Document-analysis systems and techniques. Agraphics-recognition system for interpretation of line drawings. Automation recognition of engineering drawings and maps. Image-analysis techniques for geographic information systems. Digital image processing and tree-dimensional reconstruction in the basic neurociences. Applying digital processing methods in the analysis of retinal structure. Visual percepition using a Blackbord architeture. Analysis of high-resolution aerial images. Image formation and characterization for tree-dimensional vision. Enhancement of fingerprints using digital and optical techniques. The digital morphological sampling theorem.
This is a guide to imaging techniques for sedimentologists, paleolimnologists, paleoceanographers and microscopists involved in paleoenvironmental reconstruction. Case studies illustrate the range of information obtainable from different sediments (marine, lacustrine, aeolian) and different types of samples (cores, embedded blocks, microscopic slides) using different regions of the electromagnetic spectrum (visible, UV, IR, X-ray). Includes comprehensive protocols, guidelines, and recommendations for the use of low cost image analysis techniques.
This volume constitutes the refereed proceedings of the 11th International Workshop on Combinatorial Image Analysis, IWCIA 2006, held in Berlin, June 2006. The book presents 34 revised full papers together with two invited papers, covering topics including combinatorial image analysis; grammars and models for analysis and recognition of scenes and images; combinatorial topology and geometry for images; digital geometry of curves and surfaces; algebraic approaches to image processing, and more.
This volume constitutes the refereed proceedings of the 16th International Workshop on Combinatorial Image Analysis, IWCIA 2014, held in Brno, Czech Republic, in May 2014. The 20 revised full papers and 3 invited papers presented were carefully reviewed and selected from numerous submissions. The topics covered include discrete geometry and topology in imaging science, new results in image representation, segmentation, grouping, and reconstruction, medical image processing.
Soft Computing Based Medical Image Analysis presents the foremost techniques of soft computing in medical image analysis and processing. It includes image enhancement, segmentation, classification-based soft computing, and their application in diagnostic imaging, as well as an extensive background for the development of intelligent systems based on soft computing used in medical image analysis and processing. The book introduces the theory and concepts of digital image analysis and processing based on soft computing with real-world medical imaging applications. Comparative studies for soft computing based medical imaging techniques and traditional approaches in medicine are addressed, providing flexible and sophisticated application-oriented solutions. Covers numerous soft computing approaches, including fuzzy logic, neural networks, evolutionary computing, rough sets and Swarm intelligence Presents transverse research in soft computing formation from various engineering and industrial sectors in the medical domain Highlights challenges and the future scope for soft computing based medical analysis and processing techniques
In this dissertation, novel Content-based Microscopic Image Analysis (CBMIA) methods, including Weakly Supervised Learning (WSL), are proposed to aid biological studies. In a CBMIA task, noisy image, image rotation, and object recognition problems need to be addressed. To this end, the first approach is a general supervised learning method, which consists of image segmentation, shape feature extraction, classification, and feature fusion, leading to a semi-automatic approach. In contrast, the second approach is a WSL method, which contains Sparse Coding (SC) feature extraction, classification, and feature fusion, leading to a full-automatic approach. In this WSL approach, the problems of noisy image and object recognition are jointly resolved by a region-based classifier, and the image rotation problem is figured out through SC features. To demonstrate the usefulness and potential of the proposed methods, experiments are implemented on di erent practical biological tasks, including environmental microorganism classification, stem cell analysis, and insect tracking.
The two-volume set LNCS 6978 + LNCS 6979 constitutes the proceedings of the 16th International Conference on Image Analysis and Processing, ICIAP 2011, held in Ravenna, Italy, in September 2011. The total of 121 papers presented was carefully reviewed and selected from 175 submissions. The papers are divided into 10 oral sessions, comprising 44 papers, and three post sessions, comprising 77 papers. They deal with the following topics: image analysis and representation; image segmentation; pattern analysis and classification; forensics, security and document analysis; video analysis and processing; biometry; shape analysis; low-level color image processing and its applications; medical imaging; image analysis and pattern recognition; image and video analysis and processing and its applications.
The two-volume set LNCS 3522 and 3523 constitutes the refereed proceedings of the Second Iberian Conference on Pattern Recognition and Image Analysis, IbPRIA 2005, held in Estoril, Portugal in June 2005. The 170 revised full papers presented were carefully reviewed and selected from 292 submissions. The papers are organized in topical sections on computer vision, shape and matching, image and video processing, image and video coding, face recognition, human activity analysis, surveillance, robotics, hardware architectures, statistical pattern recognition, syntactical pattern recognition, image analysis, document analysis, bioinformatics, medical imaging, biometrics, speech recognition, natural language analysis, and applications.
Computational Retinal Image Analysis: Tools, Applications and Perspectives gives an overview of contemporary retinal image analysis (RIA) in the context of healthcare informatics and artificial intelligence. Specifically, it provides a history of the field, the clinical motivation for RIA, technical foundations (image acquisition modalities, instruments), computational techniques for essential operations, lesion detection (e.g. optic disc in glaucoma, microaneurysms in diabetes) and validation, as well as insights into current investigations drawing from artificial intelligence and big data. This comprehensive reference is ideal for researchers and graduate students in retinal image analysis, computational ophthalmology, artificial intelligence, biomedical engineering, health informatics, and more. Provides a unique, well-structured and integrated overview of retinal image analysis Gives insights into future areas, such as large-scale screening programs, precision medicine, and computer-assisted eye care Includes plans and aspirations of companies and professional bodies
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists’ demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recent developments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remote sensing engineering.