No abstract

We present highly parallelizable and computationally efficient High Dynamic Range (HDR) image compositing, reconstruction, and spatotonal mapping algorithms for processing HDR video. We implemented our algorithms in the EyeTap Digital Glass electric seeing aid, for use in everyday life. We also tested the algorithms in extreme dynamic range situations, such as, electric arc welding. Our system runs in real-time, and requires no user intervention, and no fine-tuning of parameters after a one-time calibration, even under a wide variety of very difficult lighting conditions (e.g. electric arc welding, including detailed inspection of the arc, weld puddle, and shielding gas in TIG welding). Our approach can render video at 1920x1080 pixel resolution at interactive frame rates that vary from 24 to 60 frames per second with GPU acceleration. We also implemented our system on FPGAs (Field Programmable Gate Arrays) for being miniaturized and built into eyeglass frames.

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 gesturesensing pendants, and other miniature wearable devices, with low-power and compact circuits that can be easily mounted on the body.

Abstract-Toposculpting is the creation of virtual objects by moving real physical objects through space to extrude patterns like beams and pipes. In one example, a method of making pipe sculptures is proposed, using a ring-shaped object moved through space.In particular, computational lightpainting is proposed as a new form of data entry, 3D object creation, or user-interface.When combined with wearable computational photography, especially by way of a true 3D time-of-flight camera system, such as the Meta Spaceglasses (extramissive spatial imaging glass manufactured by Meta-View), real physical objects are manipulated during an actual or simulated long-exposure 3D photography process.

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. Implementations for real-time HDR videos have relied upon methods that are less accurate. Instead of weighted-sum approaches that adds up noise for large number of LDR images, lookup tables that contains results calculated using a probabilistic model can be generated. This lookup table, when compressed using quadtree structure, can be implemented on a mediumsized FPGA. The work presented in this paper improves over the earlier publication to support high-definition videos. To achieve the required bandwidth for HDR composition, the evaluation of the camera response function is performed within a compressed lookup table that has its address retrieved using a pipelined multiplexer network. We are able to generate the entire circuit using software. It is parameterizable by user-specified error constraints, allowing us to explore the trade-offs in resource usage and precision of the implementation.978-1-4799-3010