A software framework for the development of projection-based augmented reality systems

Despite the large amount of methods and applications of augmented reality, there is little homogenization on the software platforms that support them. An exception may be the low level control software that is provided by some high profile vendors such as Qualcomm and Metaio. However, these provide fine grain modules for e.g. element tracking. We are more concerned on the application framework, that includes the control of the devices working together for the development of the AR experience. In this paper we present a software framework that can be used for the development of AR applications based on camera-projector pairs, that is suitable for both fixed, and nomadic setups.Peer ReviewedPostprint (author's final draft

Optical observations of a newly identified compact galaxy group near the Zone of Avoidance

We have identified a new group of galaxies, CG J0247+44.9, at low Galactic latitude (l=143.64 deg, b=-13.29 deg), which satisfies Hickson's criteria (Hickson 1997) for Compact Groups (CGs). Our group consists of six members, two of which are in close interaction (IRAS 02443+4437). We present here optical photometry (BVRI) and low resolution spectroscopy of the individual galaxies and investigate the global properties of the group. Our morphological analysis reveals that two out of the six objects are lenticular galaxies. The others are spirals showing emission lines in their spectra through which we could classify them as a starburst galaxy (the spiral member of the IRAS 02443+4437 close pair), a Seyfert 2, a LINER and a weak H II galaxy. Since the S0/Sa is the prevailing morphology for the galaxies of this group, which is also characterized by a short crossing time and a relatively high velocity dispersion, we suggest that CG J0247+44.9 is a dynamically old compact group.Comment: 17 pages, 5 figures out of which 4 are in jpg format. A postscript version of the paper including all figures is available at: http://astro.uibk.ac.at/~giovanna/preprints/Temporins.ps.gz Accepted for publication in Astronomy and Astrophysic

Examining Autoexposure for Challenging Scenes

Autoexposure (AE) is a critical step applied by camera systems to ensure properly exposed images. While current AE algorithms are effective in well-lit environments with constant illumination, these algorithms still struggle in environments with bright light sources or scenes with abrupt changes in lighting. A significant hurdle in developing new AE algorithms for challenging environments, especially those with time-varying lighting, is the lack of suitable image datasets. To address this issue, we have captured a new 4D exposure dataset that provides a large solution space (i.e., shutter speed range from (1/500 to 15 seconds) over a temporal sequence with moving objects, bright lights, and varying lighting. In addition, we have designed a software platform to allow AE algorithms to be used in a plug-and-play manner with the dataset. Our dataset and associate platform enable repeatable evaluation of different AE algorithms and provide a much-needed starting point to develop better AE methods. We examine several existing AE strategies using our dataset and show that most users prefer a simple saliency method for challenging lighting conditions.Comment: ICCV 202

Microscope 2.0: An Augmented Reality Microscope with Real-time Artificial Intelligence Integration

The brightfield microscope is instrumental in the visual examination of both biological and physical samples at sub-millimeter scales. One key clinical application has been in cancer histopathology, where the microscopic assessment of the tissue samples is used for the diagnosis and staging of cancer and thus guides clinical therapy. However, the interpretation of these samples is inherently subjective, resulting in significant diagnostic variability. Moreover, in many regions of the world, access to pathologists is severely limited due to lack of trained personnel. In this regard, Artificial Intelligence (AI) based tools promise to improve the access and quality of healthcare. However, despite significant advances in AI research, integration of these tools into real-world cancer diagnosis workflows remains challenging because of the costs of image digitization and difficulties in deploying AI solutions. Here we propose a cost-effective solution to the integration of AI: the Augmented Reality Microscope (ARM). The ARM overlays AI-based information onto the current view of the sample through the optical pathway in real-time, enabling seamless integration of AI into the regular microscopy workflow. We demonstrate the utility of ARM in the detection of lymph node metastases in breast cancer and the identification of prostate cancer with a latency that supports real-time workflows. We anticipate that ARM will remove barriers towards the use of AI in microscopic analysis and thus improve the accuracy and efficiency of cancer diagnosis. This approach is applicable to other microscopy tasks and AI algorithms in the life sciences and beyond