Knowledge-Based Image Processing Systems
Knowledge-Based Image Processing Systems

Author: Deryn Graham

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 185

ISBN-13: 1447106350

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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.

SIGMA
SIGMA

Author: Takashi Matsuyama

Publisher: Springer Science & Business Media

Published: 2013-06-29

Total Pages: 290

ISBN-13: 1489908676

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It has long been a dream to realize machines with flexible visual perception capability. Research on digital image processing by computers was initiated about 30 years ago, and since then a wide variety of image processing algorithms have been devised. Using such image processing algorithms and advanced hardware technologies, many practical ma chines with visual recognition capability have been implemented and are used in various fields: optical character readers and design chart readers in offices, position-sensing and inspection systems in factories, computer tomography and medical X-ray and microscope examination systems in hospitals, and so on. Although these machines are useful for specific tasks, their capabilities are limited. That is, they can analyze only simple images which are recorded under very carefully adjusted photographic conditions: objects to be recognized are isolated against a uniform background and under well-controlled artificial lighting. In the late 1970s, many image understanding systems were de veloped to study the automatic interpretation of complex natural scenes. They introduced artificial intelligence techniques to represent the knowl edge about scenes and to realize flexible control structures. The first author developed an automatic aerial photograph interpretation system based on the blackboard model (Naga1980). Although these systems could analyze fairly complex scenes, their capabilities were still limited; the types of recognizable objects were limited and various recognition vii viii Preface errors occurred due to noise and the imperfection of segmentation algorithms.

Managing the Change: Software Configuration and Change Management
Managing the Change: Software Configuration and Change Management

Author: Michael Haug

Publisher: Springer Science & Business Media

Published: 2001-10-23

Total Pages: 488

ISBN-13: 9783540417859

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C. Amting Directorate General Information Society, European Commission, Brussels th Under the 4 Framework of European Research, the European Systems and Soft ware Initiative (ESSI) was part of the ESPRIT Programme. This initiative funded more than 470 projects in the area of software and system process improvements. The majority of these projects were process improvement experiments carrying out and taking up new development processes, methods and technology within the software development process of a company. In addition, nodes (centres of exper tise), European networks (organisations managing local activities), training and dissemination actions complemented the process improvement experiments. ESSI aimed at improving the software development capabilities of European enterprises. It focused on best practice and helped European companies to develop world class skills and associated technologies to build the increasingly complex and varied systems needed to compete in the marketplace. The dissemination activities were designed to build a forum, at European level, to exchange information and knowledge gained within process improvement ex periments. Their major objective was to spread the message and the results of experiments to a wider audience, through a variety ofdifferent channels. The European Experience Exchange (tUR~X) project has been one ofthese dis semination activities within the European Systems and Software Initiative.~UR~X has collected the results of practitioner reports from numerous workshops in Europe and presents, in this series of books, the results of Best Practice achieve ments in European Companies over the last few years.

Landmark-Based Image Analysis
Landmark-Based Image Analysis

Author: Karl Rohr

Publisher: Springer Science & Business Media

Published: 2013-03-14

Total Pages: 314

ISBN-13: 9401597871

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Landmarks are preferred image features for a variety of computer vision tasks such as image mensuration, registration, camera calibration, motion analysis, 3D scene reconstruction, and object recognition. Main advantages of using landmarks are robustness w. r. t. lightning conditions and other radiometric vari ations as well as the ability to cope with large displacements in registration or motion analysis tasks. Also, landmark-based approaches are in general com putationally efficient, particularly when using point landmarks. Note, that the term landmark comprises both artificial and natural landmarks. Examples are comers or other characteristic points in video images, ground control points in aerial images, anatomical landmarks in medical images, prominent facial points used for biometric verification, markers at human joints used for motion capture in virtual reality applications, or in- and outdoor landmarks used for autonomous navigation of robots. This book covers the extraction oflandmarks from images as well as the use of these features for elastic image registration. Our emphasis is onmodel-based approaches, i. e. on the use of explicitly represented knowledge in image analy sis. We principally distinguish between geometric models describing the shape of objects (typically their contours) and intensity models, which directly repre sent the image intensities, i. e. ,the appearance of objects. Based on these classes of models we develop algorithms and methods for analyzing multimodality im ages such as traditional 20 video images or 3D medical tomographic images.

