Opening the Neural Network Black Box: An Algorithm for Extracting Rules from Function Approximating Artificial Neural Networks

Artificial neural networks have been successfully applied to solve a variety of business applications involving classification and function approximation. In many such applications, it is desirable to extract knowledge from trained neural networks so that the users can gain a better understanding of the solution. Existing research has focused primarily on extracting symbolic rules for classification problems with few methods devised for function approximation problems. In order to fill this gap, we propose an approach to extract rules from neural networks that have been trained to solve function approximation problems. The extracted rules divide the data samples into groups. For all samples within a group, a linear function of the relevant input attributes of the data approximates the network output. Experimental results show that the proposed approach generates rules that are more accurate than the existing methods based on decision trees and regression

SKIN SEGMENTATION AND SKULL SEGMENTATION FOR MEDICAL IMAGING

In this paper aims we present tools for medical imaging applications to do skin and skull segmentation in a short time. The desired output for skin segmentation is a 3D visualization of the facial skin without any cavities or holes inside the head, while skull segmentation aims to create a 3D visualization of the skull bones. The algorithm used for skin segmentation is thresholding the image, extracting the largest connected component, and holefilling to fill the unnecessary holes. As for the skull segmentation, the process is done by removing the spines which is connected to the skull, and then extracting the largest connected component. Afterwards, mesh generation is done to produce the 3D objects from the processed images. This mesh generation process is done using the marching cubes algorithm. The testing results show that the skin and skull segmentation process will work well when there are no other objects that are connected to the skin or the skull. Skin segmentation process takes a significant amount of time, primarily caused by the holefilling process

Real-time Mosaic for Multi-Camera Videoconferencing

This paper describes a system for high resolution video conferencing. A number of camcorders are used to capture the video, which are then mosaiced to generate a wide angle panoramic view. Furthermore this system is made “real-time” by detecting changes and updating them on the mosaic. This system can be deployed on a single machine or on a cluster for better performance. It is also scalable and shows a good real-time performance. The main application for this system is videoconferencing for distance learning but it can be used for any high resolution broadcasting.Singapore-MIT Alliance (SMA

Spatiotemporal memory that separates `what' and `when'

Proceedings of the International Joint Conference on Neural Networks64090-409485OF

ON PATTERN DESCRIPTION AND ANALYSIS FOR SYNTACTIC PATTERN RECOGNITION WITH APPLICATION TO CHINESE CHARACTER RECOGNITION

Master'sMASTER OF SCIENC

Accurate and True Reconstruction of 3D Models with Closed-Form Solutions

3D models of objects or scenes can be reconstructed from image sequences or multiple views. Reconstruction algorithms that adopt calibrated cameras generally produce more accurate results than those that adopt uncalibrated cameras, whereas the latter are more convenient to use. Depending on an application's need, one may trade off between reconstruction accuracy and convenience. The objective of this research is to devise an accurate method for reconstructing the true and complete 3D models of small objects for use in applications such as virtual reality and model-based object recognition. This technical report focuses on the task of accurately recovering 3D coordinates of world points given a set of tracked 2D image points. To ensure reconstruction accuracy, the method uses a calibrated camera and a computer-controlled turntable. With this equipment setup, close-form solutions to the reconstruction problem are derived for various camera models including the perspective and the project..