Steven D. Feller

Multiscale gigapixel photography

Pixel count is the ratio of the solid angle within a camera’s field of view to the solid angle covered by a single detector element. Because the size of the smallest resolvable pixel is proportional to aperture diameter and the maximum field of view is scale independent, the diffraction-limited pixel count is proportional to aperture area. At present, digital cameras operate near the fundamental limit of 1–10 megapixels for millimetre-scale apertures, but few approach the corresponding limits of 1–100 gigapixels for centimetre-scale apertures. Barriers to high-pixel-count imaging include scale-dependent geometric aberrations, the cost and complexity of gigapixel sensor arrays, and the computational and communications challenge of gigapixel image management. Here we describe the AWARE-2 camera, which uses a 16-mm entrance aperture to capture snapshot, one-gigapixel images at three frames per minute. AWARE-2 uses a parallel array of microcameras to reduce the problems of gigapixel imaging to those of megapixel imaging, which are more tractable. In cameras of conventional design, lens speed and field of view decrease as lens scale increases, but with the experimental system described here we confirm previous theoretical results suggesting that lens speed and field of view can be scale independent in microcamera-based imagers resolving up to 50 gigapixels. Ubiquitous gigapixel cameras may transform the central challenge of photography from the question of where to point the camera to that of how to mine the data.

Tracking and imaging humans on heterogeneous infrared sensor arrays for law enforcement applications

We present a plan for the integration of geometric constraints in the source, sensor and analysis levels of sensor networks. The goal of geometric analysis is to reduce the dimensionality and complexity of distributed sensor data analysis so as to achieve real-time recognition and response to significant events. Application scenarios include biometric tracking of individuals, counting and analysis of individuals in groups of humans and distributed sentient environments. We are particularly interested in using this approach to provide networks of low cost point detectors, such as infrared motion detectors, with complex imaging capabilities. By extending the capabilities of simple sensors, we expect to reduce the cost of perimeter and site security applications.

Tomographic imaging of foam

The morphology of three-dimensional foams is of interest to physicists, engineers, and mathematicians. It is desired to image the 3-dimensional structure of the foam. Many different techniques have been used to image the foam, including magnetic resonance imaging, and short-focal length lenses. We use a camera and apply tomographic algorithms to accurately image a set of bubbles. We correct for the distortion of a curved plexiglas container using ray-tracing.

Multiple order coded aperture spectrometer.

We introduce a multiple order coded aperture (MOCA) spectrometer. The MOCA is a system that uses a multiplex hologram and a coded aperture to increase the spectral range and throughput of the system over conventional spectrometers while maintaining spectral resolution. This results in an order of magnitude reduction in system volume with no loss in resolution.

Tracking and imaging humans on heterogeneous infrared sensor arrays for tactical applications

We recently implemented a heterogeneous network of infrared motion detectors and an infrared camera for the detection, localization, tracking, and identification of human targets. The network integrates dense deployments of low cost motion sensors for target tracking with sparse deployments of image sensors for target registration. Such networks can be used in tactical applications for local and distributed perimeter and site security. Rapid deployments for crisis management may be of particular interest. This paper focuses particularly on the need for applications that deal with relatively dense and complex source fields such as crowds move through sensor spaces.

Characterization of the AWARE 10 two-gigapixel wide-field-of-view visible imager

System requirements for many military electro-optic and IR camera systems reflect the need for both wide-field-of-view situational awareness as well as high-resolution imaging for target identification. In this work we present a new imaging system architecture designed to perform both functions simultaneously and the AWARE 10 camera as an example at visible wavelengths. We first describe the basic system architecture and user interface followed by a laboratory characterization of the system optical performance. We then describe a field experiment in which the camera was used to identify several maritime targets at varying range. The experimental results indicate that users of the system are able to correctly identify ~10 m targets at between 4 and 6 km with 70% accuracy.

Gigapixel Imaging with the AWARE Multiscale Camera

Gigapixel cameras have been confined to specialized applications such as aerial photography and astronomical observatories. A simplified architecture would better suit terrestrial imaging and reduce instrument cost and complexity. Our gigapixel AWARE camera is based on monocentric multiscale optical design principles that produce high-resolution images with a field of view (FOV) limited only by vignetting. This design allows resolution to approach the theoretical diffraction limits for a given entrance pupil size and FOV.

Biometric tracking with coded pyroelectric sensor clusters

Human bodies are very good heat sources with peak emission wavelength of about 9?m. We use pyroelectric detectors that are differential in nature to detect human motion by their heat emissions. Coded Fresnel lens arrays create boundaries in space which helps to localize the human motion as well as classification. We design and implement a low-cost biometric tracking system using off-the-shelf components. We demonstrate tracking and classification using sensor clusters of dualelement pyroelectric detectors with coded Fresnel lens arrays.

Information flow in streaming 3D video

We describe streaming 3D video on the Argus sensor space. Argus is a Beowulf-style distributed computer with 64 processors and 64 video camera/capture pairs. Argus is a test-bed for comparing sensor space modeling and reconstruction algorithms. We describe the implementation of tomographic and stereo triangulation algorithms on this space and consider mappings from the sensor space to associated display spaces.

Three dimensional imaging with the argus sensor array

The Argus project uses an array of computers and cameras as a means of investigating telepresence and real-time three-dimensional imaging. In this paper we will briefly discuss telepresence from an information flow and visualization perspective. The paper also includes a detailed description of the Argus hardware and a software layer developed to manage the imaging and computational resources. MPEG-2 and feature extraction will be described as parallel compression systems for the Argus camera array.