Geometric Constraints for Object Detection and Delineation


Book Description

The ability to extract generic 3D objects from images is a crucial step towards automation of a variety of problems in cartographic database compilation, industrial inspection and assembly, and autonomous navigation. Many of these problem domains do not have strong constraints on object shape or scene content, presenting serious obstacles for the development of robust object detection and delineation techniques. Geometric Constraints for Object Detection and Delineation addresses these problems with a suite of novel methods and techniques for detecting and delineating generic objects in images of complex scenes, and applies them to the specific task of building detection and delineation from monocular aerial imagery. PIVOT, the fully automated system implementing these techniques, is quantitatively evaluated on 83 images covering 18 test scenes, and compared to three existing systems for building extraction. The results highlight the performance improvements possible with rigorous photogrammetric camera modeling, primitive-based object representations, and geometric constraints derived from their combination. PIVOT's performance illustrates the implications of a clearly articulated set of philosophical principles, taking a significant step towards automatic detection and delineation of 3D objects in real-world environments. Geometric Constraints for Object Detection and Delineation is suitable as a textbook or as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.




Projective Geometry and Photometry for Object Detection and Delineation


Book Description

Abstract: "Computer vision systems have traditionally performed most effectively in constrained situations, where limitations on object shape or scene structure permit reliable image analysis. For example, in model-based recognition systems, the existence of 3D models of objects of interest allows the application of geometric constraints to limit the search for interpretations of low-level image information. Many problem domains, however, do not have explicit constraints on object shape or scene content. In aerial image analysis, man-made structures take on a wide variety of shapes and sizes. Existing techniques for these domains obtain only partial solutions by the use of simplifying assumptions about imaging geometry, illumination conditions, and object shape. Few of these techniques attempt to model perspective or photometric effects, which can be powerful constraints for object detection and delineation. The central hypothesis of this work, that rigorous modeling of the image acquisition process leads to improved detection and delineation of basic volumetric forms for object recognition, leads to the formulation of a set of principles for object detection and delineation. In accordance with these principles, a fully automated monocular image analysis system, PIVOT, was developed for the task domain of cartographic building extraction from aerial imagery, using original techniques for vanishing point detection, intermediate feature generation, hypothesis generation, and building model verification. A quantitative comparative evaluation methodology for object detection and delineation is presented in this work, using unbiased image space and object space performance metrics on large datasets of imagery. Using this methodology, PIVOT was compared to three existing building extraction systems on 83 test images covering a wide variety of geographical areas, object complexities, and viewing angles. This analysis demonstrates PIVOT's improved performance from highly oblique viewpoints and on complex manmade structures, establishing the utility of rigorous camera modeling for object detection and delineation tasks, and in particular its importance for the automated population of spatial databases with cartographically accurate three-dimensional models."




Projective Geometry and Photometry for Object Detection and Delineation


Book Description

Abstract: "Computer vision systems have traditionally performed most effectively in constrained situations, where limitations on object shape or scene structure permit reliable image analysis. For example, in model-based recognition systems, the existence of 3D models of objects of interest allows the application of geometric constraints to limit the search for interpretations of low-level image information. Many problem domains, however, do not have explicit constraints on object shape or scene content. In aerial image analysis, man-made structures take on a wide variety of shapes and sizes. Existing techniques for these domains obtain only partial solutions by the use of simplifying assumptions about imaging geometry, illumination conditions, and object shape. Few of these techniques attempt to model perspective or photometric effects, which can be powerful constraints for object detection and delineation. The central hypothesis of this work, that rigorous modeling of the image acquisition process leads to improved detection and delineation of basic volumetric forms for object recognition, leads to the formulation of a set of principles for object detection and delineation. In accordance with these principles, a fully automated monocular image analysis system, PIVOT, was developed for the task domain of cartographic building extraction from aerial imagery, using original techniques for vanishing point detection, intermediate feature generation, hypothesis generation, and building model verification. A quantitative comparative evaluation methodology for object detection and delineation is presented in this work, using unbiased image space and object space performance metrics on large datasets of imagery. Using this methodology, PIVOT was compared to three existing building extraction systems on 83 test images covering a wide variety of geographical areas, object complexities, and viewing angles. This analysis demonstrates PIVOT's improved performance from highly oblique viewpoints and on complex manmade structures, establishing the utility of rigorous camera modeling for object detection and delineation tasks, and in particular its importance for the automated population of spatial databases with cartographically accurate three-dimensional models."




Automatic Extraction of Man-made Objects from Aerial and Satellite Images III


Book Description

This work is a collection of papers from the world's leading research groups in the field of automatic extraction of objects, especially buildings and roads, from aerial and space imagery, including new sensors like SAR and lidar.




Introduction to Modern Photogrammetry


Book Description

This text is designed to give students a strong grounding in the mathematical basis of photogrammetry while introducing them to related fields, such as remote sensing and digital image processing. Suitable for undergraduate photogrammetry courses typically aimed at junior and senior students, and for graduate-level courses at the Master's level. Excellent reference for those working in related fields.




Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book


Book Description

Published on the occasion of the XXIst Congress of the International Society for Photogrammetry and Remote Sensing (ISPRS) in Beiijng, China in 2008, Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book is a compilation of 34 contributions from 62 researchers active within the ISPRS. The book covers







Discrete Geometry for Computer Imagery


Book Description

This book constitutes the thoroughly refereed proceedings of the 21st IAPR International Conference on Discrete Geometry for Computer Imagery, DGCI 2019, held in Marne-la-Vallée, France, in March 2019. The 38 full papers were carefully selected from 50 submissions. The papers are organized in topical sections on discrete geometric models and transforms; discrete topology; graph-based models, analysis and segmentation; mathematical morphology; shape representation, recognition and analysis; and geometric computation.