Hatem N. Nasr

Landmark recognition for autonomous mobile robots

A novel approach for landmark recognition based on the perception, reasoning, action, and expectation (PREACTE) paradigm is presented for the navigation of autonomous mobile robots. PREACTE uses expectations to predict the appearance and disappearance of objects, thereby reducing computational complexity and locational uncertainty. It uses an innovative concept called dynamic model matching (DMM), which is based on the automatic generation of landmark description at different ranges and aspect angles and uses explicit knowledge about maps and landmarks. Map information is used to generate an expected site model (ESM) for search delimitation, given the location and velocity of the mobile robot. The landmark recognition vision system generates 2-D and 3-D scene models from the observed scene. The ESM hypotheses are verified by matching them to the image model. Experimental results that verify the performance of the PREACTE and DMM algorithms for real imagery are also presented.<<ETX>>

Knowledge- and model-based automatic target recognition algorithm adaptation

One ofthe most critical problems in automatic target recognition (ATR) systems is multiscenario adaptation. Todays ATR systems perform unpredictably, i.e., perform well in certain scenarios and poorly in others. Unless ATR systems can be made adaptable, their utility in battlefield missions remains questionable. We have developed a novel method called knowledge- and model-based algorithm adaptation (KMBAA). KMBAA automatically adapts the ATR parameters as the scenario changes so that ATR can maintain optimum performance. The KMBAA approach has been tested with a nonreal-time ATR simulation system and has demonstrated a significant improvement in detection, false alarm rate reduction, and segmentation accuracy performance.

The importance of sensor models to model-based vision applications

This paper considers the importance of sensor models to model-based recognition applications. The impact of the explicit representation of sensor models and of the sensor attributes themselves (e.g., the particular geometric transformation, whether active or passive, specular or diffuse, reflective or emissive) are illustrated using synthetic aperture radar, infrared, and CO2 laser radar target recognition examples. The model-based recognition problem is formalized using probability theory to partition the recognition process into (1) an estimation where the situation parameters (e.g., sensor, target, background) are estimated and (2) a hypothesis test where the current hypothesis (i.e., the constrained model given the estimated parameter values) is tested based on the sensed data. It is shown that strong sensor models suggest problem structure that can be exploited to develop robust indexing and model refinement / parameter estimation algorithms. It is also shown that strong sensor models form a basis for rigorous match evaluation during the hypothesis test phase of the recognition process.

Automatic Target Recognition Algorithm Performance Evaluation: The Bottleneck In The Development Life Cycle

A current critical problem in Automatic Target Recognition (ATR) technology is the inability to effectively evaluate ATR systems performance and component algorithms. Beyond the problem of evaluating system performance, it is often impossible to determine why a system is performing poorly under certain circumstances. This is largely due to the relatively unsophisticated tools and methods currently employed to extract and analyze the vast quantities of data processed by such systems. The amount of time and effort dedicated to testing, evaluation, and refinement of complex ATR systems and their component algorithm is by far the longest stage in the life cycle of the development process. Adequate tools, techniques, standards, and groundtruthing are critically needed for effective diagnostics and evaluation of next generation ATR systems. In this paper, we present a thesis on the critical issues of ATR evaluation and potential solutions.

Refocused Recognition Of Aerial Photographs At Multiple Resolution

While performing the photo interpretation task using very high resolution images, the resolution of the image is often reduced to make its processing feasible. However, in low resolution images, it becomes quite difficult to segment and locate targets of interest such as aircraft, which are relatively small. Further, in recognizing aircraft, it is generally assumed that aircraft are already located. The emphasis is placed on model matching for recognizing isolated aircraft. However, locating potential areas in the images, where aircraft may be found, is non-trivial since it requires an accurate labeling of an image. We have developed a Knowledge-Based Photo Interpretation (KEPI) system that analyzes high resolution images. This system locates aircraft by first finding large structures in low resolution images and focusing attention on areas such as tarmacs, runways, parking areas, that have high probability of containing aircraft. Higher resolution images of the regions that are the focus of attention are used in subsequent analysis. The system makes extensive use of contextual knowledge such as spatial and locational information about airport scenes. We show results using high resolution TV data.

