Model-based object recognition has become a popular paradigm in computer vision research. In most of the current model-based vision systems, the object models used for recognition are generally a priori given (e.g. obtained using a CAD model). For many object recognition applications, it is not realistic to utilize a fixed object model database with static model features. Rather, it is desirable to have a recognition system capable of performing automated object model acquisition and refinement. In order to achieve these capabilities, we have developed a system called ORACLE: Object Recognition Accomplished through Consolidated Learning Expertise. It uses two machine learning techniques known as Explanation-Based Learning (EBL) and Structured Conceptual Clustering (SCC) combined in a synergistic manner. As compared to systems which learn from numerous positive and negative examples, EBL allows the generalization of object model descriptions from a single example. Using these generalized descriptions, SCC constructs an efficient classification tree which is incremently built and modified over time. Learning from experience is used to dynamically update the specific feature values of each object. These capabilities provide a dynamic object model database which allows the system to exhibit improved performance over time. We provide an overview of the ORACLE system and present experimental results using a database of thirty aircraft models.

Image segmentation is an old and difficult problem. One of the fundamental weaknesses of current computer vision systems to be used in practical applications is their inability to adapt the segmentation process as real-world changes occur in the image. We present the first closed loop image segmentation system which incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions such as time of day, time of year, clouds, etc. The segmentation problem is formulated as an optimization problem and the genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. The goals of our adaptive image segmentation system are to provide continuous adaptation to normal environmental variations, to exhibit learning capabilities, and to provide robust performance when interacting with a dynamic environment. We present experimental results which demonstrate learning and the ability to adapt the segmentation performance in outdoor color imagery.

A closed-loop image segmentation system that incorporates a genetic algorithm to adapt the segmentation process to changes in image characteristics caused by variable environmental conditions is presented. The genetic algorithm efficiently searches the hyperspace of segmentation parameter combinations to determine the parameter set which maximizes the segmentation quality criteria. A summary of the experimental results that demonstrates the ability to perform adaptive image segmentation and to learn from experience using a collection of outdoor color imagery is given.>

A maximally passive approach to obstacle detection is described, and the details of an inertial sensor integrated optical flow analysis technique are discussed. The optical flow algorithm has been used to generate range samples using both synthetic data and real data (imagery and inertial navigation system information) obtained from a moving vehicle. The conditions under which the data were created/collected are described, and images illustrating the results of the major steps in the optical flow algorithm are provided.>

Clustering techniques have been used to perform image segmentation, to detect lines and curves in the images and to solve several other problems in pattern recognition and image analysis. In this paper we apply clustering methods to a new problem domain and present a new method based on a cluster-structure paradigm for the recognition of 2-D partially occluded objects. The cluster-structure paradigm entails the application of clustering concepts in a hierarchical manner. The amount of computational effort decreases as the recognition algorithm progresses. As compared to some of the earlier methods, which identify an object based on only one sequence of matched segments, the new technique allows the identification of all parts of the model which match with the apparent object. Also the method is able to tolerate a moderate change in scale and a significant amount of shape distortion arising as a result of segmentation and/or the polygonal approximation of the boundary of the object. The method has been evaluated with respect to a large number of examples where several objects partially occlude one another. A summary of the results is presented.