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Object-Based Image Analysis and Treaty Verification
This book describes recent progress in object-based image interpretation. It presents new results in its application to verification of nuclear non-proliferation. A comprehensive workflow and newly developed algorithms for object-based high resolution image (pre-) processing, feature extraction, change detection, classification and interpretation are developed, applied and evaluated. The analysis chain is demonstrated with satellite imagery acquired over Iranian nuclear facilities.
R 3,417.34
Computer Intrusion Detection and Network Monitoring
This book covers the basic statistical and analytical techniques of computer intrusion detection. It is the first to present a data-centered approach to these problems. It begins with a description of the basics of TCP/IP, followed by chapters dealing with network traffic analysis, network monitoring for intrusion detection, host based intrusion detection, and computer viruses and other malicious code.
R 4,392.23
Medical Image Computing and Computer Assisted Intervention – MICCAI 2026
The 19-volume set LNCS 16878 - 16896 constitutes the refereed proceedings of the 29th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2026, which took place in Strasbourg, France, during September 27 – October 1, 2026.The 1167 full papers included in the proceedings was carefully reviewed and selected from a total of 4601 submissions. The papers were organized in topical parts as follows: Part I, LNCS Volume 16878: Adapting Foundation Models for Medical Imaging: Large Vision and Language Models (I)Part II, LNCS Volume 16879: Adapting Foundation Models for Medical Imaging: Large Vision and Language Models (II)Part III, LNCS Volume 16880: Efficient Learning Strategies in Medical ImagingPart IV, LNCS Volume 16881: Domain Adaptation and Generalization in Medical ImagingPart V, LNCS Volume 16882: Trustworthy, Explainable, and Responsible Medical AIPart VI, LNCS Volume 16883: Medical Image Segmentation (I)Part VII, LNCS Volume 16884: Medical Image Segmentation (II)Part VIII, LNCS Volume 16885: Computer-Aided Diagnosis (I)Part IX, LNCS Volume 16886: Computer-Aided Diagnosis (II)Part X, LNCS Volume 16887: Longitudinal Modeling, Staging, and PrognosisPart XI, LNCS Volume 16888: Image Reconstruction and Image EnhancementPart XII, LNCS Volume 16889: Medical Image Registration; Shape AnalysisPart XIII, LNCS Volume 16890: Medical Image Synthesis and Super-resolutionPart XIV, LNCS Volume 16891: Computational Pathology; Microscopic ImagingPart XV, LNCS Volume 16892: Surgical Data Science and Scene UnderstandingPart XVI, LNCS Volume 16893: Image-Guided Surgery and InterventionPart XVII, LNCS Volume 16894: Neuroimaging and Brain-Computer InterfacesPart XVIII, LNCS Volume 16895: Benchmarking Datasets and Methods; Clinical ApplicationsPart XIX, LNCS Volume 16896: Emerging Techniques and Applications
R 4,343.91
R 1,525.00
Fusion of Artificial Intelligence and Machine Learning in Advanced Image Processing
This book focuses on the fusion of artificial intelligence and machine learning in advanced image processing, data analysis, and cyber security, as well as compiles and discusses various engineering solutions using various artificial intelligence paradigms. It looks at recent technological advancements and considers how artificial intelligence, machine learning, deep learning, soft computing, and evolutionary computing techniques can be used to design, implement, and optimize advanced image processing, data analysis, and cyber security engineering solutions. It will readers develop the insight required to use the tools of digital imaging to solve new problems.The book is divided into sections that deal withArtificial intelligence and machine learning in medicine and healthcareIntelligent decision-making and analysis technologyMachine learning and deep learning for agricultureArtificial intelligence and machine learning for security solutionsAutomation in image processingFusion of Artificial Intelligence and Machine Learning for Advanced Image Processing, Data Analysis, and Cyber Security offers a selection of chapters on the application of artificial intelligence and machine learning for advanced image processing, data analysis, and cyber security. This book will surely enhance the knowledge of readers interested in these areas.
