Automating the Hunt for Volcanoes on Venus

Our long-term goal is to develop a trainable tool for locating patterns of interest in large image databases. Toward this goal we have developed a prototype system, based on classical filtering and statistical pattern recognition techniques, for automatically locating volcanoes in the Magellan SAR database of Venus. Training for the specific volcano-detection task is obtained by synthesizing feature templates (via normalization and principal components analysis) from a small number of examples provided by experts. Candidate regions identified by a focus of attention (FOA) algorithm are classified based on correlations with the feature templates. Preliminary tests show performance comparable to trained human observers

Finding Faces in Cluttered Scenes using Random Labeled Graph Matching

An algorithm for locating quasi-frontal views of human faces in cluttered scenes is presented. The algorithm works by coupling a set of local feature detectors with a statistical model of the mutual distances between facial features it is invariant with respect to translation, rotation (in the plane), and scale and can handle partial occlusions of the face. On a challenging database with complicated and varied backgrounds, the algorithm achieved a correct localization rate of 95% in images where the face appeared quasi-frontally

Automated analysis of radar imagery of Venus: handling lack of ground truth

Lack of verifiable ground truth is a common problem in remote sensing image analysis. For example, consider the synthetic aperture radar (SAR) image data of Venus obtained by the Magellan spacecraft. Planetary scientists are interested in automatically cataloging the locations of all the small volcanoes in this data set; however, the problem is very difficult and cannot be performed with perfect reliability even by human experts. Thus, training and evaluating the performance of an automatic algorithm on this data set must be handled carefully. We discuss the use of weighted free-response receiver-operating characteristics (wFROCs) for evaluating detection performance when the “ground truth” is subjective. In particular, we evaluate the relative detection performance of humans and automatic algorithms. Our experimental results indicate that proper assessment of the uncertainty in “ground truth” is essential in applications of this nature

Face Localization via Shape Statistics

In this paper, a face localization system is proposed in which local detectors are coupled with a statistical model of the spatial arrangement of facial features to yield robust performance. The outputs from the local detectors are treated as candidate locations and constellations are formed from these. The effects of translation, rotation, and scale are eliminated by mapping to a set of shape variables. The constellations are then ranked according to the likelihood that the shape variables correspond to a face versus an alternative model. Incomplete constellations, which occur when some of the true features are missed, are handled in a principled way

Inferring individual attributes from search engine queries and auxiliary information

Internet data has surfaced as a primary source for investigation of different aspects of human behavior. A crucial step in such studies is finding a suitable cohort (i.e., a set of users) that shares a common trait of interest to researchers. However, direct identification of users sharing this trait is often impossible, as the data available to researchers is usually anonymized to preserve user privacy. To facilitate research on specific topics of interest, especially in medicine, we introduce an algorithm for identifying a trait of interest in anonymous users. We illustrate how a small set of labeled examples, together with statistical information about the entire population, can be aggregated to obtain labels on unseen examples. We validate our approach using labeled data from the political domain. We provide two applications of the proposed algorithm to the medical domain. In the first, we demonstrate how to identify users whose search patterns indicate they might be suffering from certain types of cancer. In the second, we detail an algorithm to predict the distribution of diseases given their incidence in a subset of the population at study, making it possible to predict disease spread from partial epidemiological data

Progress in use of carbon-black-polymer composite vapor detector arrays for land mine detection

Thin films of carbon black-organic polymer composites have been deposited across two metallic leads, with swelling- induced resistance changes of the films signaling the presence of vapors. To identify and classify vapors, arrays of such vapor sensing elements have been constructed. Each element contained a different organic polymer as the insulating phase. The differing gas-solid partition coefficients for the various polymers of the detector array produced a pattern of resistance changes that was used to classify vapors and vapor mixtures. The performance of this system towards DNT, the predominant signature in the vapor phase above land miens, has been evaluated in detail, with robust detection demonstrated in the laboratory in less than 5 s in air at DNT levels in the low ppb range

Kentucky UST Field Manual

This study was undertaken to address the removal and closure of defective petroleum underground storage tanks in Kentucky. Goals for the study included: To address standards for levels of contamination requiring corrective action consistent with accepted scientific and technical principles. To recommend a matrix or scoring system to be used for (a) ranking sites as to actual or potential harm to human health and the environment caused by release of petroleum from a petroleum storage tank, and (2) establishing standards and procedures for corrective action that shall adequately protect human health and the environment. To address all compounds individually and collectively known as petroleum. To produce a report that shall be scientifically defensible

Progress in use of carbon-black-polymer composite vapor detector arrays for land mine detection

Thin films of carbon black-organic polymer composites have been deposited across two metallic leads, with swelling- induced resistance changes of the films signaling the presence of vapors. To identify and classify vapors, arrays of such vapor sensing elements have been constructed. Each element contained a different organic polymer as the insulating phase. The differing gas-solid partition coefficients for the various polymers of the detector array produced a pattern of resistance changes that was used to classify vapors and vapor mixtures. The performance of this system towards DNT, the predominant signature in the vapor phase above land miens, has been evaluated in detail, with robust detection demonstrated in the laboratory in less than 5 s in air at DNT levels in the low ppb range

Status Report: Identification of Appropriate Standards for Corrective Action for a Release from Petroleum Underground Storage Tanks, Volume 1

This study was undertaken to address the removal and closure of defective petroleum underground storage tanks in Kentucky: To address standards for levels of contamination requiring corrective action consistent with accepted scientific and technical principles. To recommend a matrix or scoring system to be used for (a) ranking sites as to actual or potential harm to human health and the environment caused by a release of petroleum from a petroleum storage tank, and (b) establishing standards and procedures for corrective action that shall adequately protect human health and the environment. To address all compounds individually and collectively known as petroleum. To produce a report that shall be scientifically defensible

Autonomous Exploration for Gathering Increased Science

The Autonomous Exploration for Gathering Increased Science System (AEGIS) provides automated targeting for remote sensing instruments on the Mars Exploration Rover (MER) mission, which at the time of this reporting has had two rovers exploring the surface of Mars (see figure). Currently, targets for rover remote-sensing instruments must be selected manually based on imagery already on the ground with the operations team. AEGIS enables the rover flight software to analyze imagery onboard in order to autonomously select and sequence targeted remote-sensing observations in an opportunistic fashion. In particular, this technology will be used to automatically acquire sub-framed, high-resolution, targeted images taken with the MER panoramic cameras. This software provides: 1) Automatic detection of terrain features in rover camera images, 2) Feature extraction for detected terrain targets, 3) Prioritization of terrain targets based on a scientist target feature set, and 4) Automated re-targeting of rover remote-sensing instruments at the highest priority target