and inertial sensors, which can be inexact.
\nOne of the key problems that faced to design AR systems for outdoor sites are effectively tracking without using of markers in an environment that may be devoid of features in order to use for tracking. In addition, ensuring that any device used is weather-resistant and vandal-resistant. Furthermore, all the hard-wires that are used must be powerful enough to support AR applications, as with indoor sites. Figure 6 shows the AR for outdoor cultural heritageTable 1 shows a comparison between investigated works in AR systems, concentrated on outdoor, indoor, reconstruction and realism.
\nAugmented reality for outdoor heritage.
Researcher | \nArea | \nIndoor | \nOutdoor | \n3-D reconstruction | \nRealistic VR | \n
---|---|---|---|---|---|
Sudirman and El-Rhalibi [16] | \nCamera pose estimation | \nX | \n\n | \n | \n |
Yuan [17] | \n3D camera pose | \nX | \n\n | X | \n\n |
Frikha et al. [18] | \nCamera pose estimation | \nX | \n\n | X | \nX | \n
HoK llerer et al. [20] | \nmobile augmented reality system | \nX | \nX | \nX | \n\n |
Newman et al. [21] | \nAR wide-area sentient | \nX | \n\n | \n | \n |
Jacquemin et al. [22] | \nMobile AR | \n\n | X | \n\n | X | \n
Fukuda et al. [24] | \nAR registration | \n\n | X | \nX | \nX | \n
Chen et al. [25] | \nAR tracking | \nX | \n\n | \n | X | \n
Lee and Höllerer [36] | \nAR tracking | \nX | \n\n | X | \nX | \n
Honkamaa et al. [37] | \nAR tracking | \n\n | X | \nX | \nX | \n
Reitmayr and Drummond [38] | \nAR tracking | \n\n | X | \nX | \nX | \n
Lima et al. [40] | \nAR tracking | \nX | \n\n | X | \nX | \n
Pang et al. [33] | \nAR registration | \nX | \n\n | X | \n\n |
Hanisch et al. [26] | \nVR reconstruction | \n\n | \n | X | \n\n |
Andrés et al. [27] | \nVR generation | \n\n | X | \nX | \nX | \n
El-Hakim and Beraldin [28] | \nVR reconstruction | \n\n | X | \nX | \nX | \n
Zoellner et al. [29] | \nCultural heritage layers | \n\n | X | \nX | \nX | \n
Bres and Tellez [30] | \nMobile applications in culture heritage | \n\n | X | \n\n | X | \n
Galmangoda et al. [31] | \nVR reconstruction | \n\n | X | \nX | \nX | \n
Purnami and Putri [32] | \nVR reconstruction | \nX | \n\n | X | \n\n |
Kolivand and El Rhalibi [23] | \nRealistic virtual heritage | \n\n | X | \nX | \nX | \n
A full comparison of different techniques for marker-less augmented reality.
This chapter has presented the survey of marker-less augmented reality system. In this chapter, we have discussed different techniques related to the augmented reality. An overview on each of them was introduced, identifying the major features and highlighting the main characteristic of each technique. In addition, we have explained in detail the main issues with virtual heritage in augmented reality. We have introduced the key techniques for 3D reconstruction that used in cultural heritage. We have focussed on the main issues of augmented reality for cultural heritage such as indoor marker-less AR, outdoor marker-less AR, real-time solutions to the tracking problem, real-time registration and cultural heritage in AR. We have presented the research related to these areas and highlighted the main problem of each research.
\nThe financial support of this research is provided by KE & I fund from Liverpool John Moores University.
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