R 8,548.35
Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2013
The three-volume set LNCS 8149, 8150, and 8151 constitutes the refereed proceedings of the 16th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2013, held in Nagoya, Japan, in September 2013. Based on rigorous peer reviews, the program committee carefully selected 262 revised papers from 789 submissions for presentation in three volumes. The 86 papers included in the second volume have been organized in the following topical sections: registration and atlas construction; microscopy, histology, and computer-aided diagnosis; motion modeling and compensation; segmentation; machine learning, statistical modeling, and atlases; computer-aided diagnosis and imaging biomarkers; physiological modeling, simulation, and planning; microscope, optical imaging, and histology; cardiology; vasculatures and tubular structures; brain segmentation and atlases; and functional MRI and neuroscience applications.
R 2,015.00
Image Understanding using Sparse Representations
Image understanding has been playing an increasingly crucial role in several inverse problems and computer vision. Sparse models form an important component in image understanding, since they emulate the activity of neural receptors in the primary visual cortex of the human brain. Sparse methods have been utilized in several learning problems because of their ability to provide parsimonious, interpretable, and efficient models. Exploiting the sparsity of natural signals has led to advances in several application areas including image compression, denoising, inpainting, compressed sensing, blind source separation, super-resolution, and classification. The primary goal of this book is to present the theory and algorithmic considerations in using sparse models for image understanding and computer vision applications. To this end, algorithms for obtaining sparse representations and their performance guarantees are discussed in the initial chapters. Furthermore, approaches for designing overcomplete, data-adapted dictionaries to model natural images are described. The development of theory behind dictionary learning involves exploring its connection to unsupervised clustering and analyzing its generalization characteristics using principles from statistical learning theory. An exciting application area that has benefited extensively from the theory of sparse representations is compressed sensing of image and video data. Theory and algorithms pertinent to measurement design, recovery, and model-based compressed sensing are presented. The paradigm of sparse models, when suitably integrated with powerful machine learning frameworks, can lead to advances in computer vision applications such as object recognition, clustering, segmentation, and activity recognition. Frameworks that enhance the performance of sparse models in such applications by imposing constraints based on the prior discriminatory information and the underlying geometrical structure, and kernelizing the sparse coding and dictionary learning methods are presented. In addition to presenting theoretical fundamentals in sparse learning, this book provides a platform for interested readers to explore the vastly growing application domains of sparse representations.
R 1,113.16
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
R 2,443.77
Intelligent Image Databases
Intelligent Image Databases: Towards Advanced Image Retrieval addresses the image feature selection issue in developing content-based image retrieval systems. The book first discusses the four important issues in developing a complete content-based image retrieval system, and then demonstrates that image feature selection has significant impact on the remaining issues of system design. Next, it presents an in-depth literature survey on typical image features explored by contemporary content-based image retrieval systems for image matching and retrieval purposes. The goal of the survey is to determine the characteristics and the effectiveness of individual features, so as to establish guidelines for future development of content-based image retrieval systems. Intelligent Image Databases: Towards Advanced Image Retrieval describes the Advanced Region-Based Image Retrieval System (ARBIRS) developed by the authors for color images of real-world scenes. They have selected image regions for building ARBIRS as the literature survey suggests that prominent image regions, along with their associated features, provide a higher probability for achieving a higher level content-based image retrieval system. A major challenge in building a region-based image retrieval system is that prominent regions are rather difficult to capture in an accurate and error-free condition, particularly those in images of real-world scenes. To meet this challenge, the book proposes an integrated approach to tackle the problem via feature capturing, feature indexing, and database query. Through comprehensive system evaluation, it is demonstrated how these systematically integrated efforts work effectively to accomplish advanced image retrieval. Intelligent Image Databases: Towards Advanced Image Retrieval serves as an excellent reference and may be used as a text for advanced courses on the topic.
