Cyborg Astrobiologist Research Articles

The Cyborg Astrobiologist: matching of prior textures by image compression for geological mapping and novelty detection

AbstractWe describe an image-comparison technique of Heidemann and Ritter (2008a, b), which uses image compression, and is capable of: (i) detecting novel textures in a series of images, as well as of: (ii) alerting the user to the similarity of a new image to a previously observed texture. This image-comparison technique has been implemented and tested using our Astrobiology Phone-cam system, which employs Bluetooth communication to send images to a local laptop server in the field for the image-compression analysis. We tested the system in a field site displaying a heterogeneous suite of sandstones, limestones, mudstones and coal beds. Some of the rocks are partly covered with lichen. The image-matching procedure of this system performed very well with data obtained through our field test, grouping all images of yellow lichens together and grouping all images of a coal bed together, and giving 91% accuracy for similarity detection. Such similarity detection could be employed to make maps of different geological units. The novelty-detection performance of our system was also rather good (64% accuracy). Such novelty detection may become valuable in searching for new geological units, which could be of astrobiological interest. The current system is not directly intended for mapping and novelty detection of a second field site based on image-compression analysis of an image database from a first field site, although our current system could be further developed towards this end. Furthermore, the image-comparison technique is an unsupervised technique that is not capable of directly classifying an image as containing a particular geological feature; labelling of such geological features is donepost factoby human geologists associated with this study, for the purpose of analysing the system's performance. By providing more advanced capabilities for similarity detection and novelty detection, this image-compression technique could be useful in giving more scientific autonomy to robotic planetary rovers, and in assisting human astronauts in their geological exploration and assessment.

The Cyborg Astrobiologist: testing a novelty detection algorithm on two mobile exploration systems at Rivas Vaciamadrid in Spain and at the Mars Desert Research Station in Utah

AbstractIn 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.

The Cyborg Astrobiologist: porting from a wearable computer to the Astrobiology Phone-cam

AbstractWe have used a simple camera phone to significantly improve an ‘exploration system’ for astrobiology and geology. This camera phone will make it much easier to develop and test computer-vision algorithms for future planetary exploration. We envision that the ‘Astrobiology Phone-cam’ exploration system can be fruitfully used in other problem domains as well.

The Cyborg Astrobiologist: scouting red beds for uncommon features with geological significance

The `Cyborg Astrobiologist' (CA) has undergone a second geological field trial, at a red sandstone site in northern Guadalajara, Spain, near Riba de Santiuste. 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. The first (of three) geological structures that we studied was an outcrop of nearly homogeneous sandstone, which exhibits oxidized-iron 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 by chemical reduction, 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 contained white, textured mineral deposits on the surface of the sandstone, which were found by the CA to be interesting. The third geological structure was a 50 cm thick paleosol layer, with fossilized root structures of some plants, which were found by the CA 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. (abstract has been abridged).

The Cyborg Astrobiologist: first field experience

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 the following 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: detection and accounting for shadows caused by three-dimensional jagged edges in the outcrop; reincorporation of more sophisticated texture-analysis algorithms into the system; creation of hardware and software capabilities to control the camera's zoom lens in an intelligent manner; and, finally, 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 in finding genuinely interesting points in real-time in the geological scenery, and then gathering more information about these interest points in an automated manner.