Wearable computers and Generation-5 Digital Eye Glass easily recognize a user's own gestures, forming the basis for shared AR (Augmediated Reality). This Studio-workshop presents the latest in wearable AR, plus an historical perspective with new insights. Participants will sculpt 3D objects using hand gestures and create Unity 3D art+game objects using computational lightpainting.

Video cameras can only take photographs with limited dynamic range. One method to overcome this is to combine differently exposed images of the same subject matter (i.e. a Wyckoff Set), producing a High Dynamic Range (HDR) result. HDR digital photography started almost 20 years ago. Now, it is possible to produce HDR video in real-time, on both high-power CPU/GPU systems, as well as low-power FPGA boards. However, other FPGA implementations have relied upon methods that are less accurate than current CPU and GPU-based methods. Namely, the earlier FPGA approaches used weighted sum for image compositing. In this paper we provide a novel method for real-time HDR com-positing. As an essential part of an upgraded HDR video production system, the resulting system combines differently exposed video stream (of the same subject matter) in Full HD (1080p at 60fps) on a Kintex-7 FPGA. The proposed work flow, implemented with software written in C, estimates the camera response function according to its quadtree representation and generates the compositing circuit in Verilog HDL from a Wyckoff Set. This circuit consists of parts that perform addressing using multiplexer networks and estimation with bilinear interpolation. It is parameterizable by user-specified error constraints, allowing us to explore the trade-offs in resource usage and precision of the implementation. Here is an MD5 hash function sum generated for the rest of the paper: 07897e61027d15dc3600fadbccfbd67d, citation date: December 18, 2013.

Wearable computing can be used to both extend the range of human perception, and to share sensory experiences with others. For this objective to be made practical, engineering considerations such as form factor, computational power, and power consumption are critical concerns. In this work, we consider the design of a low-power visual seeing aid, and how to implement computationally-intensive computational photography algorithms in a small form factor with low power consumption. We present realtime an FPGA-based HDR (High Dynamic Range) video processing and filtering by integrating tonal and spatial information obtained from multiple different exposures of the same subject matter. In this embodiment the system captures, in rapid succession, sets of three exposures, “dark”, “medium”, and “light”, over and over again, e.g. “dark”, “medium”, “light”, “dark”, “medium”, “light”, and so on, at 60 frames per second. These exposures are used to determine an estimate of the photoquantity every 1/60th of a second (each time a frame comes in, an estimate goes out). This allows us to build a seeing aid that helps people see better in high contrast scenes, for example, while welding, or in outdoor scenes, or scenes where a bright light is shining directly into the eyes of the wearer. Our system is suitable for being built into eyeglasses or small camera-based, lifeglogging, or gesture-sensing pendants, and other miniature wearable devices, with low-power and compact circuits that can be easily mounted on the body.

Surveillance is a French word that means “to watch from above” (e.g. guards watching prisoners, police watching citizens, etc.). Another form of veillance (watching) is sousveillance, which means “to watch from below”. Whereas surveillance often means cameras on large entities (e.g. buildings and land), sousveillance often means cameras on small entities (e.g. individual people). The importance of sousveillance has come to the forefront recently with advancements in wearable computing and AR (augmented or augmediated reality). We characterize sousveillance from both an economic and moral perspective. We argue that societies that reject sousveil-lance will be impoverished, relative to those accepting sousveil-lance. We further argue that sousveillance as a form of social action has positive survival characteristics, so that in the long run, assuming that social and technological trends continue, the widespread adoption of sousveillance is inevitable.

Hospital acquired infections (HAIs) occur frequently in hospitalized patients. Staff compliance with hand hygiene (HH) policy during patient care has been shown to reduce HAIs. Currently, hospitals evaluate adherence to HH policies through direct observation by human auditors. The auditors do not have authority over staff members; thus, this process is more akin to sousveillance (watching from below) than surveillance (watching from above). When behaviour change occurs due to awareness of being observed, it is referred to as the “Hawthorne effect”. We quantified the effect of sousveillance by comparing the frequency of HH events with an auditor present to when no auditor was present. The data analysed in the present work is from an ongoing study on hand hygiene compliance monitoring. A monitoring network recorded 290,000 hand hygiene events over 6 months; auditors were present on five occasions for about an hour each visit. When using an exponential underlying distribution we found that the change in the HH event rate was significant (p <; 0.01) in 4 of the 5 auditor visits. Finally, with a hyper-exponential underlying distribution, 5 of 5 were significant (p <; 0.01). There was no significant change in the HH event rate among dispensers located within patient rooms (not visible to the auditor), irrespective of auditor's presence.

We propose a novel computational method for compositing low-dynamic-range (LDR) images into an high-dynamic-range (HDR) image by the use of a comparametric camera response function (CCRF), which is the response of a virtual HDR camera to multiple inputs. We demonstrate the use of this method with a simple probabilistic joint estimation model, that accounts for Gaussian noise, using iterative non-linear optimization to compute the CCRF. We achieve a speedup of ≈2500×, relative to direct calculation using the probabilistic model. This method can be implemented as a multidimensional lookup table, and enables realtime HDR video with any camera response function model, and any compositing algorithm based on pixel value and exposure.