Enhanced Virtual Transparency in Handheld AR: Digital Magnifying Glass

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1 Enhanced Virtual Transparency in Handheld AR: Digital Magnifying Glass Klen Čopič Pucihar School of Computing and Communications Lancaster University Lancaster, UK LA1 4YW Paul Coulton Imagination, Lancaster Institute for the Contemporary Arts Lancaster University Lancaster, UK LA1 4YW Abstract Handheld Augmented Reality (AR) is often presented using the magic-lens paradigm in which a magic-lens is a transparent interface. Such transparency is usually implemented by rendering camera captured video on the device s screen. The transparency quality is limited by the video stream quality which may be affected by: unfocused camera lens, poor lighting conditions and limited video stream resolution. All these factors may reduce the readability of the AR scene. To address quality of rendering and increase scene readability, this paper presents an enhanced virtual transparency solution where segments of the scene are replaced by high definition digital content. The proposed enhanced virtual transparency is demonstrated through the design of a digital magnifying glass which has been implemented on of-the-shelf mobile phone. Author Keywords Virtual transparency; Magnifying Glass; Handheld; ACM Classification Keywords H5.1. Information interfaces and presentation (e.g., HCI): Artificial, augmented, and virtual realities. Copyright is held by the author/owner(s). MobileHCI 2013, Aug 27 30, 2013, Munich, Germany. ACM /13/08. Introduction Handheld Augmented Reality (AR) is often introduced to users through the magic-lens paradigm where a magic-lens takes a form of a transparent interface

2 revealing an enhanced view of the scene lying behind the lens. However, the majority of handheld devices are not transparent, thus creating the need to artificially represent this transparency. Such virtual transparency is typically implemented utilizing the back-facing camera with the captured video rendered on the device s screen. The quality of camera utilized transparency (see Figure 1a) is dependent on the quality of the captured camera video stream which is affected by: (1) unfocused camera lens resulting in image blur; (2) lighting conditions affecting imagery contrast, brightness and illumination; (3) camera video stream resolution commonly limited in order to reduce computational cost and achieve optimal tracking performance [3, 6]. (a) The format and lens characteristics of phone cameras define hyper-focal distance1, commonly ranging between 80 and 120 centimeters. Focusing camera lens at a hyper-focal distance results in points beyond to be in focus. Consequently, image blur problem is only present when operating at distances smaller than hyper-focal distance. However, in table-top sized environments interactions at distances smaller than hyper-focal distance are commonly expected, thus the effect of image blur is likely to degrade image quality. Although auto-focusing is a potential solution, it introduces additional computational cost and requires user s cooperation in holding the phone still during the time of autofocus operation thus it may still ultimately fail. (b) Figure 1. Phone screen-shot (no image editing) demonstrating scene readability problem. (a) Showing camera utilized virtual transparency affected by image blur, brightness, contrast and limited video stream resolution (640x480 pixels). (b) Enhanced virtual transparency with optimal contrast, brightness and rendering resolution (R 1). 1 Minimal object distance at which on needs to focus in order to Similarly, lighting conditions also deteriorate as the magic-lens moves closer to the scene. This is caused by the device occluding the light sources, and is especially significant when using larger tablet-sized devices. Although a flash is readily available on most mobile phones, such directional light source is not considered as appropriate. Directional light introduces reflections and variation in illumination, consequently affecting video quality and tracking performance [6]. To achieve optimal camera tracking performance, the resolution of the video stream is commonly limited. This can be addressed by using different video stream resolution for rendering and camera pose tracking. However, yet again, such functionality comes at additional computational cost. There are many use-cases that could benefit from improved magic-lens rendering quality. One such area is handheld AR on printed media. In printed media a large format and high resolution come at affordable prices, however, once printed, the information can not be changed and is limited to showing static visual content. This limitation can be overcome using handheld AR for use cases such as: interactive paper where digital content is merged with printed media [2, 4]. However, usability of such a system may be affected by poor scene readability, a consequence of small font size, often encountered in printed media (i.e. printed maps), and poor rendering quality caused by camera utilized virtual transparency. In this paper we propose enhanced virtual transparency which can in certain applications improve magic-lens rendering quality by eliminating the problem of blur, contrast, brightness and illumination. Further, it also overcomes the problem of limited video resolution as it uses predefined textures that enable optimal resolution rendering. To demonstrate

