Introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. Theory and Use of the EM Algorithm is designed to be useful to both the EM novice and the experienced EM user looking to better understand the method and its use.
Introduces the expectation-maximization (EM) algorithm and provides an intuitive and mathematically rigorous understanding of this method. Theory and Use of the EM Algorithm is designed to be useful to both the EM novice and the experienced EM user looking to better understand the method and its use.
This book highlights the novel photoelectric detection technique on derived attributes of targets. Photoelectric detection on derived attributes of targets is a new target detection and monitoring method. It is achieved by acquiring three types of attributes of the target, including those that reflect the essential features of parts of the target, those directly generated from the target, and those synthesized by the target features. The book introduces the classification of derived attributes of targets and describes typical detection methods. Emphases are put on laser detection of aerial moving targets, using derived attributes such as the disturbance of atmospheric wind fields, trailing vortexes, and the disturbance of atmospheric components. The authors also elaborate on visible light imaging detection using derived attributes such as retroreflection and the identification of target carriers. Besides, the synthetic attributes processing of integrated aerospace images is introduced for the detection of targets on the ground and sea surfaces. This book can be used as a good reference for researchers engaged in the fields of photoelectric detection, target detection and image processing, and as a reference book for senior undergraduates and postgraduates in relevant majors.
This useful textbook/reference presents an accessible primer on the fundamentals of image texture analysis, as well as an introduction to the K-views model for extracting and classifying image textures. Divided into three parts, the book opens with a review of existing models and algorithms for image texture analysis, before delving into the details of the K-views model. The work then concludes with a discussion of popular deep learning methods for image texture analysis. Topics and features: provides self-test exercises in every chapter; describes the basics of image texture, texture features, and image texture classification and segmentation; examines a selection of widely-used methods for measuring and extracting texture features, and various algorithms for texture classification; explains the concepts of dimensionality reduction and sparse representation; discusses view-based approaches to classifying images; introduces the template for the K-views algorithm, as well as a range of variants of this algorithm; reviews several neural network models for deep machine learning, and presents a specific focus on convolutional neural networks. This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year graduate students of computer science, who will benefit from the numerous clarifying examples provided throughout the work.
The volume of data being collected in solar astronomy has exponentially increased over the past decade and we will be entering the age of petabyte solar data. Deep learning has been an invaluable tool exploited to efficiently extract key information from the massive solar observation data, to solve the tasks of data archiving/classification, object detection and recognition. Astronomical study starts with imaging from recorded raw data, followed by image processing, such as image reconstruction, inpainting and generation, to enhance imaging quality. We study deep learning for solar image processing. First, image deconvolution is investigated for synthesis aperture imaging. Second, image inpainting is explored to repair over-saturated solar image due to light intensity beyond threshold of optical lens. Third, image translation among UV/EUV observation of the chromosphere/corona, Ha observation of the chromosphere and magnetogram of the photosphere is realized by using GAN, exhibiting powerful image domain transfer ability among multiple wavebands and different observation devices. It can compensate the lack of observation time or waveband. In addition, time series model, e.g., LSTM, is exploited to forecast solar burst and solar activity indices. This book presents a comprehensive overview of the deep learning applications in solar astronomy. It is suitable for the students and young researchers who are major in astronomy and computer science, especially interdisciplinary research of them.
This book systematically introduces readers to laser imaging target detection principles and techniques. It covers the fundamentals of laser imaging and presents an extensive, up-to-date analysis of how to best use laser imaging to detect targets. This is followed by a comprehensive discussion of laser imaging target detection principles, laser imaging generation, and target detection methods. The book offers an invaluable resource for researchers, especially those who are engaged in the fields including target detection based on a laser imaging system, target detection and identification, remote sensing imaging and image processing. Additionally, it can be used as a reference book for advanced undergraduates and postgraduates of relevant majors.
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