R 3,014.54
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
R 2,251.19
Advanced Digital Image Steganography Using LSB, PVD, and EMD
In the last few decades, the use of the Internet has grown tremendously, and the use of online communications has grown even more. The lack of security in private messages between individuals, however, allows hackers to collect loads of sensitive information. Modern security measures are required to prevent this attack on the world's communication technologies. Advanced Digital Image Steganography Using LSB, PVD, and EMD: Emerging Research and Opportunities provides evolving research exploring the theoretical and practical aspects of data encryption techniques and applications within computer science. The book provides introductory knowledge on steganography and its importance, detailed analysis of how RS and PDH are performed, discussion on pixel value differencing principles, and hybrid approaches using substitution, PVD, and EMD principles. It is ideally designed for researchers and graduate and under graduate students seeking current research on the security of data during transit.
R 4,841.80
Medical Image Computing and Computer Assisted Intervention – MICCAI 2021
The eight-volume set LNCS 12901, 12902, 12903, 12904, 12905, 12906, 12907, and 12908 constitutes the refereed proceedings of the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2021, held in Strasbourg, France, in September/October 2021.*The 531 revised full papers presented were carefully reviewed and selected from 1630 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: image segmentationPart II: machine learning - self-supervised learning; machine learning - semi-supervised learning; and machine learning - weakly supervised learningPart III: machine learning - advances in machine learning theory; machine learning - attention models; machine learning - domain adaptation; machine learning - federated learning; machine learning - interpretability / explainability; and machine learning - uncertaintyPart IV: image registration; image-guided interventions and surgery; surgical data science; surgical planning and simulation; surgical skill and work flow analysis; and surgical visualization and mixed, augmented and virtual realityPart V: computer aided diagnosis; integration of imaging with non-imaging biomarkers; and outcome/disease predictionPart VI: image reconstruction; clinical applications - cardiac; and clinical applications - vascularPart VII: clinical applications - abdomen; clinical applications - breast; clinical applications - dermatology; clinical applications - fetal imaging; clinical applications - lung; clinical applications - neuroimaging - brain development; clinical applications - neuroimaging - DWI and tractography; clinical applications - neuroimaging - functional brain networks; clinical applications - neuroimaging – others; and clinical applications - oncologyPart VIII: clinical applications - ophthalmology; computational (integrative) pathology; modalities - microscopy; modalities - histopathology; and modalities - ultrasound*The conference was held virtually.
R 4,337.46
Medical Image Computing and Computer-Assisted Intervention - MICCAI 2002
The fifth international Conference in Medical Image Computing and Computer Assisted Intervention (MICCAI 2002) was held in Tokyo from September 25th to 28th, 2002. This was the first time that the conference was held in Asia since its foundation in 1998. The objective of the conference is to offer clinicians and scientists the opportunity to collaboratively create and explore the new medical field. Specifically, MICCAI offers a forum for the discussion of the state of art in computer-assisted interentions, medical robotics, and image processing among experts from multi-disciplinary professions, including but not limited to clinical doctors, computer scientists, and mechanical and biomedical engineers. The expectations of society are very high; the advancement of medicine will depend on computer and device technology in coming decades, as they did in the last decades. We received 321 manuscripts, of which 41 were chosen for oral presentation and 143 for poster presentation. Each paper has been included in these proceedings in eight-page full paper format, without any differentiation between oral and poster papers. Adherence to this full paper format, along with the increased number of manuscripts, surpassing all our expectations, has led us to issue two proceedings volumes for the first time in MICCAI’s history. Keeping to a single volume by assigning fewer pages to each paper was certainly an option for us considering our budget constraints. However, we decided to increase the volume to offer authors maximum opportunity to argue the state of art in their work and to initiate constructive discussions among the MICCAI audience.
R 4,030.00
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018.The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods.Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications.
