TY - RPRT A1 - Amano, Toshiyuki A1 - Bimber, Oliver A1 - Grundhöfer, Anselm T1 - Appearance Enhancement for Visually Impaired with Projector Camera Feedback N2 - Visually impaired is a common problem for human life in the world wide. The projector-based AR technique has ability to change appearance of real object, and it can help to improve visibility for visually impaired. We propose a new framework for the appearance enhancement with the projector camera system that employed model predictive controller. This framework enables arbitrary image processing such as photo-retouch software in the real world and it helps to improve visibility for visually impaired. In this article, we show the appearance enhancement result of Peli's method and Wolffshon's method for the low vision, Jefferson's method for color vision deficiencies. Through experiment results, the potential of our method to enhance the appearance for visually impaired was confirmed as same as appearance enhancement for the digital image and television viewing. KW - Maschinelles Sehen KW - Projector Camera System KW - Model Predictive Control KW - Visually Impaired Y1 - 2010 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20100106-14974 ER - TY - RPRT A1 - Exner, David A1 - Bruns, Erich A1 - Kurz, Daniel A1 - Grundhöfer, Anselm A1 - Bimber, Oliver T1 - Fast and Reliable CAMShift Tracking N2 - CAMShift is a well-established and fundamental algorithm for kernel-based visual object tracking. While it performs well with objects that have a simple and constant appearance, it is not robust in more complex cases. As it solely relies on back projected probabilities it can fail in cases when the object's appearance changes (e.g. due to object or camera movement, or due to lighting changes), when similarly colored objects have to be re-detected or when they cross their trajectories. We propose extensions to CAMShift that address and resolve all of these problems. They allow the accumulation of multiple histograms to model more complex object appearance and the continuous monitoring of object identi- ties to handle ambiguous cases of partial or full occlusion. Most steps of our method are carried out on the GPU for achieving real-time tracking of multiple targets simultaneously. We explain an ecient GPU implementations of histogram generation, probability back projection, im- age moments computations, and histogram intersection. All of these techniques make full use of a GPU's high parallelization. KW - Bildverarbeitung KW - CAMShift KW - Kernel-Based Tracking KW - GPU Programming KW - CAMShift KW - Kernel-Based Tracking KW - GPU Programming Y1 - 2009 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20091217-14962 ER - TY - RPRT A1 - Grundhöfer, Anselm A1 - Bimber, Oliver T1 - Dynamic Bluescreens N2 - Blue screens and chroma keying technology are essential for digital video composition. Professional studios apply tracking technology to record the camera path for perspective augmentations of the original video footage. Although this technology is well established, it does not offer a great deal of flexibility. For shootings at non-studio sets, physical blue screens might have to be installed, or parts have to be recorded in a studio separately. We present a simple and flexible way of projecting corrected keying colors onto arbitrary diffuse surfaces using synchronized projectors and radiometric compensation. Thereby, the reflectance of the underlying real surface is neutralized. A temporal multiplexing between projection and flash illumination allows capturing the fully lit scene, while still being able to key the foreground objects. In addition, we embed spatial codes into the projected key image to enable the tracking of the camera. Furthermore, the reconstruction of the scene geometry is implicitly supported. KW - Association for Computing Machinery / Special Interest Group on Graphics KW - CGI KW - Maschinelles Sehen KW - Farbstanzen KW - Erweiterte Realität KW - Projektion KW - Chroma Keying KW - Bildmischung KW - Augmented Reality KW - Projection KW - Chromakeying KW - Compositing Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20080226-13016 ER - TY - RPRT A1 - Bimber, Oliver A1 - Iwai, Daisuke T1 - Superimposing Dynamic Range N2 - We present a simple and cost-efficient way of extending contrast, perceived tonal resolution, and the color space of static hardcopy images, beyond the capabilities of hardcopy devices or low-dynamic range displays alone. A calibrated projector-camera system is applied for automatic registration, scanning and superimposition of hardcopies. We explain how high-dynamic range content can be split for linear devices with different capabilities, how luminance quantization can be optimized with respect to the non-linear response of the human visual system as well as for the discrete nature of the applied modulation devices; and how inverse tone-mapping can be adapted in case only untreated hardcopies and softcopies (such as regular photographs) are available. We believe that our approach has the potential to complement hardcopy-based technologies, such as X-ray prints for filmless imaging, in domains that operate with high quality static image content, like radiology and other medical fields, or astronomy. KW - Bildverarbeitung KW - CGI KW - Computergraphik KW - Kontrast KW - Projektor-Kamera Systeme KW - Hoher Dynamikumfang KW - Contrast KW - Projector-Camera Systems KW - High Dynamic Range Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20080422-13585 ER - TY - RPRT A1 - Grosse, Max A1 - Bimber, Oliver T1 - Coded Aperture Projection N2 - In computer vision, optical defocus is often described as convolution with a filter kernel that corresponds to an image of the aperture being used by the imaging device. The degree of defocus correlates to the scale of the