The design of benchmark imagery for validation of image an notation algorithms is considered. Emphasis is placed on imagery that contains industrial facilities, such as chemical re fineries. An application-level facility ontology is used as a means to define salient objects in the benchmark imagery. Instrinsic and extrinsic scene factors important for comprehensive validation are listed, and variability in the benchmarks discussed. Finally, the pros and cons of three forms of bench mark imagery: real, composite and synthetic, are delineated.

Verification and validation (V&V) of geospatial image analysis algorithms is a difficult task and is becoming increasingly important. While there are many types of image analysis algorithms, we focus on developing V&V methodologies for algorithms designed to provide textual descriptions of geospatial imagery. In this paper, we present a novel methodological basis for V&V that employs a domain-specific ontology, which provides a naming convention for a domain-bounded set of objects and a set of named relationships between these objects. We describe a validation process that proceeds through objectively comparing benchmark imagery, produced using the ontology, with algorithm results. As an example, we describe how the proposed V&V methodology would be applied to algorithms designed to provide textual descriptions of facilities.

This paper presents the Simulation, Tactical Operations and Mission Planning (STOMP) software architecture and framework for simulating, controlling and communicating with unmanned air vehicles (UAVs) servicing large distributed sensor networks. STOMP provides hardware-in-the-loop capability enabling real UAVs and sensors to feedback state information, route data and receive command and control requests while interacting with other real or virtual objects thereby enhancing support for simulation of dynamic and complex events.

An adaptive path planning algorithm is presented for cooperating unmanned air vehicles (UAVs) that are used to deploy and operate land-based sensor networks. The algorithm employs a global cost function to generate paths for the UAVs, and adapts the paths to exceptions that might occur. Examples are provided of the paths and adaptation.

A three-dimensional world model is crucial for many robotic tasks. Modeling techniques tend to be either fully manual or autonomous. Manual methods are extremely time consuming but also highly accurate and flexible. Autonomous techniques are fast but inflexible and, with real-world data, often inaccurate. The method presented in this paper combines the two, yielding a highly efficient, flexible, and accurate mapping tool. The segmentation and modeling algorithms that compose the method are specifically designed for industrial environments, and are described in detail. A mapping system based on these algorithms has been designed. It enables a human supervisor to quickly construct a fully defined world model from unfiltered and unsegmented real-world range imagery. Examples of how industrial scenes are modeled with the mapping system are provided.

Real-time cyclic spectral analysis is useful in many applications, but is difficult to achieve because of its computational complexity. This paper studies the distribution of complex multipliers in multiprocessor cyclic spectrum analyzers, with the objective of obtaining computational balance. Computationally balanced implementations efficiently use hardware so that computational bottlenecks are reduced and a smooth flow of data between computational sections of the analyzer is maintained. Tables are presented that give the number of complex multipliers required in each section of the analyzer to obtain computational balance.>