Program slicing has seen a plethora of applications and variations since its introduction over thirty years ago. The dominant method for computing slices involves significant complex source-code analysis to model the dependences in the code. A recently introduced alternative, Observation-Based Slicing (ORBS), sidesteps this complexity by observing the behavior of candidate slices. ORBS has several other strengths, including the ability to slice multi-language systems. However, ORBS remains rooted in tradition as it captures semantics by comparing sequences of values. This raises the question of whether it is possible to extend slicing beyond its traditional semantic roots. A few existing projects have attempted this, but the extension requires considerable effort. If it is possible to build on the ORBS platform to more easily generalize slicing to languages with non-traditional semantics, then there is the potential to vastly increase the range of programming languages to which slicing can be applied. ORBS supports this by reducing the problem to that of generalizing how semantics are captured. Taking Picture Description Languages as a case study, the challenges and effectiveness of such a generalization are considered. The results show that not only is it possible to generalize the ORBS algorithm, but the resulting slicer is quite effective removing from 27% to 98% of the original source code with an average of 85%. Finally a qualitative look at the slices finds the technique very effective, at times producing minimal slices.

Image registration is an important element in data processing for remote sensing with many applications and a wide range of solutions. Despite considerable investigation the field has not settled on a definitive solution for most applications and a number of questions remain open. This article looks at selected research issues by surveying the experience of operational satellite teams, application-specific requirements for Earth science, and our experiments in the evaluation of image registration algorithms with emphasis on the comparison of algorithms for subpixel accuracy. We conclude that remote sensing applications put particular demands on image registration algorithms to take into account domain-specific knowledge of geometric transformations and image content.

Flash LADAR cameras based on continuous-wave, time-of-flight range measurement deliver fast 3D imaging for robot applications including mapping, localization, obstacle detection and object recognition. The accuracy of the range values produced depends on characteristics of the scene as well as dynamically adjustable operating parameters of the cameras. In order to optimally set these parameters during camera operation we have devised and implemented an optimization algorithm in a modular, extensible architecture for real-time applications including robot control. The approach uses two components: offline nonlinear optimization to minimize the range error for a training set of simple scenes followed by an online, real-time algorithm to reference the training data and set camera parameters. We quantify the effectiveness of our approach and highlight topics of interest for future research.

The future of remote sensing will see the development of spacecraft formations, and with this development will come a number of complex challenges such as maintaining precise relative position and specified attitudes. At the same time, there will be increasing needs to understand planetary system processes and build accurate prediction models. One essential technology to accomplish these goals is the integration of multiple source data. For this integration, image registration and fusion represent the first steps and need to be performed with very high accuracy. In this paper, we describe studies performed in both image registration and fusion, including a modular framework that was built to describe registration algorithms, a Web-based image registration toolbox, and the comparison of several image fusion techniques using data from the EO-1/ALI and Hyperion sensors

While automatic image registration algorithms are usually being evaluated with regards to their accuracy, it is often useful to relate this accuracy to the "initial conditions", i.e., the distance between the initial navigation geolocation and the correct result. This paper describes a modular framework that was built to describe registration algorithms, and utilize this framework to attempt to classify different registration components and algorithms in terms of their responses to the initial conditions. Performances would be evaluated on synthetic data, multitemporal and multisensor data. All results of the study would be presented at the conference and would be useful for two different purposes: (1) provide automatic quality assessment of the geolocation of remote sensing data by performing interalgorithm consistency studies; and (2) be the foundations for the design of future on-board applications including planetary exploration.

The study of global environmental changes involves the comparison, fusion, and integration of multiple types of remotely-sensed data at various temporal, radiometric, and spatial resolutions. Results of this integration may be utilized for global change analysis, as well as for the validation of new instruments or for new data analysis. Furthermore, future multiple satellite missions will include many different sensors carried on separate platforms, and the amount of remote sensing data to be combined is increasing tremendously. For all of these applications, the first requires step is fast and automatic registration, and as this need for automating registration techniques is being recognized, it becomes necessary to survey all the registration methods which may be applicable to Earth and space science problems and to evaluate their performances on a large variety of existing remote sensing data as well as on simulated data of soon-to-be-flown instruments. In this paper we present one of the first steps toward such as exhaustive quantitative evaluation. First, the different components of image registration algorithms are reviewed, and different choices for each of these components are described. Then, the results of the evaluation of the corresponding algorithms combing these components are described. Then, the results of the evaluation of the corresponding algorithms combining these components are presented on several datasets. The algorithms are based on gray levels or wavelet features and compute rigid transformations (including scale, rotation, and shifts). Test datasets include synthetic data as well as data acquired over several EOS Land Validation Core Sites with the IKONOS and the Landsat-7 sensors.

The authors present an algorithm for the calibration of a stereo system with small relative angles in an uncontrolled environment. This algorithm has two advantages: (1) it is more accurate than the existing algorithms in the computer vision and photogrammetry literatures; and (2) it provides useful insight into the problem of camera calibration and relative orientation. This is done by deriving explicit analytical solutions for the relative pan, tilt, and roll angles in terms of the world pan angle (gaze angle) and the coordinates of the feature points used in their computations. These solutions allow a better understanding of the problem of calibration in general by providing insight as to how errors due to quantization and uncertainty in the location of image centers affect the computation of rotation angles. The findings are likely to be of use even when the relative rotation angles are not small.>

The Computer Vision Laboratory at the University of Maryland is designing and developing a vision system for autonomous ground navigation. Our approach to visual navigation segments the task into three levels called long range, intermediate range and short range navigation. At the long range, one would first generate a plan for the day's outing, identifying the starting location, the goal, and a low resolution path for moving from the start to the goal. From time to time, during the course of the outing, one may want to establish his position with respect to the long range plan. This could be accomplished by visually identifying landmarks of known location, and then triangulating to determine current position. We describe a vision system for position determination that we have developed as part of this project. At the intermediate range, one would look ahead to determine generally safe directions of travel called corridors of free space. Short range navigation is the process that, based on a detailed topographic analysis of one's immediate environment, enables us to safely navigate around obstacles in the current corridor of free space along a track of safe passage. We describe a quadtree based path planning algorithm which could serve as the basis for identifying such tracks of safe passage.