Download or read book Semantic Segmentation for Random Object Picking Based on Deep Learning written by Chien-Ming Lin and published by . This book was released on 2018 with total page 106 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models
Download or read book Deep Learning and Convolutional Neural Networks for Medical Image Computing written by Le Lu and published by Springer. This book was released on 2017-07-12 with total page 327 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a detailed review of the state of the art in deep learning approaches for semantic object detection and segmentation in medical image computing, and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks, with the theory supported by practical examples. Features: highlights how the use of deep neural networks can address new questions and protocols, as well as improve upon existing challenges in medical image computing; discusses the insightful research experience of Dr. Ronald M. Summers; presents a comprehensive review of the latest research and literature; describes a range of different methods that make use of deep learning for object or landmark detection tasks in 2D and 3D medical imaging; examines a varied selection of techniques for semantic segmentation using deep learning principles in medical imaging; introduces a novel approach to interleaved text and image deep mining on a large-scale radiology image database.
Download or read book Deep Learning for Computer Vision written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-04-04 with total page 564 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on deep learning neural networks for computer vision in python with Keras.
Download or read book Medical Image Recognition Segmentation and Parsing written by S. Kevin Zhou and published by Academic Press. This book was released on 2015-12-11 with total page 548 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes the technical problems and solutions for automatically recognizing and parsing a medical image into multiple objects, structures, or anatomies. It gives all the key methods, including state-of- the-art approaches based on machine learning, for recognizing or detecting, parsing or segmenting, a cohort of anatomical structures from a medical image. Written by top experts in Medical Imaging, this book is ideal for university researchers and industry practitioners in medical imaging who want a complete reference on key methods, algorithms and applications in medical image recognition, segmentation and parsing of multiple objects. Learn: - Research challenges and problems in medical image recognition, segmentation and parsing of multiple objects - Methods and theories for medical image recognition, segmentation and parsing of multiple objects - Efficient and effective machine learning solutions based on big datasets - Selected applications of medical image parsing using proven algorithms - Provides a comprehensive overview of state-of-the-art research on medical image recognition, segmentation, and parsing of multiple objects - Presents efficient and effective approaches based on machine learning paradigms to leverage the anatomical context in the medical images, best exemplified by large datasets - Includes algorithms for recognizing and parsing of known anatomies for practical applications
Download or read book Handbook of Research on Deep Learning Based Image Analysis Under Constrained and Unconstrained Environments written by Raj, Alex Noel Joseph and published by IGI Global. This book was released on 2020-12-25 with total page 381 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent advancements in imaging techniques and image analysis has broadened the horizons for their applications in various domains. Image analysis has become an influential technique in medical image analysis, optical character recognition, geology, remote sensing, and more. However, analysis of images under constrained and unconstrained environments require efficient representation of the data and complex models for accurate interpretation and classification of data. Deep learning methods, with their hierarchical/multilayered architecture, allow the systems to learn complex mathematical models to provide improved performance in the required task. The Handbook of Research on Deep Learning-Based Image Analysis Under Constrained and Unconstrained Environments provides a critical examination of the latest advancements, developments, methods, systems, futuristic approaches, and algorithms for image analysis and addresses its challenges. Highlighting concepts, methods, and tools including convolutional neural networks, edge enhancement, image segmentation, machine learning, and image processing, the book is an essential and comprehensive reference work for engineers, academicians, researchers, and students.
Download or read book A Selection of Image Understanding Techniques written by Yu-Jin Zhang and published by CRC Press. This book was released on 2023-01-31 with total page 349 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers a comprehensive introduction to seven commonly used image understanding techniques in modern information technology. Readers of various levels can find suitable techniques to solve their practical problems and discover the latest development in these specific domains. The techniques covered include camera model and calibration, stereo vision, generalized matching, scene analysis and semantic interpretation, multi-sensor image information fusion, content-based visual information retrieval, and understanding spatial-temporal behavior. The book provides aspects from the essential concepts overview and basic principles to detailed introduction, explanation of the current methods and their practical techniques. It also presents discussions on the research trends and latest results in conjunction with new development of technical methods. This is an excellent read for those who do not have a subject background in image technology but need to use these techniques to complete specific tasks. These essential information will also be useful for their further study in the relevant fields.