R 2,015.00
Dental Image Processing for Human Identification
This book presents an approach to postmortem human identification using dental image processing based on dental features and characteristics, and provides information on various identification systems based on dental features using image processing operations. The book also provides information on a novel human identification approach that uses Infinite Symmetric Exponential Filter (ISEF) based edge detection and contouring algorithms.Provides complete details on dental imaging;Discusses the important features of a human identification approach and presents a brief review on DICOM standard for dental imaging;Presents human identification approach based on dental features.
R 4,060.58
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
R 2,255.88
Machine Interpretation Of Patterns: Image Analysis And Data Mining
This review volume provides from both theoretical and application points of views, recent developments and state-of-the-art reviews in various areas of pattern recognition, image processing, machine learning, soft computing, data mining and web intelligence.Machine Interpretation of Patterns: Image Analysis and Data Mining is an essential and invaluable resource for professionals and advanced graduates in computer science, mathematics and life sciences. It can also be considered as an integrated volume to researchers interested in doing interdisciplinary research where computer science is a component.
R 4,466.11
Classification
As the amount of information recorded and stored electronically grows ever larger, it becomes increasingly useful, if not essential, to develop better and more efficient ways to summarize and extract information from these large, multivariate data sets. The field of classification does just that-investigates sets of "objects" to see if they can be summarized into a small number of classes comprising similar objects.Researchers have made great strides in the field over the last twenty years, and classification is no longer perceived as being concerned solely with exploratory analyses. The second edition of Classification incorporates many of the new and powerful methodologies developed since its first edition. Like its predecessor, this edition describes both clustering and graphical methods of representing data, and offers advice on how to decide which methods of analysis best apply to a particular data set. It goes even further, however, by providing critical overviews of recent developments not widely known, including efficient clustering algorithms, cluster validation, consensus classifications, and the classification of symbolic data.The author has taken an approach accessible to researchers in the wide variety of disciplines that can benefit from classification analysis and methods. He illustrates the methodologies by applying them to data sets-smaller sets given in the text, larger ones available through a Web site.Large multivariate data sets can be difficult to comprehend-the sheer volume and complexity can prove overwhelming. Classification methods provide efficient, accurate ways to make them less unwieldy and extract more information. Classification, Second Edition offers the ideal vehicle for gaining the background and learning the methodologies-and begin putting these techniques to use.
R 3,823.29
Genetic Programming for Image Classification
This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of images. Genetic Programming (GP) is an evolutionary computation technique that can automatically evolve computer programs to solve any given problem. This is an important research field of GP and image classification. No book has been published in this field. This book shows how different techniques, e.g., image operators, ensembles, and surrogate, are proposed and employed to improve the accuracy and/or computational efficiency of GP for image classification. The proposed methods are applied to many different image classification tasks, and the effectiveness and interpretability of the learned models will be demonstrated. This book is suitable as a graduate and postgraduate level textbook in artificial intelligence, machine learning, computer vision, and evolutionary computation.
R 6,228.00
Image and Graphics Technologies and Applications
This book constitutes the refereed proceedings of the 16th Conference on Image and Graphics Technologies and Applications, IGTA 2021, held in Beijing, China in June, 2021. The 21 papers presented were carefully reviewed and selected from 86 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR, as well as image technology applications. The volume contains the following thematic blocks: image processing and enhancement techniques (image information acquisition, image/video coding, image/video transmission, image/video storage, compression, completion, dehazing, reconstruction and display, etc.); biometric identification techniques (biometric identification and authentication techniques including face, fingerprint, iris and palm-print, etc.); machine vision and 3D reconstruction (visual information acquisition, camera calibration, stereo vision, 3D reconstruction, and applications of machine vision in industrial inspection, etc.); image/video big data analysis and understanding (object detection and recognition, image/video retrieval, image segmentation, matching, analysis and understanding); computer graphics (modeling, rendering, algorithm simplification and acceleration techniques, realistic scene generation, 3D reconstruction algorithm, system and application, etc.); virtual reality and human-computer interaction (virtual scene generation techniques, tracing and positioning techniques for large-scale space, augmented reality techniques, human-computer interaction techniques based on computer vision, etc.); applications of image and graphics (image/video processing and transmission, biomedical engineering applications, information security, digital watermarking, text processing and transmission, remote sensing, telemetering, etc.); other research works and surveys related to the applications of image and graphics technology.
