Semantic-aware Image Analysis PDF Download

Author: Weihao Li
Publisher:
ISBN:
Category :
Languages : en
Pages : 0

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Author: Halina Kwaśnicka
Publisher: Springer
ISBN: 3319738917
Category : Technology & Engineering
Languages : en
Pages : 171

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Book Description
This book presents cutting-edge research on various ways to bridge the semantic gap in image and video analysis. The respective chapters address different stages of image processing, revealing that the first step is a future extraction, the second is a segmentation process, the third is object recognition, and the fourth and last involve the semantic interpretation of the image. The semantic gap is a challenging area of research, and describes the difference between low-level features extracted from the image and the high-level semantic meanings that people can derive from the image. The result greatly depends on lower level vision techniques, such as feature selection, segmentation, object recognition, and so on. The use of deep models has freed humans from manually selecting and extracting the set of features. Deep learning does this automatically, developing more abstract features at the successive levels. The book offers a valuable resource for researchers, practitioners, students and professors in Computer Engineering, Computer Science and related fields whose work involves images, video analysis, image interpretation and so on.

Author: Stan Sclaroff
Publisher: Springer Nature
ISBN: 3031064305
Category : Computers
Languages : en
Pages : 786

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Book Description
The proceedings set LNCS 13231, 13232, and 13233 constitutes the refereed proceedings of the 21st International Conference on Image Analysis and Processing, ICIAP 2022, which was held during May 23-27, 2022, in Lecce, Italy, The 168 papers included in the proceedings were carefully reviewed and selected from 307 submissions. They deal with video analysis and understanding; pattern recognition and machine learning; deep learning; multi-view geometry and 3D computer vision; image analysis, detection and recognition; multimedia; biomedical and assistive technology; digital forensics and biometrics; image processing for cultural heritage; robot vision; etc.

Author: Evaggelos Spyrou
Publisher: CRC Press
ISBN: 1351831836
Category : Computers
Languages : en
Pages : 555

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Book Description
Broad in scope, Semantic Multimedia Analysis and Processing provides a complete reference of techniques, algorithms, and solutions for the design and the implementation of contemporary multimedia systems. Offering a balanced, global look at the latest advances in semantic indexing, retrieval, analysis, and processing of multimedia, the book features the contributions of renowned researchers from around the world. Its contents are based on four fundamental thematic pillars: 1) information and content retrieval, 2) semantic knowledge exploitation paradigms, 3) multimedia personalization, and 4) human-computer affective multimedia interaction. Its 15 chapters cover key topics such as content creation, annotation and modeling for the semantic web, multimedia content understanding, and efficiency and scalability. Fostering a deeper understanding of a popular area of research, the text: Describes state-of-the-art schemes and applications Supplies authoritative guidance on research and deployment issues Presents novel methods and applications in an informative and reproducible way Contains numerous examples, illustrations, and tables summarizing results from quantitative studies Considers ongoing trends and designates future challenges and research perspectives Includes bibliographic links for further exploration Uses both SI and US units Ideal for engineers and scientists specializing in the design of multimedia systems, software applications, and image/video analysis and processing technologies, Semantic Multimedia Analysis and Processing aids researchers, practitioners, and developers in finding innovative solutions to existing problems, opening up new avenues of research in uncharted waters.

Author: Muhammad Zubair Khan
Publisher:
ISBN:
Category : Electronic dissertations
Languages : en
Pages : 0

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Book Description
Deep learning has an enormous impact on medical image analysis. Many computer-aided diagnostic systems equipped with deep networks are rapidly reducing human intervention in healthcare. Among several applications, medical image semantic segmentation is one of the core areas of active research to delineate the anatomical structures and other regions of interest. It has a significant contribution to healthcare and provides guided interventions, radiotherapy, and improved radiological diagnostics. Experts believe that intelligent applications designed for medicine will soon take over the role of a radiologist. The goal of bringing semantic segmentation, specifically in healthcare, is to boost efficiency in diagnostics by labeling every pixel with its corresponding class. The core concept revolved around taking random input size and produce output with similar size and sufficient inference. Over time, researchers have proposed distinct architectures for end-to-end and pixel-to-pixel self-sufficient training. In retinal image analysis, the development of semantic segmentation techniques has opened doors for researchers to precisely extract regions of interest and automatically detect the symptoms of various retinal diseases such as diabetic and hypertensive retinopathy. These diseases are common in subjects with diabetes and hypertension. It may cause vascular occlusion and produce fragile micro-vessels in an advanced stage of neovascularization. An excessive amount of sugar in the blood and extended force on vessels rupture the newly developed micro-vessels, providing blood and other fluids leak into the retina. It may cause blindness in severe cases if not diagnosed and treated at an early stage. It is critical to find the initial symptoms, including abnormal vascular growth, hard exudates, arterial and venous occlusion, and the appearance of bulges on the outer layer of vessels. The primary step is to automate the analysis of variations, appear in vessels for detecting retinopathy. In our dissertation, we have performed a context-sensitive semantic segmentation to capture long-range dependencies and restore lossy pixels of manually annotated groundtruths. We also applied morphological image processing techniques to create masks for unmasked datasets. The method adopted literature to apply leave-one-out and k-fold strategies for unordered data distributions. The use of context information in predicting target pixels bring added precision to the vision-critical system. We also designed a fully automated screening system based on a unified modeling approach of diagnosis. The system can extract multiple ocular features with a novel semantic segmentation network to early detect the symptoms of retinal disease. We proposed a novel technique of dynamic inductive learning with single-point decision criteria, striving to optimize the image segmentation model using multi-criteria decision support feedback. It is found that dynamic inductive transfer learning reduces the subjectivity of hyperparameter selection in a model validation process. We further designed a feature-oriented ensemble network for extracting multiple retinal features. It includes a set of models that reflect feature-based needs to prevent intensity loss, micro-vessels overlap, and data redundancy. The proposed learning protocol with a minimalist approach can compete with state-of-the-art work without a performance compromise. The pitfall of previously proposed work is also addressed through the self-defined assessment criteria. Our research also proposed an architecture inspired by the generative adversarial network. The network enhanced the base model with residual, dense, and attention mechanisms. The residual mechanism helped extend the network depth without falling into the gradient problem and improved the model response by transmitting useful feature representations. The dense block helped increase the information flow for reusing feature representations that reduced the number of trainable parameters. However, the attention mechanism performed the domain-centric synthesis and helped conserve local context by highlighting fine details. Our model shown a promising response in extracting both macro and micro-vessels and reported high true positive rate and structural similarity index scores.