Contextual image understanding of airport photographs

While performing the photo interpretation task using very high resolution images, the resolution of the image is often reduced to make its processing feasible. However, in low resolution images, it becomes quite difficult to segment and locate targets of interest such as aircraft, which are relatively small. Further, in recognizing aircraft, it is generally assumed that aircraft are already located. The emphasis is placed on model matching for recognizing isolated aircraft. However, locating potential areas in the images, where aircraft may be found, is non-trivial since it requires an accurate labeling of an image. We have developed a Knowledge-Based Photo Interpretation (KBPI) system that analyzes high resolution images. This system locates aircraft by first finding large structures in low resolution images and focusing attention on areas such as tarmacs, runways, parking areas, that have high probability of containing aircraft. Higher resolution images of the regions that are the focus of attention are used in subsequent analysis. The system makes extensive use of contextual knowledge such as spatial and locational information about airport scenes. We show results using high resolution TV data.

Development of an electronic terrain board as a processor test and evaluation tool

Current development, training, and testing of automatic target recognizers/cuers relies almost exclusively on image data taken at field sites or from physical terrain boards. Each of these approaches has several advantages as well as disadvantages. For example, field test data are severely limited in the variety of terrain and targets typically available. In addition, the environment is too variable to support parametric testing of processors. On the other hand, the targets and their signatures are real as is atmospheric attenuation, sensor settings, sensor artifacts, etc. In contrast, the physical terrain board is highly controllable and is ideally suited for parametric studies of processors. However, the physical terrain boards are simulations of targets and backgrounds and typically do not include the important contributions of sensor-specific noise or atmospheric attenuation on target signatures. More importantly, physical terrain boards have not yet incorporated a method for multi-sensor testing. This paper will describe in detail the advantages and disadvantages of field and physical terrain board testing and will present the concept of a digital terrain board that addresses many of the limitations of previous approaches while not sacrificing their advantages. Specific approaches will be discussed and preliminary results of testing processors with several gradations of synthetic imagery will be presented.

Automated, instrumentation, evaluation and diagnostics of automatic target recognition systems

Adequate tools for diagnosis and evaluation of Automatic Target Recognition (ATR) systems are very critical for their successful development. In this paper we describe system called: Automated Instrumentation and Evaluation (Auto-I). Auto-I provides many of the needed capabilities for rapid testing and evaluation of ATR systems. It also provides a module for automatic adaptation of algorithms parameters using algorithms performance models, optimization and Artificial Intelligence techniques. The current design of Auto-I is modular, it is designed so it can be interfaced to other ATR systems .

A Technique for Automatic Design of Image Segmentation Algorithms

Image segmentation is an essential step in every practical image processing system. Current image segmentation algorithms suffer from a well known problem- They have poor performances on images different from the ones that were used in their initial development and training stages. In this paper we discuss a system concept for automatic design of segmentation algorithms based on image and objects metrics, and knowledge of image processing primitives.The proposed system concept makes makes use of Planning techniques, discussed in Artificial Intelligence.This paper provides the essential elements of the next generation Automatic Segmentation Design (ASD) systems that will not be scenario dependent. Applications of this concept include robotic vision, automatic target recognition, and image understanding systems.

ATR performance modeling for building multiscenario adaptive systems

Modeling Automatic Target Recognition (ATR) system performance is important for a number of reasons. Many of these reasons have to do with the fact that performance models can enhance the ability to predict the ATR system performance in scenarios where data is not available. However, a critical use of ATR performance models that has not been explored until recently is the adaptation of the ATR system parameters. A system has been developed in recent years called Knowledge and Model-Based Algorithm Adaptation (KMBAA) for automatic ATR parameters adaptation. KMBAA has shown tremendous success in its ability to adapt ATR parameters and enhance the ATR system performance. KMBAA relies heavily on the use of complex ATR performance models. These models relate a number of ATR performance measures, such as probability of detection, to a number of ATR critical parameters, such as bright thresholds, and image/scene metrics, such as target range. The models being used in the KMBAA systems, and the process of building such models, are discussed in this paper.