R 3,114.00
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
The eight-volume set LNCS 13431, 13432, 13433, 13434, 13435, 13436, 13437, and 13438 constitutes the refereed proceedings of the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, which was held in Singapore in September 2022.The 574 revised full papers presented were carefully reviewed and selected from 1831 submissions in a double-blind review process. The papers are organized in the following topical sections:Part I: Brain development and atlases; DWI and tractography; functional brain networks; neuroimaging; heart and lung imaging; dermatology;Part II: Computational (integrative) pathology; computational anatomy and physiology; ophthalmology; fetal imaging;Part III: Breast imaging; colonoscopy; computer aided diagnosis;Part IV: Microscopic image analysis; positron emission tomography; ultrasound imaging; video data analysis; image segmentation I;Part V: Image segmentation II; integration of imaging with non-imaging biomarkers;Part VI: Image registration; image reconstruction;Part VII: Image-Guided interventions and surgery; outcome and disease prediction; surgical data science; surgical planning and simulation; machine learning – domain adaptation and generalization;Part VIII: Machine learning – weakly-supervised learning; machine learning – model interpretation; machine learning – uncertainty; machine learning theory and methodologies.
R 2,251.91
Computational Intelligence Paradigms in Advanced Pattern Classification
This monograph presents selected areas of application of pattern recognition and classification approaches including handwriting recognition, medical image analysis and interpretation, development of cognitive systems for image computer understanding, moving object detection, advanced image filtration and intelligent multi-object labelling and classification. It is directed to the scientists, application engineers, professors, professors and students will find this book useful.
R 4,706.81
Practical Image and Video Processing Using MATLAB
UP-TO-DATE, TECHNICALLY ACCURATE COVERAGE OF ESSENTIAL TOPICS IN IMAGE AND VIDEO PROCESSING This is the first book to combine image and video processing with a practical MATLAB®-oriented approach in order to demonstrate the most important image and video techniques and algorithms. Utilizing minimal math, the contents are presented in a clear, objective manner, emphasizing and encouraging experimentation. The book has been organized into two parts. Part I: Image Processing begins with an overview of the field, then introduces the fundamental concepts, notation, and terminology associated with image representation and basic image processing operations. Next, it discusses MATLAB® and its Image Processing Toolbox with the start of a series of chapters with hands-on activities and step-by-step tutorials. These chapters cover image acquisition and digitization; arithmetic, logic, and geometric operations; point-based, histogram-based, and neighborhood-based image enhancement techniques; the Fourier Transform and relevant frequency-domain image filtering techniques; image restoration; mathematical morphology; edge detection techniques; image segmentation; image compression and coding; and feature extraction and representation. Part II: Video Processing presents the main concepts and terminology associated with analog video signals and systems, as well as digital video formats and standards. It then describes the technically involved problem of standards conversion, discusses motion estimation and compensation techniques, shows how video sequences can be filtered, and concludes with an example of a solution to object detection and tracking in video sequences using MATLAB®. Extra features of this book include: More than 30 MATLAB® tutorials, which consist of step-by-step guides toexploring image and video processing techniques using MATLAB®Chapters supported by figures, examples, illustrative problems, and exercisesUseful websites and an extensive list of bibliographical references This accessible text is ideal for upper-level undergraduate and graduate students in digital image and video processing courses, as well as for engineers, researchers, software developers, practitioners, and anyone who wishes to learn about these increasingly popular topics on their own.