kernel. Convolving an image with the inverse aperture kernel will digitally sharpen the image and consequently compensate optical defocus. This is referred to as deconvolution or inverse filtering. In frequency domain, the reciprocal of the filter kernel is its inverse, and deconvolution reduces to a division. Low magnitudes in the Fourier transform of the aperture image, however, lead to intensity values in spatial domain that exceed the displayable range. Therefore, the corresponding frequencies are not considered, which then results in visible ringing artifacts in the final projection. This is the main limitation of previous approaches, since in frequency domain the Gaussian PSF of spherical apertures does contain a large fraction of low Fourier magnitudes. Applying only small kernel scales will reduce the number of low Fourier magnitudes (and consequently the ringing artifacts) -- but will also lead only to minor focus improvements. To overcome this problem, we apply a coded aperture whose Fourier transform has less low magnitudes initially. Consequently, more frequencies are retained and more image details are reconstructed. KW - Association for Computing Machinery / Special Interest Group on Graphics KW - CGI KW - Projektion KW - Blende Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20080227-13020 ER - TY - RPRT A1 - Bimber, Oliver T1 - Superimposing Dynamic Range N2 - Replacing a uniform illumination by a high-frequent illumination enhances the contrast of observed and captured images. We modulate spatially and temporally multiplexed (projected) light with reflective or transmissive matter to achieve high dynamic range visualizations of radiological images on printed paper or ePaper, and to boost the optical contrast of images viewed or imaged with light microscopes. KW - Bildverarbeitung KW - CGI KW - Computergraphik KW - Kontrast KW - Projektor-Kamera Systeme KW - Hoher Dynamikumfang KW - Mikroskopie KW - Contrast KW - Projector-Camera Systems KW - High Dynamic Range KW - Microscopy Y1 - 2008 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20090303-14662 ER - TY - RPRT A1 - Kurz, Daniel A1 - Häntsch, Ferry A1 - Grosse, Max A1 - Schiewe, Alexander A1 - Bimber, Oliver T1 - Laser Pointer Tracking in Projector-Augmented Architectural Environments N2 - We present a system that applies a custom-built pan-tilt-zoom camera for laser-pointer tracking in arbitrary real environments. Once placed in a building environment, it carries out a fully automatic self-registration, registrations of projectors, and sampling of surface parameters, such as geometry and reflectivity. After these steps, it can be used for tracking a laser spot on the surface as well as an LED marker in 3D space, using inter-playing fisheye context and controllable detail cameras. The captured surface information can be used for masking out areas that are critical to laser-pointer tracking, and for guiding geometric and radiometric image correction techniques that enable a projector-based augmentation on arbitrary surfaces. We describe a distributed software framework that couples laser-pointer tracking for interaction, projector-based AR as well as video see-through AR for visualizations with the domain specific functionality of existing desktop tools for architectural planning, simulation and building surveying. KW - Association for Computing Machinery / Special Interest Group on Graphics KW - CGI KW - Architektur KW - Maschinelles Sehen KW - Laserpointer Tracking KW - Erweiterte Realität KW - Interaktion KW - Projektion KW - Verteilte Systeme KW - Laser Pointer Tracking KW - Augmented Reality KW - Interaction KW - Projection KW - Distributed Systems Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-8183 ER - TY - RPRT A1 - Bruns, Erich A1 - Brombach, Benjamin A1 - Bimber, Oliver T1 - Mobile Phone Enabled Museum Guidance with Adaptive Classification N2 - Although audio guides are widely established in many museums, they suffer from several drawbacks compared to state-of-the-art multimedia technologies: First, they provide only audible information to museum visitors, while other forms of media presentation, such as reading text or video could be beneficial for museum guidance tasks. Second, they are not very intuitive. Reference numbers have to be manually keyed in by the visitor before information about the exhibit is provided. These numbers are either displayed on visible tags that are located near the exhibited objects, or are printed in brochures that have to be carried. Third, offering mobile guidance equipment to visitors leads to acquisition and maintenance costs that have to be covered by the museum. With our project PhoneGuide we aim at solving these problems by enabling the application of conventional camera-equipped mobile phones for museum guidance purposes. The advantages are obvious: First, today’s off-the-shelf mobile phones offer a rich pallet of multimedia functionalities ---ranging from audio (over speaker or head-set) and video (graphics, images, movies) to simple tactile feedback (vibration). Second, integrated cameras, improvements in processor performance and more memory space enable supporting advanced computer vision algorithms. Instead of keying in reference numbers, objects can be recognized automatically by taking non-persistent photographs of them. This is more intuitive and saves museum curators from distributing and maintaining a large number of physical (visible or invisible) tags. Together with a few sensor-equipped reference tags only, computer vision based object recognition allows for the classification of single objects; whereas overlapping signal ranges of object-distinct active tags (such as RFID) would prevent the identification of individuals that are grouped closely together. Third, since we assume that museum visitors will be able to use their own devices, the acquisition and maintenance cost for museum-owned devices decreases. