VICIOS JURÍDICOS EN LA EMISIÓN DEL TÍTULO DE CRÉDITO

True augmented reality must provide enhanced information to the users’ senses which they are then able to interact with. Many systems exist which state they are AR systems, but are really only presenting data in novel ways [104-106]. Direct interaction of the sense is not necessary; interaction with the environment, which in-turn presents new sensory data, is appropriate. The chemical experiment found in [106] could be considered lacking in substance. While manipulating chemical structures, via fiducial markers, provides students with a novel way of viewing 3D models, does it provide enhanced information? To engage visual and tactile sensors, the user could have been 'handling' atoms to create molecules, with a sense of heat to reflect endo- or exo-thermic reactions. The published method is little different to using a mouse to scroll a 3D structure, so offers little value other than a demonstration of the technology. For example, data presented to military pilots through the Head’s Up Display (HUD), is a visual representation of information not previously sensed [107], such as target information. The pilot is able to move the aircraft (interact with the environment) to successfully engage a target (modify the visual information). Overall, the system provides creates a new sensory system which the pilot is immersed within. The purpose of the HUD has given the military confidence in AR technology, and produced further advances, reflected in today’s F-35 fighter jet which relies on a helmet-mounted HUD [108] for significant pilot interaction with their aircraft and environment. Today, AR continues to grow in areas as diverse as manufacturing [109] where AR reduces training and technician errors, medical [42] practices providing x-ray vision for simple procedures, and entertainment [110] through AR games.

2.2.1 Industry AR Developments

Uptake of AR within the manufacturing and construction industries has produced cost benefits by improving worker efficiency and reducing human errors. For both industries, the servicing and maintenance of equipment has improved through AR support [111, 112]. Beginning with VR to create walk-throughs of complex environments [113], AR has evolved to allow maintenance staff to service or repair

equipment after a significant reduction in the number of training hours [114]. With a head mounted display (HMD), technicians are able to locate key aspects of an assembly; where they may be prompted to perform a series of steps to operate the equipment [44], or to connect or complete the assembly with secondary components [109, 115].

Apart from aircraft HUD usage, the military has also assessed AR as a battlefield assistant [116]. Soldiers on the ground wearing HMD’s, are given greater situation awareness, with visual feedback of critical tactical knowledge such as routes, enemy positions and strength, and real-time updates. Soldier interaction with the system ensures well-coordinated and well communicated goals are achieved while increasing survivability. Interpreting large real-time data sets used for medical diagnosis are also improved through AR. Surgical procedures such as guided needle biopsies [42] or laparoscopy [117] supply visual representations to the surgeon, of the region of interest and their actions as if they has x-ray vision.

2.2.2 Gaming AR

Opportunities for AR within the gaming community have interesting and varied implementations. Conceptual development of the popular game Quake [110] demonstrated how gaming and AR can be taken out of darkened rooms and into the real world. Using HMD’s and backpack computing, the real world becomes the playing arena. Pokémon Go! [38] generated wide-spread enthusiasm, and its implementation required only minimal AR methods utilising very little computer vision processing. A novel approach to game creation involves utilising fiducial markers representing objects within the game space [118]. The players steer vehicles using a controller covered with fiducial markers. Detecting the motion and pose of the steering markers directs the vehicle through the user create obstacle course. The number of configurations is enormous, allowing unlimited game play. New hardware systems such as Microsoft’s Kinect 3D [55] depth sensing camera offers new resources to further promote AR gaming.

2.2.3 AR in Education

As this work has previously stated: there is little research available on AR for RAL, and only a handful of systems such as the very clever implementation shown in Figure 1-3 by Andujar et al[52]. The use of AR within education has really only been

demonstrated at conception level, with little work to define and construct working toolsets. Manipulating three-dimensional objects through the movement of fiducial markers has been common, such as with the magic lens [119] venture, but offers very little in actual course content delivery.

To avoid the initial pitfalls of RAL, the pedagogical value of AR needs to be understood, instead of creating AR environments for the sake of the technology. Salmi et al. [120] and Lee [121] have created an understanding and measures necessary for AR to progress and improve the delivery of course content. While the conclusions highlight the difficulty of AR (which is the difficulty applied to AR in every field) the value was also recognised. Some research suggests that AR in learning environments might provide the impetuous to re-conceptualise some of the key concepts in education, such as context, engagement and authenticity [122]. Studying the factors that affect content delivery and the effectiveness of AR in the learning environment has been called for and is an area of intense review [50].

2.2.4 Other AR Sensory Systems

Visual AR systems are the dominant method of interfacing our senses, but our other senses have had successful research too. Haptic interfaces, which provide feedback on touch [54], or the sense of temperature [57], or the application of force [56], help to improve the user experience and perception of immersion; however, aural feedback has not been a studied feature of current AR systems. Within a remote laboratory environment, sound is presented to the student as a consequence of the video streaming. Taking advantage of audio cue’s or simulating the sounds of remote apparatus such as the intravenous infusion pump [76] provide an improved sense of presence with the device.

2.2.5 Object Tracking Systems

Object tracking within AR systems relies on one or several Computer Vision techniques. Creating an understanding of the video scene is a non-trivial exercise, requiring complex analysis of each and every frame arriving to the CV/AR processing systems. Locating key reference points is vital for active registration. Unless the AR system knows the location of strategic landmarks, real and virtual objects will not properly align [41], creating confusion and loss of user immersion. Proper alignment of real and virtual visual objects is the critical problem to solve for AR systems [123].

Computer vision image processes such as edge detection and corner point or feature point detection are used to locate and isolate important landmarks.

Detecting and isolating feature points, whether as part of an object or reference point requires comprehensive image processing. Corner and Edge detection models are the framework for the majority of CV processes. Baseline models such as Moravec [17] and Harris [124] have been consistently used because of their simplicity and effectiveness. Interpretations of pixel intensities or energy levels are the focal for many CV image processes. Energy levels are used by Moravec: see Equation 2-1, in which energy (𝐸) is summed in a floating window (𝑤).

Moving the floating window creates an energy response such that a central pixel will generate an energy map minimal when placed at a corner.

2.2.6 Markerless Tracking

Object tracking without the use of fiducial markers, in real-time processing, has been consistently difficult to achieve. Achieving spatial relationships based solely on discovered natural markers becomes challenging. Pre-training or learning features within the video scene provides a means to gain an understanding of the environment prior to operational actions [6, 44, 125]. Markerless tracking without fiducial markers and without prior knowledge of the environment is not a normal consideration. Novel methods to ascertain camera pose and reference points have considered planar structure within the scene [126], structure from motion [127, 128] to build a three-dimensional model, and identifying natural features [129].