Evaluation of the fractal dimension as a pattern recognition feature using neural networks - Daponte, John S; Parikh, Jo Ann; Decker, James; Vitale, Joseph N (Proceedings of SPIE, 1993)
In the past fractal dimension has often been computed using a stochastic approach based on a random walk process, which has been found to be very time consuming. More recently, mathematical morphology has been used to compute the fractal dimension in a more timely fashion. This paper describes how the fractal dimension computed using mathematical morphology can be used in the texture analysis of ultrasonic imagery. The discriminatory ability of the fractal dimension as a pattern recognition feature is evaluated and compared to more traditional parameters. This analysis includes comparisons with statistical features in which each parameter is treated as an independent variable and in which interactions between those variables are evaluated. Pattern recognition techniques include Stepwise Discriminant Analysis, Linear Discriminant Analysis, and Nearest Neighbor Analyisis in addition to Backpropagation Neural Network Classifiers. Our results identify the fractal dimension as one of the most important parameters for distinguishing between normal and abnormal livers. In this study, consisting of 186 images, a significant statistical difference was found for both the mean and standard deviation of the fractal dimension between the normal and abnormal groups using parametric and nonparametric statistical techniques. © 1993 SPIE. All rights reserved.
0 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/113-246-779-261-025 tex.type: [object Object]
Keywords: Back propagation neural networks; Backpropagation; Discriminant analysis; Fractal dimension; Independent variables; Linear discriminant analysis; Mathematical morphology; Mean and standard deviations; Morphology; Neural networks; Pattern recognition; Pattern recognition techniques; Statistical differences; Statistical techniques; Stepwise discriminant analysis; Stochastic systems; Textures; dept.csc

Neural network solutions to logic programs with geometric constraints - Parikh, Jo Ann; Werkheiser, Anne; Subrahmanian, V S (Proceedings of SPIE, 1993)
Hybrid knowledge bases (HKBs), proposed by Nerode and Subrahmanian, provide a uniform theoretical framework for dealing with the mixed data types and multiple reasoning modes required for solving logical deployment problems. Algorithms based on mixed integer linear programming techniques have been developed for the syntactic subset of HKBs corresponding to function-free Prolog-like logic programs. In this study, we examine the ability of neural networks to solve a more comprehensive set of problems expressed within the hybrid knowledge base framework. The objective of this research is to design and implement a nonlinear optimization procedure for solving extended logic programs with neural networks. We focus upon two types of extensions which are typically required in the formulation of logical deployment problems. The first type of extension, which we shall refer to as a Type I extension, consists of embedding numerical and geometric constraints into logic programs. The second type of extension, which we shall call a Type II extension, consists of incorporating optimization problems into logic clauses. © 1993 SPIE. All rights reserved.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/006-720-998-107-50X tex.type: [object Object]
Keywords: Computer circuits; Deployment problems; Design and implements; Geometric constraint; Integer programming; Knowledge based systems; Logic programming; Mixed-integer linear programming techniques; Network solutions; Neural networks; Non-linear optimization; Nonlinear programming; Optimization problems; PROLOG (programming language); Theoretical framework; dept.csc