Download or read book Artificial Intelligence and Robotics written by Huimin Lu and published by Springer Nature. This book was released on 2024-01-03 with total page 550 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 8th International Symposium on Artificial Intelligence and Robotics, ISAIR 2023, held in Beijing, China, during October 21–23, 2023. The 50 full papers included in this book were carefully reviewed and selected from 103 submissions. They focus on three important areas of Pattern Recognition: Artificial Intelligence; Robotics and Internet of Things, Covering Various Technical Aspects.
Download or read book Machine Learning and Deep Learning Techniques for Medical Image Recognition written by Ben Othman Soufiene and published by CRC Press. This book was released on 2023-12-01 with total page 270 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.
Download or read book Advanced Deep Learning Strategies for the Analysis of Remote Sensing Images written by Yakoub Bazi and published by MDPI. This book was released on 2021-06-15 with total page 438 pages. Available in PDF, EPUB and Kindle. Book excerpt: The rapid growth of the world population has resulted in an exponential expansion of both urban and agricultural areas. Identifying and managing such earthly changes in an automatic way poses a worth-addressing challenge, in which remote sensing technology can have a fundamental role to answer—at least partially—such demands. The recent advent of cutting-edge processing facilities has fostered the adoption of deep learning architectures owing to their generalization capabilities. In this respect, it seems evident that the pace of deep learning in the remote sensing domain remains somewhat lagging behind that of its computer vision counterpart. This is due to the scarce availability of ground truth information in comparison with other computer vision domains. In this book, we aim at advancing the state of the art in linking deep learning methodologies with remote sensing image processing by collecting 20 contributions from different worldwide scientists and laboratories. The book presents a wide range of methodological advancements in the deep learning field that come with different applications in the remote sensing landscape such as wildfire and postdisaster damage detection, urban forest mapping, vine disease and pavement marking detection, desert road mapping, road and building outline extraction, vehicle and vessel detection, water identification, and text-to-image matching.
Download or read book Learning Robust Representations in Random Forest and Deep Neural Networks for Semantic Segmentation written by Byeongkeun Kang and published by . This book was released on 2018 with total page 148 pages. Available in PDF, EPUB and Kindle. Book excerpt: As semantic segmentation provides the class and the location of objects in a captured scene, it has been one of the core algorithms in many computer vision applications including autonomous driving, robot navigation, surveillance camera system, and human-machine interaction. Most of these applications demand high accuracy, robustness, and efficiency to understand a captured scene accurately in a timely manner in order to avoid accidents, to provide a meaningful warning, and to communicate naturally. We address this needs by using two popular approaches: random forest and deep neural network. We start by introducing a cascaded random forest for binary class segmentation. The framework first detects regions of interest and then segments foreground in the regions. Since the detection reduces the regions for the segmentation forest, the cascaded scheme improves efficiency and accuracy. We then explore learning more robust representations in a random forest. Since predetermined constraints in typical feature extractors restrict learning and extracting optimal features, we present a random forest framework that learns the weights, shapes, and sparsities of feature extractors. We propose an unconstrained filter, an iterative optimization algorithm for learning, a processing pipeline for inference. Experimental results demonstrate that the proposed method achieves real-time semantic segmentation using limited computational and memory resources. Moreover, we present a method to learn/extract depth-adaptive features in a deep neural network. It accomplishes a step toward depth-invariant feature learning and extracting. Since typical neural networks receive inputs from predetermined locations regardless of the distance from the camera, it is challenging to generalize the features of objects at various distances. Hence, we propose the depth-adaptive multiscale convolution layer consisting of the adaptive perception neuron and the in-layer multiscale neuron. The adaptive neuron is to adjust the receptive field at each spatial location using the depth information. The multiscale neuron is to learn features at multiple scales. Experimental results show that the proposed method outperforms the state-of-the-art methods without any additional layers or pre/post-processing. Lastly, we present applications of segmentation including sign language fingerspelling recognition and hand articulation tracking. We also present a potential data augmentation method using generative adversarial networks.
