David M. Doria

Analysis of regularization edge detection in image processing

Regularization is a paradigm for performing image segmentation and edge detection, that can be implemented in a neural network type architecture. Various topics and problems pertaining to the use of regularization for image processing applications are discussed. Topics include data fusion, sensor blur, and the operation on partitioned images. A mathematical analysis of the different topics is presented, including a modification of the original regularization energy functional to perform data fusion.

Sensor to Shooter Information Fusion for Rapid Targeting (SSIFRT)

This paper describes the sensor to shooter information fusion for rapid targeting program. The objective of this program is to design, develop, test, and demonstrate the fusion of intelligence, surveillance, and reconnaissance data with on-board sensor data. This decentralized information fusion system will take advantage of both on- board tactical platform and off-board sensor data to generate a high performance identification capability. The algorithm development will address Automatic Target Recognition, ground target tracking, target cueing, and registration of imagery residing on both ground state (off- board) and tactical aircraft (on-board) systems. Analysis of data link and processing requirements/capabilities will be performed to determine an on-board and off-board fusion architecture.

Feature distributions and probability of identification

In this paper we present a method of analysis of model based automatic target recognition (ATR) algorithms, as a function of a number of important parameters of the system, including the number and size of the models, the correlations between models, the expected probability of detection of features, the rates of occurrence of unpredicted features, and the spatial resolution of the predicted features, as defined by a local spatial feature density. Analytical results for a two class problem are presented as a function of between-class correlation and feature localization accuracy.

Neural network ATR indexing system

We have developed a novel neural network based automatic target recognition (ATR) indexing system. This system utilizes regularization edge detection, adaptive vector quantization (AVQ) clustering, model driven feedback, and backpropagation trained networks. It can be designed to be invariant to either translation, or translation and rotation. The system incorporates both top-down and bottom-up processing to suppress background clutter.

Using model-driven feedback in neural network object recognition

In this paper we deal with the problem of edge extraction for the purpose of matching to a known model or set of models. We describe an approach to using geometric model based information within a feedback system, without the requirement for prior pose estimation by a matching process. We call this process model driven feedback (MDF). The feedback system uses a chord based transform of the image edges that is invariant either to translation or both translation and rotation, depending on its form. By representing both the data and model information using a geometrically invariant transform, and iteratively minimizing a function of the differences between the model and data transforms, the system is able to eliminate background edges while retaining object edges that are similar in shape to the model.