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - Anpassung KW - Mobiltelefone KW - Museumsführer KW - Adaptive Klassifizierung KW - Ad-hoc Sensor-Netzwerke KW - mobile phones KW - object recognition KW - museum guidance KW - adaptive classification KW - ad-hoc sensor networks Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-9406 ER - TY - RPRT A1 - Bruns, Erich A1 - Bimber, Oliver T1 - Adaptive Training of Video Sets for Image Recognition on Mobile Phones N2 - We present an enhancement towards adaptive video training for PhoneGuide, a digital museum guidance system for ordinary camera–equipped mobile phones. It enables museum visitors to identify exhibits by capturing photos of them. In this article, a combined solution of object recognition and pervasive tracking is extended to a client–server–system for improving data acquisition and for supporting scale–invariant object recognition. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - Anpassung KW - mobile phones KW - object recognition KW - neural networks KW - museum guidance KW - pervasive tracking KW - temporal adaptation Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-8223 ER - TY - RPRT A1 - Grundhöfer, Anselm A1 - Seeger, Manja A1 - Häntsch, Ferry A1 - Bimber, Oliver T1 - Dynamic Adaptation of Projected Imperceptible Codes N2 - In this paper we present a novel adaptive imperceptible pattern projection technique that considers parameters of human visual perception. A coded image that is invisible for human observers is temporally integrated into the projected image, but can be reconstructed by a synchronized camera. The embedded code is dynamically adjusted on the fly to guarantee its non-perceivability and to adapt it to the current camera pose. Linked with real-time flash keying, for instance, this enables in-shot optical tracking using a dynamic multi-resolution marker technique. A sample prototype is realized that demonstrates the application of our method in the context of augmentations in television studios. KW - Association for Computing Machinery / Special Interest Group on Graphics KW - CGI KW - Maschinelles Sehen KW - Erweiterte Realität KW - Kamera Tracking KW - Projektion KW - Augmented Reality KW - Camera Tracking KW - Projection Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-8168 ER - TY - RPRT A1 - Wetzstein, Gordon A1 - Bimber, Oliver T1 - Radiometric Compensation through Inverse Light Transport N2 - Radiometric compensation techniques allow seamless projections onto complex everyday surfaces. Implemented with projector-camera systems they support the presentation of visual content in situations where projection-optimized screens are not available or not desired - as in museums, historic sites, air-plane cabins, or stage performances. We propose a novel approach that employs the full light transport between a projector and a camera to account for many illumination aspects, such as interreflections, refractions and defocus. Precomputing the inverse light transport in combination with an efficient implementation on the GPU makes the real-time compensation of captured local and global light modulations possible. KW - Association for Computing Machinery / Special Interest Group on Graphics KW - CGI KW - Maschinelles Sehen KW - Projektionssystem KW - radiometrische Kompensation KW - Licht Transport KW - Projector-Camera Systems KW - Radiometric Compensation KW - Inverse Light Transport Y1 - 2007 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-8126 ER - TY - RPRT A1 - Bruns, Erich A1 - Brombach, Benjamin A1 - Zeidler, Thomas A1 - Bimber, Oliver T1 - Enabling Mobile Phones To Support Large-Scale Museum Guidance N2 - We present a museum guidance system called PhoneGuide that uses widespread camera equipped mobile phones for on-device object recognition in combination with pervasive tracking. It provides additional location- and object-aware multimedia content to museum visitors, and is scalable to cover a large number of museum objects. KW - Objektverfolgung KW - Neuronales Netz KW - Handy KW - Objekterkennung KW - Museum KW - mobile phones KW - object recognition KW - neural networks KW - museum guidance KW - pervasive tracking Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-6777 ER - TY - RPRT A1 - Föckler, Paul A1 - Zeidler, Thomas A1 - Bimber, Oliver T1 - PhoneGuide: Museum Guidance Supported by On-Device Object Recognition on Mobile Phones N2 - We present PhoneGuide – an enhanced museum guidance approach that uses camera-equipped mobile phones and on-device object recognition. Our main technical achievement is a simple and light-weight object recognition approach that is realized with single-layer perceptron neuronal networks. In contrast to related systems which perform computational intensive image processing tasks on remote servers, our intention is to carry out all computations directly on the phone. This ensures little or even no network traffic and consequently decreases cost for online times. Our laboratory experiments and field surveys have shown that photographed museum exhibits can be recognized with a probability of over 90%. We have evaluated different feature sets to optimize the recognition rate and performance. Our experiments revealed that normalized color features are most effective for our method. Choosing such a feature set allows recognizing an object below one second on up-to-date phones. The amount of data that is required for differentiating 50 objects from multiple perspectives is less than 6KBytes. KW - Neuronales Netz KW - Objekterkennung KW - Handy KW - Museum KW - Mobile phones KW - object recognition KW - neural networks KW - museum guidance Y1 - 2005 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:gbv:wim2-20111215-6500 ER -