We present results from the first geological field tests of the 'Cyborg Astrobiologist', which is a wearable computer and video camcorder system that we are using to test and train a computer-vision system towards having some of the autonomous decision-making capabilities of a field-geologist and field-astrobiologist. The Cyborg Astrobiologist platform has thus far been used for testing and development of these algorithms and systems: robotic acquisition of quasi-mosaics of images, real-time image segmentation, and real-time determination of interesting points in the image mosaics. The hardware and software systems function reliably, and the computer-vision algorithms are adequate for the first field tests. In addition to the proof-of-concept aspect of these field tests, the main result of these field tests is the enumeration of those issues that we can improve in the future, including: first, detection and accounting for shadows caused by 3D jagged edges in the outcrop; second, reincorporation of more sophisticated texture-analysis algorithms into the system; third, creation of hardware and software capabilities to control the camera's zoom lens in an intelligent manner; and fourth, development of algorithms for interpretation of complex geological scenery. Nonetheless, despite these technical inadequacies, this Cyborg Astrobiologist system, consisting of a camera-equipped wearable-computer and its computer-vision algorithms, has demonstrated its ability of finding genuinely interesting points in real-time in the geological scenery, and then gathering more information about these interest points in an automated manner.

The 'Cyborg Astrobiologist' has undergone a second geological field trial, at a site in northern Guadalajara, Spain, near Riba de Santiuste. The site at Riba de Santiuste is dominated by layered deposits of red sandstones. The Cyborg Astrobiologist is a wearable computer and video camera system that has demonstrated a capability to find uncommon interest points in geological imagery in real-time in the field. In this second field trial, the computer vision system of the Cyborg Astrobiologist was tested at seven different tripod positions, on three different geological structures. The first geological structure was an outcrop of nearly homogeneous sandstone, which exhibits oxidizediron impurities in red and and an absence of these iron impurities in white. The white areas in these "red beds" have turned white because the iron has been removed. The iron removal from the sandstone can proceed once the iron has been chemically reduced, perhaps by a biological agent. The computer vision system found in one instance several (iron-free) white spots to be uncommon and therefore interesting, as well as several small and dark nodules. The second geological structure was another outcrop some 600 meters to the east, with white, textured mineral deposits on the surface of the sandstone, at the bottom of the outcrop. The computer vision system found these white, textured mineral deposits to be interesting. We acquired samples of the mineral deposits for geochemical analysis in the laboratory. This laboratory analysis of the crust identifies a double layer, consisting of an internal millimeter-size layering of calcite and an external centimeter-size effluorescence of gypsum. The third geological structure was a 50 cm thick paleosol layer, with fossilized root structures of some plants. The computer vision system also found certain areas of these root structures to be interesting. A quasi-blind comparison of the Cyborg Astrobiologist's interest points for these images with the interest points determined afterwards by a human geologist shows that the Cyborg Astrobiologist concurred with the human geologist 68% of the time (true positive rate), with a 32% false positive rate and a 32% false negative rate. The performance of the Cyborg Astrobiologist's computer vision system was by no means perfect, so there is plenty of room for improvement. However, these tests validate the image-segmentation and uncommon-mapping technique that we first employed at a different geological site (Rivas Vaciamadrid) with somewhat different properties of the imagery.

No abstract

Detailed animation of 3D articulated body models is in principle desirable but is also a highly resource‐intensive task. Resource limitations are particularly critical in 3D visualizations of multiple characters in real‐time game sequences. We investigated to what extent observers perceptually process the level of detail in naturalistic character animations. Only if such processing occurs would it be justified to spend valuable resources on richness of detail. An experiment was designed to test the effectiveness of 3D body animation. Observers had to judge the level of overall skill exhibited by four simulated soccer teams. The simulations were based on recorded RoboCup simulation league games. Thus objective skill levels were known from the teams' placement in the tournament. The animations' level of detail was varied in four increasing steps of modelling complexity. Results showed that observers failed to notice the differences in detail. Nonetheless, clear effects of character animation on perceived skill were found. We conclude that character animation co‐determines perceptual judgements even when observers are completely unaware of these manipulations. Copyright © 2000 John Wiley & Sons, Ltd.

In previous work, a platform was developed for testing computer-vision algorithms for robotic planetary exploration. This platform consisted of a digital video camera connected to a wearable computer for real-time processing of images at geological and astrobiological field sites. The real-time processing included image segmentation and the generation of interest points based upon uncommonness in the segmentation maps. Also in previous work, this platform for testing computer-vision algorithms has been ported to a more ergonomic alternative platform, consisting of a phone camera connected via the Global System for Mobile Communications (GSM) network to a remote-server computer. The wearable-computer platform has been tested at geological and astrobiological field sites in Spain (Rivas Vaciamadrid and Riba de Santiuste), and the phone camera has been tested at a geological field site in Malta. In this work, we (i) apply a Hopfield neural-network algorithm for novelty detection based upon colour, (ii) integrate a field-capable digital microscope on the wearable computer platform, (iii) test this novelty detection with the digital microscope at Rivas Vaciamadrid, (iv) develop a Bluetooth communication mode for the phone-camera platform, in order to allow access to a mobile processing computer at the field sites, and (v) test the novelty detection on the Bluetooth-enabled phone camera connected to a netbook computer at the Mars Desert Research Station in Utah. This systems engineering and field testing have together allowed us to develop a real-time computer-vision system that is capable, for example, of identifying lichens as novel within a series of images acquired in semi-arid desert environments. We acquired sequences of images of geologic outcrops in Utah and Spain consisting of various rock types and colours to test this algorithm. The algorithm robustly recognized previously observed units by their colour, while requiring only a single image or a few images to learn colours as familiar, demonstrating its fast learning capability.