Image Analysis and Recognition PDF Download

Author: Aurélio Campilho
Publisher: Springer Science & Business Media
ISBN: 3642137741
Category : Computers
Languages : en
Pages : 465

View

Book Description
This book constitutes the thoroughly refereed proceedings of the 7th International Conference, ICIAR 2010, held in Póvoa de Varzin, Portugal in June 2010. The 88 revised full papers were selected from 164 submissions. The papers are organized in topical sections on Image Morphology, Enhancement and Restoration, Image Segmentation, Featue Extraction and Pattern Recognition, Computer Vision, Shape, Texture and Motion Analysis, Coding, Indexing, and Retrieval, Face Detection and Recognition, Biomedical Image Analysis, Biometrics and Applications

Author: Mohamed Kamel
Publisher: Springer
ISBN: 3642390943
Category : Computers
Languages : en
Pages : 828

View

Book Description
This book constitutes the thoroughly refereed proceedings of the 10th International Conference on Image Analysis and Recognition, ICIAR 2013, held in Póvoa do Varzim, Portugal, in June 2013, The 92 revised full papers presented were carefully reviewed and selected from 177 submissions. The papers are organized in topical sections on biometrics: behavioral; biometrics: physiological; classification and regression; object recognition; image processing and analysis: representations and models, compression, enhancement , feature detection and segmentation; 3D image analysis; tracking; medical imaging: image segmentation, image registration, image analysis, coronary image analysis, retinal image analysis, computer aided diagnosis, brain image analysis; cell image analysis; RGB-D camera applications; methods of moments; applications.

Author: Munish Kumar
Publisher: MDPI
ISBN: 3036517146
Category : Technology & Engineering
Languages : en
Pages : 112

View

Book Description
This book is to chart the progress in applying machine learning, including deep learning, to a broad range of image analysis and pattern recognition problems and applications. In this book, we have assembled original research articles making unique contributions to the theory, methodology and applications of machine learning in image analysis and pattern recognition.

Author: Earl Gose
Publisher: Prentice Hall
ISBN:
Category : Computers
Languages : en
Pages : 504

View

Book Description
Over the past 20 to 25 years, pattern recognition has become an important part of image processing applications where the input data is an image. This book is a complete introduction to pattern recognition and its increasing role in image processing. It covers the traditional issues of pattern recognition and also introduces two of the fastest growing areas: Image Processing and Artificial Neural Networks. Examples and digital images illustrate the techniques, while an appendix describes pattern recognition using the SAS statistical software system.

Author: Aythami Morales
Publisher: Springer Nature
ISBN: 3030313328
Category : Computers
Languages : en
Pages : 657

View

Book Description
This 2-volume set constitutes the refereed proceedings of the 9th Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2019, held in Madrid, Spain, in July 2019. The 99 papers in these volumes were carefully reviewed and selected from 137 submissions. They are organized in topical sections named: Part I: best ranked papers; machine learning; pattern recognition; image processing and representation. Part II: biometrics; handwriting and document analysis; other applications.

Author: Horst Bunke
Publisher: World Scientific
ISBN: 9814500380
Category : Computers
Languages : en
Pages : 851

View

Book Description
Optical character recognition and document image analysis have become very important areas with a fast growing number of researchers in the field. This comprehensive handbook with contributions by eminent experts, presents both the theoretical and practical aspects at an introductory level wherever possible.

Author: João Manuel R. S. Tavares
Publisher: Springer
ISBN: 9783030934194
Category : Computers
Languages : en
Pages : 490

View

Book Description
This book constitutes the proceedings of the 25th Iberoamerican Congress on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, CIARP 2021, which took place during May 10–13, 2021. The conference was initially planned to take place in Porto, Portugal, but changed to a virtual event due to the COVID-19 pandemic. The 45 papers presented in this volume were carefully reviewed and selected from 82 submissions. They were organized in topical sections as follows: medical applications; natural language processing; metaheuristics; image segmentation; databases; deep learning; explainable artificial intelligence; image processing; machine learning; and computer vision.

Author: K.C. Santosh
Publisher: Springer
ISBN: 9811323399
Category : Computers
Languages : en
Pages : 174

View

Book Description
The book focuses on one of the key issues in document image processing – graphical symbol recognition, which is a sub-field of the larger research domain of pattern recognition. It covers several approaches: statistical, structural and syntactic, and discusses their merits and demerits considering the context. Through comprehensive experiments, it also explores whether these approaches can be combined. The book presents research problems, state-of-the-art methods that convey basic steps as well as prominent techniques, evaluation metrics and protocols, and research standpoints/directions that are associated with it. However, it is not limited to straightforward isolated graphics (visual patterns) recognition; it also addresses complex and composite graphical symbols recognition, which is motivated by real-world industrial problems.

Author: Klaus D. Toennies
Publisher: Springer
ISBN: 1447173201
Category : Computers
Languages : en
Pages : 589

View

Book Description
This comprehensive guide provides a uniquely practical, application-focused introduction to medical image analysis. This fully updated new edition has been enhanced with material on the latest developments in the field, whilst retaining the original focus on segmentation, classification and registration. Topics and features: presents learning objectives, exercises and concluding remarks in each chapter; describes a range of common imaging techniques, reconstruction techniques and image artifacts, and discusses the archival and transfer of images; reviews an expanded selection of techniques for image enhancement, feature detection, feature generation, segmentation, registration, and validation; examines analysis methods in view of image-based guidance in the operating room (NEW); discusses the use of deep convolutional networks for segmentation and labeling tasks (NEW); includes appendices on Markov random field optimization, variational calculus and principal component analysis.

Author: Marian Stewart Bartlett
Publisher: Springer Science & Business Media
ISBN: 1461516374
Category : Computers
Languages : en
Pages : 181

View

Book Description
Face Image Analysis by Unsupervised Learning explores adaptive approaches to image analysis. It draws upon principles of unsupervised learning and information theory to adapt processing to the immediate task environment. In contrast to more traditional approaches to image analysis in which relevant structure is determined in advance and extracted using hand-engineered techniques, Face Image Analysis by Unsupervised Learning explores methods that have roots in biological vision and/or learn about the image structure directly from the image ensemble. Particular attention is paid to unsupervised learning techniques for encoding the statistical dependencies in the image ensemble. The first part of this volume reviews unsupervised learning, information theory, independent component analysis, and their relation to biological vision. Next, a face image representation using independent component analysis (ICA) is developed, which is an unsupervised learning technique based on optimal information transfer between neurons. The ICA representation is compared to a number of other face representations including eigenfaces and Gabor wavelets on tasks of identity recognition and expression analysis. Finally, methods for learning features that are robust to changes in viewpoint and lighting are presented. These studies provide evidence that encoding input dependencies through unsupervised learning is an effective strategy for face recognition. Face Image Analysis by Unsupervised Learning is suitable as a secondary text for a graduate-level course, and as a reference for researchers and practitioners in industry.