deep learning technology image (19 risultati)

Condizione: New.

paperback. Condizione: 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.

paperback. Condizione: New. Language:Chinese.Paperback.Pub Date:2023-09 Publisher:Hohai University Press.

paperback. Condizione: New. Language:Chinese.Paperback. Pub Date: 2023-08 Pages: 140 Publisher: Science Press Using deep learning for image detection and feature extraction has been a research hotspot in recent years. This book systematically summarizes the relevant achievements in automatic recognition and extraction of pavement cracks at home and abroad. introduces the relevant theories of deep learning in detail. constructs a semantic segmentation network with improved residual network and attention mechanism. combin.

paperback. Condizione: New. Language:Chinese.Paperback.Pub Date:2022-06-01 Pages:448 Publisher:China Water Resources and Hydropower Press Machine Learning Deep Learning Image Recognition from Basics to Case Practice introduces the field of artificial intelligence with a large number of graphics and images combined with Python code Knowledge about image recognition. The book consists of 3 parts. of which the first part introduces the basic knowledge and the use of related tools such as Python. Jupyter Nobebook and NumPy.

paperback. Condizione: New. Language:Chinese.Paperback. Pub Date: 2024-05 Pages: 472 Publisher: Minzu University of China Press Image restoration is one of the important research directions in the field of image processing. It aims to restore the content of the lost area by utilizing the known information in the damaged image. In recent years. with the development of deep learning technology. image restoration has made breakthrough progress in both visual effects and the accuracy of restoration results. and has been wid.

paperback. Condizione: New. Paperback.Pub Date:2019-12 Pages:116 Language:Publisher:Image semantic segmentation (ISS) is a research hotspot involving computer vision. pattern recognition and artificial intelligence. The image semantic segmentation based on deep learning (ISSbDL) method uses deep computational models to learn abstract image features. which promotes the development of ISS related research. Among the ISSbDL methods. the ISS method based on the encoder-decoder model has problems such as loss of pixel spatia.

paperback. Condizione: New. Paperback. Pub Date: 2018-10-01 Pages: 255 Language: Chinese Publisher: Tsinghua University Press. Introduction to Deep Processing Technology Image Processing starts with the basic concepts of machine learning and image processing. and gradually explains the basics of deep learning image processing technology. Principle and simple implementation. Then introduce several examples of how to use depth.

paperback. Condizione: New. Language:Chinese.Paperback. Pub Date: 2019-04-01 Pages: 280 Publisher: Mechanical Industry Press This book provides a comprehensive introduction to the core technologies and applications of deep learning in the field of image processing. The book not only pays attention to the explanation of the basic theory. but also provides one or two cases of different difficulty for each reader from the beginning of Chapter 4. The reader can modify it based on the existing code.

paperback. Condizione: New. Language:Chinese.Paperback. Pub Date: 2020-03-01 Pages: 271 Publisher: Machinery Industry Press Deep Learning Image Recognition Technology: Based on TensorFlow Object Detection API and OpenVINO Tool Suite firstly describes artificial intelligence. deep learning. convolutional neural networks. The concept of target detection and migration learning. and then detailed how to build a development environment for deep learning target detection applications based on the TensorFlow Object Detection A.