Author: Elisa H. Barney Smith
Publisher: Springer Nature
ISBN: 3031705491
Category :
Languages : en
Pages : 452

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Book Description

Author: S. Kevin Zhou
Publisher: Academic Press
ISBN: 0323858880
Category : Computers
Languages : en
Pages : 544

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Book Description
Deep Learning for Medical Image Analysis, Second Edition is a great learning resource for academic and industry researchers and graduate students taking courses on machine learning and deep learning for computer vision and medical image computing and analysis. Deep learning provides exciting solutions for medical image analysis problems and is a key method for future applications. This book gives a clear understanding of the principles and methods of neural network and deep learning concepts, showing how the algorithms that integrate deep learning as a core component are applied to medical image detection, segmentation, registration, and computer-aided analysis.· Covers common research problems in medical image analysis and their challenges · Describes the latest deep learning methods and the theories behind approaches for medical image analysis · Teaches how algorithms are applied to a broad range of application areas including cardiac, neural and functional, colonoscopy, OCTA applications and model assessment · Includes a Foreword written by Nicholas Ayache

Author: Ismail Ben Ayed
Publisher: Elsevier
ISBN: 0128053208
Category : Computers
Languages : en
Pages : 182

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Book Description
High-Order Models in Semantic Image Segmentation reviews recent developments in optimization-based methods for image segmentation, presenting several geometric and mathematical models that underlie a broad class of recent segmentation techniques. Focusing on impactful algorithms in the computer vision community in the last 10 years, the book includes sections on graph-theoretic and continuous relaxation techniques, which can compute globally optimal solutions for many problems. The book provides a practical and accessible introduction to these state-of -the-art segmentation techniques that is ideal for academics, industry researchers, and graduate students in computer vision, machine learning and medical imaging. Gives an intuitive and conceptual understanding of this mathematically involved subject by using a large number of graphical illustrations Provides the right amount of knowledge to apply sophisticated techniques for a wide range of new applications Contains numerous tables that compare different algorithms, facilitating the appropriate choice of algorithm for the intended application Presents an array of practical applications in computer vision and medical imaging Includes code for many of the algorithms that is available on the book's companion website

Author: Tat-Seng Chua
Publisher: Springer Science & Business Media
ISBN: 3642105424
Category : Computers
Languages : en
Pages : 209

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Book Description
This book constitutes the refereed proceedings of the 4th International Conference on Semantics and Digital Media Technologies, SAMT 2009, held in Graz, Austria, in December 2009. The 13 revised full papers and 8 short papers presented together with the abstracts of 2 invited keynote lectures were carefully reviewd and selected from 41 submissions. The volume discusses topics such as semantic analysis and multimedia, semantic retrieval and multimedia, semantic metadata management of multimedia, semantic user interfaces for multimedia, semantics in visualization and computer graphics, as well as applications of semantic multimedia.

Author: Noreddine Gherabi
Publisher: Springer Nature
ISBN: 303072588X
Category : Computers
Languages : en
Pages : 315

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Book Description
This book describes important methodologies, tools and techniques from the fields of artificial intelligence, basically those which are based on relevant conceptual and formal development. The coverage is wide, ranging from machine learning to the use of data on the Semantic Web, with many new topics. The contributions are concerned with machine learning, big data, data processing in medicine, similarity processing in ontologies, semantic image analysis, as well as many applications including the use of machine leaning techniques for cloud security, artificial intelligence techniques for detecting COVID-19, the Internet of things, etc. The book is meant to be a very important and useful source of information for researchers and doctoral students in data analysis, Semantic Web, big data, machine learning, computer engineering and related disciplines, as well as for postgraduate students who want to integrate the doctoral cycle.