R 6,023.00
R 1,916.60
Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018.The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. Part II: Optical and Histology Applications: Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. Cardiac, Chest and Abdominal Applications: Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. Part III: Diffusion Tensor Imaging and Functional MRI: Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. Neuroimaging and Brain Segmentation Methods: Neuroimaging; Brain Segmentation Methods.Part IV: Computer Assisted Intervention: Image Guided Interventions and Surgery; Surgical Planning, Simulation and Work Flow Analysis; Visualization and Augmented Reality. Image Segmentation Methods: General Image Segmentation Methods, Measures and Applications; Multi-Organ Segmentation; Abdominal Segmentation Methods; Cardiac Segmentation Methods; Chest, Lung and Spine Segmentation; Other Segmentation Applications.
R 4,030.00
Image and Graphics Technologies and Applications
This book constitutes the refereed proceedings of the 15th Conference on Image and Graphics Technologies and Applications, IGTA 2020, held in Beijing, China in September, 2020.*The 24 papers presented were carefully reviewed and selected from 115 submissions. They provide a forum for sharing progresses in the areas of image processing technology; image analysis and understanding; computer vision and pattern recognition; big data mining, computer graphics and VR, as well as image technology applications.*The conference was held virtually due to the COVID-19 pandemic.
R 2,180.19
Medical Content-Based Retrieval for Clinical Decision Support
We are pleased to present this set of peer-reviewed papers from the ?rst MICCAI Workshop on Medical Content-Based Retrieval for Clinical Decision Support. The MICCAI conference has been the ?agship conference for the m- ical imaging community re?ecting the state of the art in techniques of segm- tation, registration, and robotic surgery. Yet, the transfer of these techniques to clinical practice is rarely discussed in the MICCAI conference. To address this gap, we proposed to hold this workshop with MICCAI in London in September 2009. The goal of the workshop was to show the application of content-based retrieval in clinical decision support. With advances in electronic patient record systems, a large number of pre-diagnosed patient data sets are now bec- ing available. These data sets are often multimodal consisting of images (x-ray, CT, MRI), videos and other time series, and textual data (free text reports and structuredclinicaldata). Analyzing thesemultimodalsourcesfordisease-speci?c information across patients can reveal important similarities between patients and hence their underlying diseases and potential treatments.Researchers are now beginning to use techniques of content-based retrieval to search for disea- speci?c information in modalities to ?nd supporting evidence for a disease or to automatically learn associations of symptoms and diseases. Benchmarking frameworks such as ImageCLEF (Image retrieval track in the Cross-Language Evaluation Forum) have expanded over the past ?ve years to include large m- ical image collections for testing various algorithms for medical image retrieval and classi?cation.
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Visual Event Detection
Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyzing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g. , recognition of gestures, activities, facial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis.
R 4,631.81
Advances in Low-Level Color Image Processing
Color perception plays an important role in object recognition and scene understanding both for humans and intelligent vision systems. Recent advances in digital color imaging and computer hardware technology have led to an explosion in the use of color images in a variety of applications including medical imaging, content-based image retrieval, biometrics, watermarking, digital inpainting, remote sensing, visual quality inspection, among many others. As a result, automated processing and analysis of color images has become an active area of research, to which the large number of publications of the past two decades bears witness. The multivariate nature of color image data presents new challenges for researchers and practitioners as the numerous methods developed for single channel images are often not directly applicable to multichannel ones. The goal of this volume is to summarize the state-of-the-art in the early stages of the color image processing pipeline.
R 2,015.00
ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging
This book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment.
R 5,152.52
Intelligent Condition Based Monitoring
This book discusses condition based monitoring of rotating machines using intelligent adaptive systems. The book employs computational intelligence and fuzzy control principles to deliver a module that can adaptively monitor and optimize machine health and performance. This book covers design and performance of such systems and provides case studies and data models for fault detection and diagnosis. The contents cover everything from optimal sensor positioning to fault diagnosis. The principles laid out in this book can be applied across rotating machinery such as turbines, compressors, and aircraft engines. The adaptive fault diagnostics systems presented can be used in multiple time and safety critical applications in domains such as aerospace, automotive, deep earth and deep water exploration, and energy.