Object-Based Image Analysis
Object-Based Image Analysis

Author: Thomas Blaschke

Publisher: Springer Science & Business Media

Published: 2008-08-09

Total Pages: 804

ISBN-13: 3540770585

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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).

Image Processing and Intelligent Computing Systems
Image Processing and Intelligent Computing Systems

Author: Prateek Singhal

Publisher: CRC Press

Published: 2023-01-17

Total Pages: 321

ISBN-13: 1000822958

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There is presently a drastic growth in multimedia data. During the Covid-19 pandemic, we observed that images helped doctors immensely in the rapid detection of Covid-19 infection in patients. There are many critical applications in which images play a vital role. These applications use raw image data to extract some useful information about the world around us. The quick extraction of valuable information from raw images is one challenge that academicians and professionals face in the present day. This is where image processing comes into action. Image processing’s primary purpose is to get an enhanced image or extract some useful information from raw image data. Therefore, there is a major need for some technique or system that addresses this challenge. Intelligent Systems have emerged as a solution to address quick image information extraction. In simple words, an Intelligent System can be defined as a mathematical model that adapts itself to deal with a problem’s dynamicity. These systems learn how to act so an image can reach an objective. An Intelligent System helps accomplish various image-processing functions like enhancement, segmentation, reconstruction, object detection, and morphing. The advent of Intelligent Systems in the image-processing field has leveraged many critical applications for humankind. These critical applications include factory automation, biomedical imaging analysis, decision econometrics, as well as related challenges.

Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications
Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications

Author: Jesús Medina

Publisher: Springer

Published: 2018-05-29

Total Pages: 773

ISBN-13: 3319914790

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This three volume set (CCIS 853-855) constitutes the proceedings of the 17th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2017, held in Cádiz, Spain, in June 2018. The 193 revised full papers were carefully reviewed and selected from 383 submissions. The papers are organized in topical sections on advances on explainable artificial intelligence; aggregation operators, fuzzy metrics and applications; belief function theory and its applications; current techniques to model, process and describe time series; discrete models and computational intelligence; formal concept analysis and uncertainty; fuzzy implication functions; fuzzy logic and artificial intelligence problems; fuzzy mathematical analysis and applications; fuzzy methods in data mining and knowledge discovery; fuzzy transforms: theory and applications to data analysis and image processing; imprecise probabilities: foundations and applications; mathematical fuzzy logic, mathematical morphology; measures of comparison and entropies for fuzzy sets and their extensions; new trends in data aggregation; pre-aggregation functions and generalized forms of monotonicity; rough and fuzzy similarity modelling tools; soft computing for decision making in uncertainty; soft computing in information retrieval and sentiment analysis; tri-partitions and uncertainty; decision making modeling and applications; logical methods in mining knowledge from big data; metaheuristics and machine learning; optimization models for modern analytics; uncertainty in medicine; uncertainty in Video/Image Processing (UVIP).

Fuzzy Sets Methods in Image Processing and Understanding
Fuzzy Sets Methods in Image Processing and Understanding

Author: Isabelle Bloch

Publisher: Springer Nature

Published: 2023-01-01

Total Pages: 311

ISBN-13: 303119425X

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This book provides a thorough overview of recent methods using higher level information (object or scene level) for advanced tasks such as image understanding along with their applications to medical images. Advanced methods for fuzzy image processing and understanding are presented, including fuzzy spatial objects, geometry and topology, mathematical morphology, machine learning, verbal descriptions of image content, fusion, spatial relations, and structural representations. For each methodological aspect covered, illustrations from the medical imaging domain are provided. This is an ideal book for graduate students and researchers in the field of medical image processing.