UUnniivveerrssiittyy ooff CCeennttrraall FFlloorriiddaa SSTTAARRSS Electronic Theses and Dissertations, 2004-2019 2005 CCrreeaattiinngg GGeeoo--ssppeecciifificc RRooaadd DDaattaabbaasseess FFrroomm AAeerriiaall PPhhoottooss FFoorr DDrriivviinngg SSiimmuullaattiioonn Dahai Guo University of Central Florida Part of the Computer Engineering Commons Find similar works at: https://stars.library.ucf.edu/etd University of Central Florida Libraries http://library.ucf.edu This Doctoral Dissertation (Open Access) is brought to you for free and open access by STARS. It has been accepted for inclusion in Electronic Theses and Dissertations, 2004-2019 by an authorized administrator of STARS. For more information, please contact [email protected]. SSTTAARRSS CCiittaattiioonn Guo, Dahai, "Creating Geo-specific Road Databases From Aerial Photos For Driving Simulation" (2005). Electronic Theses and Dissertations, 2004-2019. 447. https://stars.library.ucf.edu/etd/447 Creating Geo-Specific Road Databases from Aerial Photos for Driving Simulation by DAHAI GUO M.S. University of Central Florida, 2001 A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Electrical and Computer Engineering in the College of Engineering and Computer Science at the University of Central Florida Orlando, Florida Summer Term 2005 Major Professors: Harold I. Klee and Arthur R. Weeks © 2005 Dahai Guo ii ABSTRACT Geo-specific road database development is important to a driving simulation system and a very labor intensive process. Road databases for driving simulation need high resolution and accuracy. Even though commercial software is available on the market, a lot of manual work still has to be done when the road crosssectional profile is not uniform. This research deals with geo-specific road databases development, especially for roads with non-uniform cross sections. In this research, the United States Geographical Survey (USGS) road information is used with aerial photos to accurately extract road boundaries, using image segmentation and data compression techniques. Image segmentation plays an important role in extracting road boundary information. There are numerous methods developed for image segmentation. Six methods have been tried for the purpose of road image segmentation. The major problems with road segmentation are due to the large variety of road appearances and the many linear features in roads. A method that does not require a database of sample images is desired. Furthermore, this method should be able to handle the complexity of road appearances. The proposed method for road segmentation is based on the mean-shift clustering algorithm and it yields a high accuracy. In the phase of building road databases and visual databases based on road segmentation results, the Linde-Buzo-Gray (LBG) vector quantization algorithm is used to identify repeatable cross section profiles. In the phase of texture mapping, five major uniform textures are considered - pavement, white marker, yellow marker, concrete and grass. They are automatically mapped to polygons. In the chapter of results, snapshots of road/visual database are presented. iii I dedicate this dissertation to my beloved wife, Jiaying, who has been giving me tremendous support during my graduate studies. I also want to thank Dr. Klee, Dr. Weeks, Dr. Bauer, and Dr. Radwan. They brought me to the field of driving simulation and have given a lot of instructions. Dr. Foroosh is appreciated for his service in my committee. iv TABLE OF CONTENTS LIST OF TABLES........................................................................................................................xii LIST OF ACRONYMS/ABBREVIATIONS..............................................................................xiii CHAPTER ONE: INTRODUCTION.............................................................................................1 CHAPTER TWO: LITERATION REVIEW..................................................................................4 2.1 Road Extraction from Aerial Photos.....................................................................................4 2.2 Image Segmentation Algorithms........................................................................................12 2.2.1 Edge Detection and Grouping......................................................................................12 2.2.2 Textured-Based Image Segmentation By Comparing Histograms..............................14 2.2.3 Feature-Based Comparison Using EM Method...........................................................15 2.2.4 Region-Growing Algorithm for Image Segmentation.................................................17 2.2.5 Active Contour Model.................................................................................................18 2.2.6 Mean-Shift Clustering Algorithm for Image Segmentation........................................19 2.4 Introduction to USGS Data.................................................................................................21 2.5 STS Road Database Formats..............................................................................................22 CHAPTER THREE: EXPERIMENTS OF ROAD SEGMENTATION......................................24 3.1 Edge Detection and Edge Linking......................................................................................24 3.2 Texture-Based Segmentation Using the Histogram Intersection Method..........................29 3.3 Feature-Based Image Segmentation Using EM Method....................................................36 3.4 Region-Growing Image Segmentation...............................................................................43 3.5 Growing Active Contour Model.........................................................................................47 3.6 Mean-Shift Clustering Algorithm.......................................................................................49 v CHAPTER FOUR: METHODOLOGY.......................................................................................55 4.1 Matching DLG Information with Aerial Photos.................................................................55 4.2 Post Processing the Segmentation Results by the Mean-Shift Clustering Method............58 4.3 Conditional Morphological Methods..................................................................................59 4.4 Removing Shadows and Occlusions...................................................................................61 4.5 Manual Adjustment.............................................................................................................62 4.6 LBG Algorithm...................................................................................................................64 4.7Automatically Creating 3D Road Model.............................................................................67 4.8 Summary of the Methodology............................................................................................69 CHAPTER FIVE: RESULTS.......................................................................................................71 5.1 The Results of Saturation-Based Segmentation..................................................................71 5.2 The Results of Conditional Morphological Operations......................................................76 5.3 The Results of Shadow and Occlusion Removal................................................................79 5.4 The Accuracy of Road Segmentation.................................................................................79 5.5 The Results of Manually Adjusted Road Boundaries.........................................................80 5.6 Results of the LBG Algorithm............................................................................................83 5.7 Results of Building Visual Databases.................................................................................85 5.8 Limitations of the Proposed Methodology.........................................................................90 5.8.1 Image Segmentation Problems....................................................................................90 5.8.2 High Resolution Elevation Information Not Available...............................................92 5.8.3 Less Information for Building a Complete 3D Road Modeling..................................93 CHAPTER SIX: FUTURE WORKS............................................................................................94 CHAPTER SEVEN: CONCLUSION...........................................................................................95 vi LIST OF REFERENCES..............................................................................................................99 vii LIST OF FIGURES Fig. 1 STS Road Databases Requirements...................................................................................22 Fig. 2 The Boundary for Judge Right Connections......................................................................25 Fig. 3 Examples of Connecting Two Edges With Same Parallel Partner.....................................26 Fig. 4 Results of Edge Detection and Grouping (1)......................................................................27 Fig. 5 Results of Edge Detection and Grouping (2)......................................................................28 Fig. 6 Linear Features As Noises..................................................................................................29 Fig. 7 Original, Color Ratio Gradient, and Hue Gradient Images (1)..........................................30 Fig. 8 Original, Color Ratio Gradient, and Hue Gradient Images (2)..........................................31 Fig. 9 Sample Images....................................................................................................................31 Fig. 10 Texture-Based Segmentation Using the Histogram Intersection Method........................32 Fig. 11 Improved Edge Grouping.................................................................................................34 Fig. 12 Example of Failing Texture-Based Segmentation (1)......................................................34 Fig. 13 Example of Failing Texture-Based Segmentation (2)......................................................35 Fig. 14 Results of Feature-Based Segmentation Using EM Method (1)......................................38 Fig. 15 Results of Feature-Based Segmentation Using EM Method (2)......................................39 Fig. 16 Results of Feature-Based Segmentation Using EM Method (3)......................................40 Fig. 17 Results of Feature-Based Segmentation Using EM Method (4)......................................41 Fig. 18 Results of Region-Growing Method (1)...........................................................................45 Fig. 19 Results of Region-Growing Method (2)...........................................................................45 Fig. 20 Results of Region-Growing Method (3)...........................................................................46 Fig. 21 Results of Region-Growing Method (4)...........................................................................46 viii Fig. 22 Results of An Active Contour Method.............................................................................48 Fig. 23 Result of the Mean-Shift Clustering Algorithm (1)..........................................................50 Fig. 24 Result of the Mean-Shift Clustering Algorithm (2)..........................................................50 Fig. 25 Result of the Mean-Shift Clustering Algorithm (3)..........................................................50 Fig. 27 Result of the Mean-Shift Clustering Algorithm (5)..........................................................51 Fig. 28 Result of the Mean-Shift Clustering Algorithm (6)..........................................................51 Fig. 29 Result of the Mean-Shift Clustering Algorithm (7)..........................................................52 Fig. 30 Result of the Mean-Shift Clustering Algorithm (8)..........................................................53 Fig. 31 Sizes of Extracted Objects................................................................................................54 Fig. 32 Linear and Non-Linear Distortions...................................................................................55 Fig. 33 The Graph of Non-Linear Distortion Measurement.........................................................57 Fig. 34 Matching of the USGS DLG and UCF Aerial Photos......................................................58 Fig. 35 The Number of Non-Pavement Pixels vs. Lateral Offset.................................................60 Fig. 36 Inserting a Point................................................................................................................63 Fig. 37 Connecting Two Objects..................................................................................................63 Fig. 38 Results of a Manual Adjustment......................................................................................63 Fig. 39 Transforming Cross Sections Top Views in to True Profiles...........................................64 Fig. 40 Texture Mapping in Road Modeling................................................................................68 Fig. 41 Flow Chart of the Proposed Method.................................................................................70 Fig. 42 Examples of Saturation Images........................................................................................71 Fig. 43 Saturation Value in a Descending Order..........................................................................72 Fig. 44 Result of Segmentation after Using Saturation as a Cue (1)............................................73 Fig. 45 Result of Segmentation after Using Saturation as a Cue (2)............................................73 ix
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