3 enhanced virtual transparency a digital magnifying glass is designed and implemented on a commercially available handheld device. Enhanced Virtual Transparency The proposed enhanced virtual transparency solution for handheld AR is based on using digital content to replace parts of the camera image, approach similar to non-vision based AR systems [5]. The solution is primarily aimed at planar table-top sized environments where the scene is reconstructed using a predefined high pixel-density texture. By knowing camera position, the texture can be superimposed onto the base-plane of an augmented scene (i.e. the map) whilst continuing to render the live video stream. When looking at predefined scene area the magic-lens render is able to show a high-quality image, with optimal sharpness, contrast, brightness, illumination and resolution (see Figure 1b. However, this comes at the cost of limiting the scene to static environments, resulting in occluding any dynamic objects introduced to the scene (i.e. hand interacting within the augmented scene). Figure 2. Digital magnifying glass prototype augmenting pink pin icons to mark state capitals on a printed map. Rendering-ratio: Rendering vs. Screen Resolution Figure 3. Graph showing resolutionratio (R=rendering resolution/ screen resolution) for phone-sized (HTC Sensation) and tablet-sized (Motorola XOOM) displays. When implementing virtual transparency one of the factors influencing the quality of the rendering is the resolution of the camera captured video stream. In pursuit of optimal camera pose tracking performance the resolution is often limited, in our case where commercial library Vuforia2 is used for camera pose tracking to 640x480px. This leads to lower rendering quality than can actually be displayed on the screen. 2 Here we define rendering-ratio (R) as a proportion between the maximal possible rendering resolution and screen resolution. In case of camera utilized virtual transparency rendering-ratio (R) bellow 1 is expected (on Figure 3). In contrast to camera utilized virtual transparency where the resolution-ratio (R) is constant, in enhanced virtual transparency, the resolution-ratio (R) changes with magic-lens distance (d ) (see Figure 3). This is due to the fact that the area of the scene that needs to be rendered on magic-lens (w) changes with magic-lens distance (d ), whereas texture pixel-density (kdpi) remains constant (see Figure 4). By utilizing Figure 3 it is easy to identify in which magic-lens distance regions (d ) the proposed enhanced virtual transparency results in higher resolution ratio when compared to camera utilized virtual transparency. In case of 300 dpi images and camera capture resolution of 640x480, higher resolution is achieved at magic-lens distances bigger than ~5 centimeters (for tablet-size lens at ~8 centimeters) and optimal resolution-ratio (R=1) is reached at approximately ~8 centimeters (for tabletsize lens at ~13 centimeters). Designing Digital Magnifying Glass One potential problem that may arise is handheld AR interaction with printed media is scene readability. To improve readability of the scene a magic-lens view replicating a magnifying glass is presented (see Figure 2). In the case of traditional transparency this magnification can only be achieved by stretching the camera captured image thus showing the same amount of detail but on a larger area of the screen. In the enhanced transparency, presented in this paper,

4 Figure 4. Demonstrates how the area of camera-captured scene (w) changes with magic-lens distance (d ). The figure also defines rendering-ration (R) as proportion between camera-captured area (w) and screen resolution (Wscreen) which is defined by magic-lens distance (d ), pixel-density kdpi and camera field-ofview ( α ). the magnification can be achieved by rendering the view closer to the magic lens. Consequently, in regions where higher texture detail is available (see Figure 3 where R>1), the magic lens shows higher detail compared to the original view. A decision to show magnification view as only part of the magic-lens render was made as it allows participants to keep the magnified view in wider context which is in line with previous research exploring large image navigation on small displays, where context was found to significantly improve system usability [1]. Implementing Digital Magnifying Glass The digital magnifying glass prototype is implemented on the paper map texture showing the map of the world. In both transparency modes the original magiclens view is stretched to achieve the magnification effect. However, due to higher resolution of the original view in case of enhanced virtual transparency its magnified view shows higher detail respectively. The resolution of the texture being overlaid onto the base plane is limited by available application memory, in our case to 128 MB. To allow easier memory allocation a decision to segment map texture into 96 tiles was made. Each tile holds 512x512 pixels of 3 channel 8bit depth colour information, thus the texture inhibits ~76 MB of memory space. Dependent on the use case the enhanced virtual transparency could significantly improve rendering at smaller texture resolutions. Previously presented rendering-ration equation (see Figure 4) could be used to support decision-making on optimal texture resolution. Conclusion and Future Work As is demonstrated by the Video, poor quality of rendering may affect scene readability and usability of handheld AR interfaces when operated in conjunction with printed media. The enhanced transparency is a possible solution. As demonstrated by digital magnifying glass, the rendering quality improvement is obvious. However, running a user study to confirm the proposed enhanced virtual transparency significantly improves scene readability is required. Additionally, the scene is limited to static environments. This removes the ability to render the hand interacting within the augmented scene, therefore, research into ways of reintroducing the hand should be considered. Acknowledgements Authors would like to thank Slovene Human Resources Development and Scholarship Fund for PhD funding that made carrying this work out possible. References [1] Baudisch, P., Good, N., Bellotti, V., and Schraedley, P. Keeping Things in Context : A Comparative Evaluation of Focus Plus Context Screens, Overviews, and Zooming, In SIGCHI 02, pp. [2] Henze, N., Boll, S. Who s That Girl? Handheld Augmented Reality for Printed Photo Books, In INTERACT 11, pp [3] Kurz, D. and Benhimane, S. Gravity-Aware Handheld Augmented Reality, In ISMAR 11. [4] Liao, C., Liu, Q., Liew, B., and Wilcox, L. Pacer: fine-grained interactive paper via camera-touch hybrid gestures on a cell phone, In SIGCHI 10. [5] Sanneblad, J., and Holmquist, L. E. Ubiquitous graphics: combining hand-held and wall-size displays to interact with large images, AVI 06. [6] Wagner, D., Schmalstieg, D., and Bischof, H., Multiple target detection and tracking with guaranteed framerates on mobile phones, In ISMAR 09

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