deep learning applications image (110 resultados)

Condición: New. In.

Condición: New. In.

Condición: Brand New. New.SoftCover International edition. Different ISBN and Cover image but contents are same as US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Condición: New. In.

Condición: New. In.

Paperback. Condición: Brand New. 343 pages. 9.00x6.25x0.75 inches. In Stock.

Condición: New. In.

Condición: New.

hardcover. Condición: Very Good.

paperback. Condición: New. Paperback.Pub Date:2022-07-01 Pages:159 Language:Chinese Publisher:Tsinghua University Press This book systematically discusses the principles and applications of deep learning image retrieval. The book is divided into two parts: the first part of image retrieval basics (Chapters 1 to 3). which introduces image retrieval technology. deep learning foundation. and deep learning-based image retrieval methods; the second part of image retrieval applications (Chapters 4 to 8) ). taking vehicle ima.

Softcover. Condición: Very Good. 1. Auflage. Unread, with a mimimum of shelfwear. Immediately dispatched from Germany.

Condición: New. KlappentextDeep learning and image processing are two areas of great interest to academics and industry professionals alike. The areas of application of these two disciplines range widely, encompassing fields such as medicine, robotics, .

HRD. Condición: New. New Book. Shipped from UK. Established seller since 2000.

Condición: New.

Condición: New. In.

Condición: New. In.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book presents cutting-edge research and applications of deep learning in a broad range of medical imaging scenarios, such as computer-aided diagnosis, image segmentation, tissue recognition and classification, and other areas of medical and healthcare problems. Each of its chapters covers a topic in depth, ranging from medical image synthesis and techniques for muskuloskeletal analysis to diagnostic tools for breast lesions on digital mammogramsand glaucoma on retinal fundus images. It also provides an overview of deep learning in medical image analysis and highlights issues and challenges encountered by researchers and clinicians, surveying and discussing practical approaches in general and in the context of specific problems. Academics, clinical and industry researchers, as well as young researchers and graduate students in medical imaging, computer-aided-diagnosis, biomedical engineering and computer vision will find this book a great reference and very useful learning resource.

Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3.The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

Taschenbuch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides state-of-the-art coverage of deep learning applications in image analysis. The book demonstrates various deep learning algorithms that can offer practical solutions for various image-related problems; also how these algorithms are used by scientists and scholars in industry and academia. This includes autoencoder and deep convolutional generative adversarial network in improving classification performance of Bangla handwritten characters, dealing with deep learning-based approaches using feature selection methods for automatic diagnosis of covid-19 disease from x-ray images, imbalance image data sets of classification, image captioning using deep transfer learning, developing a vehicle over speed detection system, creating an intelligent system for video-based proximity analysis, building a melanoma cancer detection system using deep learning, plant diseases classification using AlexNet, dealing with hyperspectral images using deep learning, chest x-ray image classification of pneumonia disease using efficient net and inceptionv3.The book also addresses the difficulty of implementing deep learning in terms of computation time and the complexity of reasoning and modelling different types of data where information is currently encoded. Each chapter has the application of various new or existing deep learning models such as Deep Neural Network (DNN) and Deep Convolutional Neural Networks (DCNN). The detailed utilization of deep learning packages that are available in MATLAB, Python and R programming environments have also been discussed, therefore, the readers will get to know about the practical implementation of deep learning as well. The content of this book is presented in a simple and lucid style for professionals, nonprofessionals, scientists, and students interested in the research area of deep learning applications in image analysis.

paperback. Condición: New. Paperback. Pub Date: 2020-12-01 Pages: 288 Language: Chinese Publisher: Science Press Deep Learning and Image Analysis-Basics and Applications divided into two parts: basic and application. in-depth introduction to the application of deep learning to image analysis Basic concepts. methods and techniques.?In the basic part. Chapter 1 introduces the basic knowledge of neural networks and deep learning. On this basis. Chapters 2. 3. 4. and 5 respectively discuss in depth the use of deep learning.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Image segmentation is a crucial aspect of clinical decision-making in the medical field. The integration of image segmentation techniques has dramatically enhanced healthcare delivery. Also, the advancement of deep learning, particularly Convolutional Neural Networks (CNNs), has brought about a significant transformation in medical image analysis. These advanced algorithms have shown exceptional abilities in identifying complex patterns and features in medical images, revolutionizing diagnostic imaging. However, the complexity and scale of these models present significant challenges. This requires a substantial amount of computational resources and expert knowledge for successful implementation. Addressing these challenges is crucial to fully exploit the potential of deep learning in the field of medical image segmentation. To address the challenges, this study combines metaheuristic optimization algorithms with deep learning. These algorithms, inspired by natural processes, provide an effective way to optimize the structure and parameters of CNNs, thus making the process of medical image segmentation more efficient.