Billedbehandling

De seneste år er digital farvelægning af gamle fotografier blevet en populær måde at bringe fortiden til live. Det er blevet muligt at farvelægge gamle billeder i så god en kvalitet, at det næsten er blevet svært at kende forskel på et nyt farvefoto og så et farvelagt sort-hvid fotografi. Billederne får et helt nyt liv – og mange af dem ser ud som om de næsten er taget i går.Forfatteren Arne Kjær Hansen er en af dem, der har brugt teknikken. Han har fundet 55 billeder frem fra det gamle Aarhus og farvelagt dem efter de farver, der blev brugt, da billedet blev taget. Der findes billeder fra sidst i 1800-tallet til 1930’erne.Datidens gader og de mennesker, der befolkede dem, kommer pludselig tæt på, når man sidder og betragter Arne Kjær Hansens værker. Hvert enkelt foto er forklaret med en tekst forfattet af historiker Jørgen Mührmann Lund.

Lightroom er det mest populære program blandt professionelle fotografer til efterbehandling og organisering af digitale fotos. Programmets brugervenlighed betyder dog, at også mange amatørfotografer har taget Lightroom til sig.I dette hæfte kan du lære, hvordan du på optimal og effektiv vis arbejder med dine billeder i programmet. Hæftet er logisk bygget op som et opslagsværk, så du hurtigt kan danne dig overblik over de enkelte funktioner.Du vil finde ud af, hvordan du bedst muligt kan bruge biblioteksdelen af programmet til at organisere og sortere dine billeder ved hjælp af de forskellige katalogiseringsværktøjer.Desuden skal vi kigge på de mange efterbehandlingsfunktioner, der lader dig gøre alt fra at rette lys- og farveforhold til at foretage reelle billedmanipulationer med retoucheringsværktøjet.Hæftet er skrevet til Lightroom 5, men kan i vid udstrækning også bruges til ældre versioner af programmet.

This book constitutes the refereed proceedings of the evaluation track of the 9th China Health Information Processing Conference, CHIP 2023, held in Hangzhou, China, during October 27¿29, 2023. The 15 algorithms papers and 6 overview papers included in this book were carefully reviewed and selected from a total of 66 submissions to the conference. They were organized in topical sections as follows: CHIP-PromptCBLUE Medical Large Model Evaluation; Chinese Medical Text Few-shot Named Entity Recognition; Drug Paper Document Recognition and Entity Relation Extraction; CHIP-YIER Medical Large Model Evaluation; Medical Literature PICOS Identification; Chinese Diabetes Question Classification;

This book constitutes the First Segmentation of the Aorta Challenge, SEG.A. 2023, which was held in conjunction with the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, on October 8, 2023. The 8 full and 3 short papers presented have been carefully reviewed and selected for inclusion in the book. They focus specifically on robustness, visual quality and meshing of automatically generated segmentations of aortic vessel trees from CT imaging. The challenge was organized as a ¿container submission¿ challenge, where participants had to upload their algorithms to Grand Challenge in the form of Docker containers. Three tasks were created for SEG.A. 2023.

This book presents a comprehensive set of methods for quantifying geometric quantities such as the volume of a tumor, the total surface area of the alveoli in a lung, the length of plant roots, or of blood vessels, the number of neurons in a brain compartment, the connectivity number of trabecular bone, the mean size of grains in a rock, etc.. The methods, illustrated by twenty solved case studies, are based on properly sampled slices, sections, or projections of the material, observable under light, laser, or electron microscopy, or under non-invasive radiological devices such as ecography, computed tomography, or magnetic resonance imaging. Thus, the input usually consists of flat images, and the output consists of relevant quantities defined in three dimensions. Stereology is the discipline of providing sampling designs which warrant unbiased estimation of the corresponding quantities, that is, estimation with zero mean deviation from the target. Sampling is usually systematic (i.e., with regularly spaced probes), and sparse (as opposed to reconstructions) and it is thereby efficient and easy to implement.Stereology is essentially geometric sampling, grounded on integral geometry. The necessary elements of both disciplines are detailed in textbook style, and may be used for postgraduate courses, or to serve the interest of scientists in general. Hitherto no other book on stereology has appeared which encompasses the theory, methodology, and applications of stereology in an interconnected and comprehensive way. The currently available error variance prediction formulae under systematic sampling, and their (non-obvious) derivation, are all gathered, for the first time, in the last chapter. The exposition is augmented by 127 line drawings for the theory, and 27 color pictures of real materials for the case studies.

