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Automatic Extraction of Man-Made Objects from Aerial Space Images : Monte Verita - Armin Gruen

Automatic Extraction of Man-Made Objects from Aerial Space Images

Monte Verita

By: Armin Gruen (Editor), Olaf Kuebler (Editor), Peggy Agouris (Editor)

Hardcover Published: 28th August 1995
ISBN: 9783764352646
Number Of Pages: 323

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Advancements in digital sensor technology, digital image analysis techniques, as well as computer software and hardware have brought together the fields of computer vision and photogrammetry, which are now converging towards sharing, to a great extent, objectives and algorithms. The potential for mutual benefits by the close collaboration and interaction of these two disciplines is great, as photogrammetric know-how can be aided by the most recent image analysis developments in computer vision, while modern quantitative photogrammetric approaches can support computer vision activities. Devising methodologies for automating the extraction of man-made objects (e.g. buildings, roads) from digital aerial or satellite imagery is an application where this cooperation and mutual support is already reaping benefits. The valuable spatial information collected using these interdisciplinary techniques is of improved qualitative and quantitative accuracy. This book offers a comprehensive selection of high-quality and in-depth contributions from world-wide leading research institutions, treating theoretical as well as implementational issues, and representing the state-of-the-art on this subject among the photogrammetric and computer vision communities.

Using Context to Control Computer Vision Algorithmsp. 3
Inferring Homogeneous Regions from Rich Image Attributesp. 13
Stereo Reconstruction from Multiple Viewsp. 23
Model Registration and Validationp. 33
Automatic Matching of 3-D Models to Imageryp. 43
Structural 3D-Analysis of Aerial Images with a Blackboard-Based Production Systemp. 53
Semi-Automatic Detection and Extraction of Man-Made Objects in Multispectral Aerial and Satellite Imagesp. 63
Information Extraction from Digital Images - A KTH Approachp. 73
Linear Feature Extraction with Dynamic Programming and Globally Enforced Least Squares Matchingp. 83
Semi-Automatic Feature Extraction by Snakesp. 95
From Ziplock Snakes to Velcro Surfacesp. 105
A Layered Abduction Model of Building Recognitionp. 117
Detection of Buildings from Monocular Imagesp. 125
High-Resolution Stereo for the Detection of Buildingsp. 135
3-D Reconstruction of Urban Scenes from Sequences of Imagesp. 145
Automatic Extraction of Buildings and Terrain from Aerial Imagesp. 169
Mid-Level Vision Processes for Automatic Building Extractionp. 179
Geometric Versus Texture Detail in 3-D Models of Real World Buildingsp. 189
Use of DTMs/DSMs and Orthoimages to Support Building Extractionp. 199
Data Fusion for the Detection and Reconstruction of Buildingsp. 211
Building Extraction and Verification from Spaceborne and Aerial Imagery Using Image Understanding Fusion Techniquesp. 221
Building Extraction from Stereo Pairs of Aerial Images: Accuracy and Productivity Constraint of a Topographic Production Linep. 231
Tracking Roads in Satellite Images by Playing Twenty Questionsp. 243
New Geometric Stochastic Technology for Finding and Recognizing Roads and Their Features in Aerial Imagesp. 255
Road Tracing by Profile Matching and Kalman Filteringp. 265
Model-Based Road Extraction from Imagesp. 275
Automatic Extraction and Structuring of Objects from Scanned Topographical Maps - An Alternative to the Extraction from Aerial and Space Images?p. 287
Cooperative Use of Aerial Images and Maps for the Interpretation of Urban Scenesp. 297
Map-Based Semantic Modeling for the Extraction of Objects from Aerial Imagesp. 307
List of Workshop Participantsp. 317
Author Indexp. 321
Table of Contents provided by Blackwell. All Rights Reserved.

ISBN: 9783764352646
ISBN-10: 3764352647
Series: Monte Verita
Audience: General
Format: Hardcover
Language: English
Number Of Pages: 323
Published: 28th August 1995
Country of Publication: CH
Dimensions (cm): 23.39 x 15.6  x 2.06
Weight (kg): 0.65

Earn 542 Qantas Points
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