Procedural design

We present the first realistic, physically based, fully coupled, real-time weather design tool for use in urban procedural… Expand

We present a system for the lighting design of procedurally modeled buildings. The design is procedurally specified as part of… Expand

Goal-oriented requirements engineering (GORE) has been introduced as a means of modeling and understanding the motivations for… Expand

This paper presents a novel approach for the automatic creation of vegetation scenarios in real or virtual 3D cities in order to… Expand

Negotiation over conflicting interests and demands involves two separate but interacting dimensions. One dimension is given by… Expand

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, February 2004. 

This paper presents a new procedural analog design tool called PAD. It is a chart-based design environment dedicated to the… Expand

This paper presents a new approach to the use of design patterns for the reengineering of legacy code, using a mapping from… Expand

.......................................................................................................................................................... v 1 . INTRODUCTION ............................................................................................................................................ 1 PART ONE ............................................................................................................................................................ 5 2 . LATTICE MODELS ........................................................................................................................................ 7 3 . PROPOSED LATTICE MODEL ................................................................................................................. 13 3.1 MODEL SOLUTIONS .................................................................................................................................... 16 .............................................................................................................................................. 3.2 TABULATION 28 4 . MODEL IMPLEMENTATION ................................................................................................................... 41 .................................................................................................................................................. 4.1 PREAMBLE 41 .................................................................................................................................. 4.2 MODEL ~IMULATION 44 4.2.1 Monte Carlo simulation ..................................................................................................................... 44 ........................................................................................................................ 4.2.2 Construction of chain 47 4.2.3 Energy computation and the Metropolis algorithm ......................................................................... 51 4.2.4 Random number generator (RNG) .................................................................................................... 52 4.2.5 Experimental results .......................................................................................................................... 53 4.3 APPLICATION TO MULTISPECTRAL ANALYSIS .......................................................................................... 55 4.3.1 Problem description and implementation assumptions ................................................................... 55 4.3.2 Algorithm for implementation ........................................................................................................... 58 4.4 RATIONALE FOR USAGE AN EXPERIMENTAL EVIDENCE ....................................................................... 60 .......................................................................................................................... 4.4.1 Experimental design 60 4.4.2 Results interpretation ........................................................................................................................ 65 4.5 EXPERIMENTS ON REMOTE SENSING DATA .............................................................................................. 75 4.5.1 DCJightline data ........................................................................................................................... 75 ......................................................................................................................................... 4.5.2 Forest data 80 PART TWO ......................................................................................................................................................... 83 5 . INTERPRETING REMOTE SENSING DATA ........................................................................................ 85 ................................................................... 6 . A CASE STUDY . ANALYZING THE D.C. FLIGHTLINE 97 6.1 THE FUSION SUITE ..................................................................................................................................... 98 ................................................................................................... 6.1. I Maximum likelihood classification 98 ........................................................................ 6.1.2 Unsupervised segmentation using the lattice model 99 6.1.3 Fusion . HYDICE data + Digital Elevation Map (DEM) ................................................................ 99 .................................................................................................... 6.1.4 Decision tree (or graph) structure 99 ............................................................................................................................................ 6.1.5 Masking 100 6.1.6 Negative training ....................................................................................................................... 100 .................................................................................................................... 6.2 D.C. FLIGHTLINE ANALYSIS 100 ....................................................................................................... 6.2.1 Scrubbing . Removing bad data 100 6.2.2 Root Node Statistical analysis ....................................................................................................... 103 6.2.3 Node 1 Separating WATER + SHADOW ........................................................................................... 106 6.2.4 Node 2 Segmenting SHADOW .......................................................................................................... 108 .............................................................................................. 6.2.5 Node 3 Separating GRASS and TREE I10 ............................................................................................................ 6.2.6 Node 4 Extracting roofops 113 ................................................................................................ 6.2.7 Node 5 Negative training on PATH 1.21 ........................................................................ 6.2.8 Nodes 6, 7 Negative training on WATER^, WATER 124 ........................................................................ 6.2.9 Node 8 Re-assigning SHADOW (spectral method) 128 ............................................................ 6.2.10 Node 9 Negative training on classes WATER + WATER2 130 6.2.11 Node 96 Reassigning class SHADOW (spatial scheme) ............................................................... 133 6.2.12 Synopsis ........................................................................................................................................... 135 ........................................................................................................................................ BIBLIOGRAPHY 1 3 9 APPENDICES ................................................................................................................................................... 147 ....................................................................................................... . APPENDIX A T H E LATTICE MODELS 147 ............................................................................................... A . I MATLAB code for solving 2xN model 147 A.2 MATLAB code for solving 3xN model ............................................................................................... 147 ............................................ A.3 MATLAB code for generating transfer matrices for L-ary 3xN model 149 ....................................................... A.4 MATLAB code for computing magnetization M for 2xN model 149 A.5 MATLAB code for computing correlation C for 2xN model ............................................................. 150 ............................................................. A.6 MATLAB code forperjormance estimation for 2xN model 151 ............................................................................................. A . 7 Correlation calculation for test images 152 ...................................................................... A.8 Model characteristics and performance of estimators I52 GLOSSARY ....................................................................................................................................................... 159 

This study demonstrated the viability of using a changing criterion procedure for increasing the work production rates of three… Expand