R 8,419.91
Artificial Intelligence Applications in Electrical Transmission and Distribution Systems Protection
Artificial intelligence (AI) can successfully help in solving real-world problems in power transmission and distribution systems because AI-based schemes are fast, adaptive, and robust and are applicable without any knowledge of the system parameters. This book considers the application of AI methods for the protection of different types and topologies of transmission and distribution lines. It explains the latest pattern-recognition-based methods as applicable to detection, classification, and location of a fault in the transmission and distribution lines, and to manage smart power systems including all the pertinent aspects. FEATURES Provides essential insight on uses of different AI techniques for pattern recognition, classification, prediction, and estimation, exclusive to power system protection issues Presents an introduction to enhanced electricity system analysis using decision-making tools Covers AI applications in different protective relaying functions Discusses issues and challenges in the protection of transmission and distribution systems Includes a dedicated chapter on case studies and applicationsThis book is aimed at graduate students, researchers, and professionals in electrical power system protection, stability, and smart grids.
R 3,744.29
Perspectives on Content-Based Multimedia Systems
Multimedia data comprising of images, audio and video is becoming increasingly common. The decreasing costs of consumer electronic devices such as digital cameras and digital camcorders, along with the ease of transportation facilitated by the Internet, has lead to a phenomenal rise in the amount of multimedia data generated and distributed. Given that this trend of increased use of multimedia data is likely to accelerate, there is an urgent need for providing a clear means of capturing, storing, indexing, retrieving, analyzing and summarizing such data. Content-based access to multimedia data is of primary importance since it is the natural way by which human beings interact with such information. To facilitate the content-based access of multimedia information, the first step is to derive feature measures from these data so that a feature space representation of the data content can be formed. This can subsequently allow for mapping the feature space to the symbol space (semantics) either automatically or through human intervention. Thus, signal to symbol mapping, useful for any practical system, can be successfully achieved. Perspectives on Content-Based Multimedia Systems provides a comprehensive set of techniques to tackle these important issues. This book offers detailed solutions to a wide range of practical problems in building real systems by providing specifics of three systems built by the authors. While providing a systems focus, it also equips the reader with a keen understanding of the fundamental issues, including a formalism for content-based multimedia database systems, multimedia feature extraction, object-based techniques, signature-based techniques and fuzzy retrieval techniques. The performance evaluation issues of practical systems is also explained. This book brings together essential elements of building a content-based multimedia database system in a way that makes them accessible to practitioners in computer science and electrical engineering. It can also serve as a textbook for graduate-level courses.
R 4,671.04
Image
The three essays in Image, written by leading philosophers of religion, explore the modern power of the visual at the intersection of the human and the technological. Modern life is steeped in images, image-making, and attempts to control the world through vision. Mastery of images has been advanced by technologies that expand and reshape vision and enable us to create, store, transmit, and display images. The three essays in Image, written by leading philosophers of religion Mark C. Taylor, Mary-Jane Rubenstein, and Thomas A. Carlson, explore the power of the visual at the intersection of the human and the technological. Building on Heidegger’s notion that modern humanity aims to master the world by picturing or representing the real, they investigate the contemporary culture of the image in its philosophical, religious, economic, political, imperial, and military dimensions, challenging the abstraction, anonymity, and dangerous disconnection of contemporary images. Taylor traces a history of capitalism, focusing on its lack of humility, particularly in the face of mortality, and he considers art as a possible way to reconnect us to the earth. Through a genealogy of iconic views from space, Rubenstein exposes the delusions of conquest associated with extraterrestrial travel. Starting with the pressing issues of surveillance capitalism and facial recognition technology, Carlson extends Heidegger’s analysis through a meditation on the telematic elimination of the individual brought about by totalizing technologies. Together, these essays call for a consideration of how we can act responsibly toward the past in a way that preserves the earth for future generations. Attending to the fragility of material things and to our own mortality, they propose new practices of imagination grounded in love and humility.
R 3,031.00
R 381.00
R 432.00
R 855.00
R 698.00
R 336.00
