Cloud Computing has delivered unprecedented compute capacity to NASA missions at affordable rates. Missions like the Mars Exploration Rovers (MER) and Mars Science Lab (MSL) are enjoying the elasticity that enables them to leverage hundreds, if not thousands, or machines for short durations without making any hardware procurements. In this paper, we describe Polyphony, a resilient, scalable, and modular framework that efficiently leverages a large set of computing resources to perform parallel computations. Polyphony can employ resources on the cloud, excess capacity on local machines, as well as spare resources on the supercomputing center, and it enables these resources to work in concert to accomplish a common goal. Polyphony is resilient to node failures, even if they occur in the middle of a transaction. We will conclude with an evaluation of a production-ready application built on top of Polyphony to perform image-processing operations of images from around the solar system, including Mars, Saturn, and Titan.

The Science Activity Planner (SAP) is the primary science operations tool for the Mars Exploration Rover mission and NASA’s Software of the Year for 2004. SAP utilizes a variety of visualization and planning capabilities to enable the mission operations team to direct the activities of the Spirit and Opportunity rovers. This paper outlines some of the challenging requirements that drove the design of SAP and discusses lessons learned from the development and use of SAP in mission operations.

We present an account of the scientific visualization techniques that were used to support science planning for NASA's 2003 Mars Exploration Rover Mission. The great sophistication of the rover's Athena Science Payload required a wide variety of visualization modalities to make the large amounts of scientific data accessible and readily understood by the science planners under the tight time constraints of the tactical planning schedule. A number of techniques were also newly developed to support science operations, including on-the-fly mosaic stitching and rendering, image cube visualization, and data fusion. Many other well-established visualization techniques were applied to science operations also, such as anaglyph stereo, high performance 3D visualization, and optimized I/O and memory management techniques for the loading, processing, and visualization of very large data sets. We discuss each of these topics and demonstrate their application in the MER mission with examples.

The Science Activity Planner (SAP) is the science planning tool used for the Mars Exploration Rover (MER) mission. This paper begins with an overview of the software developed for MER and how it was used for science downlink analysis and activity planning. The overview of SAP is then followed by a report on a number of new development efforts that aim to improve on the capabilities of SAP for future missions. The selected areas discussed herein include the application of geographical information systems in tactical downlink analysis, new strategies for distributed data access and planning support, a virtual field test capability to support “what if?” scenarios for new technologies and mission concepts, and the use of agile development methods to improve the development of Mars surface mission support software as a whole.

Previous performance evaluation of range image segmentation algorithms has depended on manual tuning of algorithm parameters, and has lacked a basis for a test of the significance of differences between algorithms. We present an automated framework for evaluating the performance of range image segmentation algorithms. Automated tuning of algorithm parameters in this framework results in performance as good as that previously obtained with careful manual tuning by the algorithm developers. Use of multiple training and test sets of images provides the basis for a test of the significance of performance differences between algorithms. The framework implementation includes range images, ground truth overlays, program source code, and shell scripts. This framework should a) make it possible to objectively and reliably compare the performance of range image segmentation algorithms; b) allow informed experimental feedback for the design of improved segmentation algorithms. The framework is demonstrated using range images, but in principle it could be used to evaluate region segmentation algorithms for any type of image.

We present a methodology for correcting color images taken in practical indoor environments, such as laboratories, factories, and studios, that explicitly models illuminant location, surface reflectance and geometry, and camera responsivity. We explicitly model surfaces by taking our color images with corresponding registered three-dimensional (3-D) range images, which provide surface orientation and location information for every point in the scene. We automatically detect regions where color correction should not be applied, such as specularities, coarse texture regions, and jump edges. This correction results in objective color measures of the imaged surfaces. This kind of integrated, comprehensive system of color correction has not existed until now. i.e., it is the first of its kind in computer vision. We demonstrate results of applying this methodology to real images for applications in photorealistic rerendering, skin lesion detection, burn scar color measurement, and general color image enhancement. We also have tested the method under different lighting configurations and with three different range scanners.

We introduce a software framework called the Java Vision Toolkit (JVT) for teaching image processing and computer vision. The toolkit provides over 50 image operations and presents them to the user in a GUI that can render grayscale, color and 3D range images. The software is written in Java, enabling it to be integrated into HTML documents and interactive course materials. The framework is designed for extensibility using a source code template that supports the implementation of any new operation with a minimal amount of supporting code. For students, this framework encapsulates the GUI, file I/O and other trivial programming details and allows them the maximum amount of time to spend on understanding computer vision. We compare the JVT with other computer vision software frameworks that are used for teaching and research. We also discuss the use of the JVT in an undergraduate image processing course at the University of South Florida.

We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of three spheres at known relative positions. The process uses intensity images to find the positions of the light sources. We conducted experiments to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within 2.7/spl plusmn/4/spl deg/ at /spl alpha/=.05 (6 percent relative) of its true direction and within 0.13/spl plusmn/.02 m at /spl alpha/=.05 (9 percent relative) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information is used for color correction.

We present a methodology for calibrating multiple light source locations in 3D from images. The procedure involves the use of a novel calibration object that consists of either 2 or 3 spheres at known relative positions. There are two variants of the process: one which uses range and intensity imaging to find the positions of the light sources, and one that uses only the intensity image to locate the illuminants. We conducted experiments using both variations of the technique to locate light sources in 51 different positions in a laboratory setting. Our data shows that the vector from a point in the scene to a light source can be measured to within 3/spl deg/(6%) of its tote direction and within 0.13 m (9%) of its true magnitude compared to empirically measured ground truth. Finally, we demonstrate how light source information can be applied to burn scar color correction and color segmentation.

We have developed an automated framework for performance evaluation of curved-surface range image segmentation algorithms. Enhancements over our previous work include automated training of parameter values, correcting the artifact problem in K/sup 2/T scanner images, and acquisition of images of the same scenes from different range scanners. The image dataset includes planar, spherical, cylindrical, conical, and toroidal surfaces. We have evaluated the automated parameter tuning technique and found that it compares favorably with manual parameter tuning. We present initial results from comparing curved-surface segmenters by Besl and Jain (1988) and by Jiang and Bunke (1998).

We have developed an automated framework for objectively evaluating the performance of region segmentation algorithms. This framework is demonstrated with range image data sets, but is applicable to any type of imagery. Parameters of the segmentation algorithm are tuned using training images. Images and source code for the training process care publicly available. The trained parameters are then used to evaluate the algorithm on a (sequestered) test set. The primary performance metric is the average number of correctly segmented regions. Statistical tests are used to determine the significance of performance improvement over a baseline algorithm.

This work focuses on creating a framework for objectively evaluating the performance of range image segmentation algorithms. The algorithms are evaluated in terms of correct segmentation, over- and under-segmentation, missed and noise regions. A set of images with ground truth was created for this work. The images were captured using a structured light scanner. Images used in the evaluation contain planar, spherical, cylindrical, toroidal and conical surface patches. The different surface patches in each image were manually identified to establish ground truth for performance evaluation. Two segmentation algorithms from the literature are compared.