This book constitutes the proceedings of the 14th International Workshop, CDMRI 2023, held in conjunction with MICCAI 2023, the 26th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Vancouver, BC, Canada, on October 8, 2023.The 17regular papers presented in this book were carefully reviewed and selected from 19 submissions. These contributions cover various aspects, including preprocessing, signal modeling, tractography, bundle segmentation, and clinical applications. Many of these studies employ novel machine learning implementations, highlighting the evolving landscape of techniques beyond the more traditional physics-based algorithms.

This double volume set LNCS 14393-14394 constitutes the proceedings from the workshops held at the 26th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2023 Workshops, which took place in Vancouver, BC, Canada, in October 2023. The 54 full papers together with 14 short papers presented in this volume were carefully reviewed and selected from 123 submissions from all workshops.The papers of the workshops are presenting the topical sections: Eighth International Skin Imaging Collaboration Workshop (ISIC 2023) First Clinically-Oriented and Responsible AI for Medical Data Analysis (Care-AI 2023) Workshop First International Workshop on Foundation Models for Medical Artificial General Intelligence (MedAGI 2023) Fourth Workshop on Distributed, Collaborative and Federated Learning (DeCaF 2023) First MICCAI Workshop on Time-Series Data Analytics and Learning First MICCAI Workshop on Lesion Evaluation and Assessment with Follow-Up (LEAF) AI For Treatment Response Assessment and predicTion Workshop (AI4Treat 2023) Fourth International Workshop on Multiscale Multimodal Medical Imaging (MMMI 2023) Second International Workshop on Resource-Effcient Medical Multimodal Medical Imaging Image Analysis (REMIA 2023)

This book constitutes the refereed proceedings of ICIVIS2023, held in Baoding, China, in August 2023. The papers included in the proceedings have been carefully reviewed and selected from the submitted manuscripts in the areas of image, vision and intelligent systems. This book provides a reference for theoretical innovative problems as well as recent practical solutions and applications for the state-of-the-art results in image, vision and intelligent systems. The intended audience of the book includes researchers, professors, experts, practitioners and professionals in the field of image, vision and intelligent systems worldwide.

This book constitutes the refereed proceedings of the 17th China Conference on Wireless Sensor Networks, CWSN 2023, which took place in Dalian, China, in October 2023. The 22 full papers presented in this volume were carefully reviewed and selected from 105 submissions, including 38 English papers and 67 Chinese papers. The conference provided an academic exchange of research and a development forum for IoT researchers, developers, enterprises, and users. Exchanging results and experience of research and applications in IoT, and discussing the key challenges and research hotspots, is the main goal of the forum. As a high-level forum for the design, implementation, and application of IoT, the conference promoted the exchange and application of the theories and technologies of IoT-related topics.

