Knowledge Based Image Analysis
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Object-Based Image Analysis
Author: Thomas Blaschke
language: en
Publisher: Springer Science & Business Media
Release Date: 2008-08-09
This book brings together a collection of invited interdisciplinary persp- tives on the recent topic of Object-based Image Analysis (OBIA). Its c- st tent is based on select papers from the 1 OBIA International Conference held in Salzburg in July 2006, and is enriched by several invited chapters. All submissions have passed through a blind peer-review process resulting in what we believe is a timely volume of the highest scientific, theoretical and technical standards. The concept of OBIA first gained widespread interest within the GIScience (Geographic Information Science) community circa 2000, with the advent of the first commercial software for what was then termed ‘obje- oriented image analysis’. However, it is widely agreed that OBIA builds on older segmentation, edge-detection and classification concepts that have been used in remote sensing image analysis for several decades. Nevert- less, its emergence has provided a new critical bridge to spatial concepts applied in multiscale landscape analysis, Geographic Information Systems (GIS) and the synergy between image-objects and their radiometric char- teristics and analyses in Earth Observation data (EO).
Knowledge-based Image Analysis
The work reported was directed toward employing a priori knowledge in the automatic analysis of aerial imagery. Major objectives of the research were directed toward (1) map-guided registration, (2) verification of geographic data bases extracted from imagery, (3) enrichment of geographic data bases, and (4) automatic terrain feature extraction using multiple sources of knowledge and multi-level decision making. The key component in all of the work was the matching of existing iconic structure in a geographic data base (GDB) with detected image structure. By using iconic knowledge, the image interpretation paradigm becomes a three step process. First, some primitive features of the imagery must be recognized without any area-specific knowledge. Second, the imagery is aligned or registered with the knowledge base by drawing correspondences between the image features and their iconic analogues in the GDB. The matching is formalized by derivation of a transformation which maps points (x, y) of the image to points (u, v) in GDB coordinates. The final step of the process is the analysis of those parts of the image which were not successfully interpreted in steps 1 and 2. This implies a top-down search for image structures which correspond to features in the GDB. Section 2 of the report treats primitive extraction. The emphasis is currently on lineal, point, and region features only. A method for automatically inferring a rotation and translation transforming image to map is given in Section 3. Classification of registered regions is discussed in Section 4. Verification of lineal GDB features in gray-scale imagery is introduced in Section 5. (Author).
Knowledge-Based Image Processing Systems
Author: Deryn Graham
language: en
Publisher: Springer Science & Business Media
Release Date: 2012-12-06
Knowledge-based (or expert systems) and image processing have been applied to many domains but, although both fields frequently address common application areas, they are rarely applied together. Often a combined knowledge-based system and image processing approach can be highly appropriate and this book provides an insight into both areas and show students how a judicious mix of the two can result in a more effective system. The authors include detailed case studies to illustrate the two approaches as well as worked examples and solutions to problems throughout the text. Third and fourth year undergraduates and MSc students with some computer science background will find this book invaluable. Postgraduates and researchers looking for an introduction to either area - or ways to combine the two - will also welcome this clearly written and comprehensive text.