Download or read book Artificial Intelligence of Things AIoT in Precision Agriculture written by Yaqoob Majeed and published by Frontiers Media SA. This book was released on 2024-02-12 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: The merging of Artificial Intelligence (AI) and Internet-of-Things is known as Artificial Intelligence-of-Things (AIoT). IoT consists of interlinked computing devices and machines which can acquire, transfer, and execute field/industrial operations without human involvement, while AI processes the acquired data and helps extract the required information. The technologies work in synergy: AI enriches IoT through machine learning and deep learning-based data analysis and learning capabilities, whereas IoT enriches AI through data acquisition, connectivity, and data exchange. Precision agriculture is becoming critically important for sustainable food production to meet the growing food demand. In recent decades, AI and IoT techniques have played an increasing role within industrial operations (e.g. autonomous manufacturing, automated supply chain management, predictive maintenance, smart energy grids, smart home appliances, and wearables), however, agricultural field operations are still heavily dependent on human labor. This is because these operations are ill-defined, unstructured, and susceptible to variation in natural conditions (e.g. illumination, landscape, atmosphere) plus the biological nature of crops (fruits, stems, leaves, and/or shoots continuously change their shape and/or color as they grow).
Download or read book Smart Trends in Computing and Communications written by Yu-Dong Zhang and published by Springer Nature. This book was released on 2022-07-05 with total page 786 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers high-quality papers presented at the Sixth International Conference on Smart Trends in Computing and Communications (SmartCom 2022), organized by Global Knowledge Research Foundation (GR Foundation) in partnership with IFIP InterYIT during January 11–12, 2022. It covers the state of the art and emerging topics in information, computer communications, and effective strategies for their use in engineering and managerial applications. It also explores and discusses the latest technological advances in, and future directions for, information and knowledge computing and its applications.
Download or read book Recent Research on Geotechnical Engineering Remote Sensing Geophysics and Earthquake Seismology written by Attila Çiner and published by Springer Nature. This book was released on with total page 448 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book GCEC 2017 written by Biswajeet Pradhan and published by Springer. This book was released on 2018-05-12 with total page 1503 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 1st Global Civil Engineering Conference, GCEC 2017, held in Kuala Lumpur, Malaysia, on July 25–28, 2017. It highlights how state-of-the-art techniques and tools in various disciplines of Civil Engineering are being applied to solve real-world problems. The book presents interdisciplinary research, experimental and/or theoretical studies yielding new insights that will advance civil engineering methods. The scope of the book spans the following areas: Structural, Water Resources, Geotechnical, Construction, Transportation Engineering and Geospatial Engineering applications.
Download or read book A Selection of Image Analysis Techniques written by Yu-Jin Zhang and published by CRC Press. This book was released on 2022-10-05 with total page 330 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book focuses on seven commonly used image analysis techniques. It covers aspects from basic principles and practical methods, to new advancement of each selected technique to help readers solve image‐processing related problems in real-life situations. The selected techniques include image segmentation, segmentation evaluation and comparison, saliency object detection, motion analysis, mathematical morphology methods, face recognition and expression classification. The author offers readers a three‐step strategy toward problem‐solving: first, essential principles; then, a detailed explanation; and finally, a discussion on practical and working techniques for specific tasks. He also encourages readers to make full use of available materials from the latest developments and trends. This is an excellent book for those who do not have a complete foundation in image technology but need to use image analysis techniques to perform specific tasks in particular applications.
Download or read book Deep Neural Networks and Data for Automated Driving written by Tim Fingscheidt and published by Springer Nature. This book was released on 2022-07-19 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book brings together the latest developments from industry and research on automated driving and artificial intelligence. Environment perception for highly automated driving heavily employs deep neural networks, facing many challenges. How much data do we need for training and testing? How to use synthetic data to save labeling costs for training? How do we increase robustness and decrease memory usage? For inevitably poor conditions: How do we know that the network is uncertain about its decisions? Can we understand a bit more about what actually happens inside neural networks? This leads to a very practical problem particularly for DNNs employed in automated driving: What are useful validation techniques and how about safety? This book unites the views from both academia and industry, where computer vision and machine learning meet environment perception for highly automated driving. Naturally, aspects of data, robustness, uncertainty quantification, and, last but not least, safety are at the core of it. This book is unique: In its first part, an extended survey of all the relevant aspects is provided. The second part contains the detailed technical elaboration of the various questions mentioned above.