Humans use their eyes and their brains to see and visually sense the three-dimensional structure of the world around them. Computer Vision is the science that aims to give a similar, if not better, an ability to a machine or computer. Computer Vision is concerned with the automatic extraction, analysis and understanding of useful information from a single image or a sequence of images. It involves the development of a theoretical and algorithmic basis to achieve visual understanding.The term "surveillance" is derived from the French word meaning "to watch over" and, as applied to public health, means the close monitoring of the occurrence of selected health conditions in the population. In 1963, disease surveillance was de¿ned as "the continued watchfulness over the distribution and trends of incidence through systematic collection, consolidation, and evaluation of morbidity. The mortality reports and other relevant data" directly linked to timely regular dissemination of the results to "those who need to know" . The word surveillance means to observe a speci¿c area or to monitor the activities or behavior of individual or a group. It is very useful to the government to maintain social control, monitor and recognize threats, and investigate criminal activity. Digital and intelligent video surveillance has been one of the most active research areas in the ¿eld of computer vision. The goal is to e¿ciently extract useful information from a huge amount of videos collected by surveillance cameras by automatically detecting, tracking and recognizing objects of interest, and understanding and analyzing their activities.Surveillance cameras or video cameras normally used for the reason of recording or monitoring sensitive or strategic areas of a particular business. They are often connected to a recording device, and may be watching by a security guard or law enforcement öcer. A surveillance camera can be placed in strategic locations throughout private or public areas.

This book deals with the recent advancements in computer vision techniques such as active learning, few-shot learning, zero shot learning, explainable and interpretable ML, online learning, AutoML etc. and their applications in medical domain. Moreover, the key challenges which affect the design, development, and performance of medical imaging systems are addressed. In addition, the state-of-the-art medical imaging methodologies for efficient, interpretable, explainable, and practical implementation of computer imaging techniques are discussed. At present, there are no textbook resources that address the medical imaging technologies. There are ongoing and novel research outcomes which would be useful for the development of novel medical imaging technologies/processes/equipment which can improve the current state of the art.The book particularly focuses on the use of data driven new technologies on medical imaging vision such as Active learning, Online learning, few shot learning, AutoML, segmentation etc.

This book presents and analyzes methods to perform image co-segmentation. In this book, the authors describe efficient solutions to this problem ensuring robustness and accuracy, and provide theoretical analysis for the same. Six different methods for image co-segmentation are presented. These methods use concepts from statistical mode detection, subgraph matching, latent class graph, region growing, graph CNN, conditional encoder¿decoder network, meta-learning, conditional variational encoder¿decoder, and attention mechanisms. The authors have included several block diagrams and illustrative examples for the ease of readers. This book is a highly useful resource to researchers and academicians not only in the specific area of image co-segmentation but also in related areas of image processing, graph neural networks, statistical learning, and few-shot learning.

This book constitutes the refereed proceedings of the 10th International Conference on Algorithms and Discrete Applied Mathematics, CALDAM 2024, held in Bhilai, India during February 15¿17, 2024.The 22 full papers included in this book were carefully reviewed and selected from 57 submissions. They were organized in topical sections as follows: Algorithms and Complexity; Computational Geometry; Discrete Applied Mathematics; Graph Algorithms; Graph Theory.

This book constitutes the revised selected papers of the 13th International Conference on HPCST 2023, held in Barnaul, Russia, during May 19¿20, 2023.The 21 full papers included in this book were carefully reviewed and selected from 81 submissions. The papers are organized in topical sections as follows: Hardware for High-Performance Computing and Signal Processing; Information Technologies and Computer Simulation of Physical Phenomena; Computing Technologies in Data Analysis and Decision Making; Information and Computing Technologies in Automation and Control Science; Computing Technologies in Information Security Applications.

Content based image retrieval (CBIR) has become a popular area of research for both computer vision and multimedia communities. It aims at organizing digital picture archives by analyzing their visual contents. CBIR techniques make use of these visual contents to retrieve in response to any particular query. Note that this differs from traditional retrieval systems based on keywords to search images. Due to widespread variations in the images of standard image databases, achieving high precision and recall for retrieval remains a challenging task. In the recent past, many CBIR algorithms have applied Bag of Visual Words (BoVW) for modeling the visual contents of images. Though BoVW has emerged as a popular image content descriptor, it has some important limitations which can in turn adversely affect the retrieval performance. Image retrieval has many applications in diverse ¿elds including healthcare, biometrics, digital libraries, historical research and many more (da Silva Torres and Falcao, 2006). In the retrieval system, two kinds of approaches are mainly followed, namely, Text-Based Image Retrieval (TBIR) and Content-Based Image Retrieval (CBIR). The former approach requires a lot of hu- man effort, and time and perception. Content based image retrieval is a technique that enables an user to extract similar images based on a query from a database containing large number of images.The basic issue in designing a CBIR system is to select the image features that best represent the image content in a database. As a part of a CBIR system, one has to apply appropriate visual content descriptors to represent these images. A query image should be represented similarly. Then, based on some measures of similarity, a set of images would be retrieved from the avail- able image database. The relevance feedback part, which incorporates inputs from a user, can be an optional block in a CBIR system. The fundamental problem in CBIR is how to transform the visual contents into distinctive features for dissimilar images, and into similar features for images that look alike. BoVW has emerged as a popular model for representing the visual content of an image in the recent past. It tries to bridge the gap between low level visual features and high-level semantic features to some extent.

This book constitutes the proceedings of the Third BenchCouncil International Symposium on Intelligent Computers, Algorithms, and Applications, IC 2023, which took place in Sanya, China, in December 2023. The 18 full papers and 8 short papers included in this book were carefully reviewed and selected from 50 submissions. They were organized in topical sections as follows: AI Algorithms and Systems; AI for Ocean science and engineering; AI in finance; AI for education; AI for materials science and engineering; AI for medicine; AI for civil aviation; AI for high energy physics; AI for law.

This book covers a comprehensive approach to the development and application of a suite of novel algorithms for practical approximate knowledge-based inference. It includes an introduction to the fundamental concepts of fuzzy sets, fuzzy logic, and fuzzy inference. Collectively, this book provides a systematic tutorial and self-contained reference to recent advances in the field of fuzzy rule-based inference. Approximate reasoning systems facilitate inference by utilizing fuzzy if-then production rules for decision-making under circumstances where knowledge is imprecisely characterized. Compositional rule of inference (CRI) and fuzzy rule interpolation (FRI) are two typical techniques used to implement such systems. The question of when to apply these potentially powerful reasoning techniques via automated computation procedures is often addressed by checking whether certain rules can match given observations. Both techniques have been widely investigated to enhance the performance of approximate reasoning. Increasingly more attention has been paid to the development of systems where rule antecedent attributes are associated with measures of their relative significance or weights. However, they are mostly implemented in isolation within their respective areas, making it difficult to achieve accurate reasoning when both techniques are required simultaneously. This book first addresses the issue of assigning equal significance to all antecedent attributes in the rules when deriving the consequents. It presents a suite of weighted algorithms for both CRI and FRI fuzzy inference mechanisms. This includes an innovative reverse engineering process that can derive attribute weightings from given rules, increasing the automation level of the resulting systems. An integrated fuzzy reasoning approach is then developed from these two sets of weighted improvements, showcasing more effective and efficient techniques for approximate reasoning. Additionally, the book provides an overarching application to interpretable medical risk analysis, thanks to the semantics-rich fuzzy rules with attribute values represented in linguistic terms. Moreover, it illustrates successful solutions to benchmark problems in the relevant literature, demonstrating the practicality of the systematic approach to weighted approximate reasoning.

En la medida que la digitalización de la sociedad va alcanzando prácticamente todos los ámbitos de la misma, el patrimonio cultural ha sucumbido irremediablemente a dicha deriva siendo digitalizado masivamente y transcodificado en una nueva realidad. Este paradigma de la digitalización del patrimonio cultural, es analizado extensamente en este trabajo de investigación originado en la tesis doctoral del autor. Así, filosofía, historia y un extenso análisis técnico de normas, estándares y recomendaciones se combinan en un texto único cargado de datos técnicos y reflexiones sobre el estado de la cuestión en la digitalización del patrimonio cultural.