An enormous number of burial objects have been unearthed from ancient tombs in archaeological excavations in China. These mingqi were made in all kinds of materials and in a broad range of forms, techniques and craftsmanship. In this book Quinghua Guo examines a particular type of mingqi -- pottery building. The striking realism of the pottery buildings suggests that they were modelled after actual buildings. They bring to life courtyard houses, manors, towers, granaries and pigsty-privies, as well as cooking ranges and well pavilions. These pottery buildings, previously little known, preserve knowledge of antiquity and demonstrate the architectural quality and structural variety of the period. The author identifies the typology of the pottery buildings they signify in terms of ontology and semiology, in order to provide a conceptual map for classification, and identifies building systems reflected by the mingqi to detect architectonic systems of the Han dynasty. Key features of this volume include: Cross-disciplinary research -- architectural study interlocking with archaeological study; architectural study interlocking with graphic study. The Han pottery buildings are important architectural models from the ancient world, and are contrasted with wooden houses of Middle-Kingdom Egypt and brick buildings of the Minor civilisation, Crete, allowing cross-cultural comparisons.
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
LiDAR Principles, Processing and Applications in Forest Ecology introduces the principles of LiDAR technology and explains how to collect and process LiDAR data from different platforms based on real-world experience. The book provides state-of the-art algorithms on how to extract forest parameters from LiDAR and explains how to use them in forest ecology. It gives an interdisciplinary view, from the perspective of remote sensing and forest ecology. Because LiDAR is still rapidly developing, researchers must use programming languages to understand and process LiDAR data instead of established software. In response, this book provides Python code examples and sample data. Sections give a brief history and introduce the principles of LiDAR, as well as three commonly seen LiDAR platforms. The book lays out step-by-step coverage of LiDAR data processing and forest structure parameter extraction, complete with Python examples. Given the increasing usefulness of LiDAR in forest ecology, this volume represents an important resource for researchers, students and forest managers to better understand LiDAR technology and its use in forest ecology across the world. The title contains over 15 years of research, as well as contributions from scientists across the world. Presents LiDAR applications for forest ecology based in real-world experience Lays out the principles of LiDAR technology in forest ecology in a systematic and clear way Provides readers with state-of the-art algorithms on how to extract forest parameters from LiDAR Offers Python code examples and sample data to assist researchers in understanding and processing LiDAR data Contains over 15 years of research on LiDAR in forest ecology and contributions from scientists working in this field across the world
An enormous number of burial objects have been unearthed from ancient tombs in archaeological excavations in China. These mingqi were made in all kinds of materials and in a broad range of forms, techniques and craftsmanship. In this book Quinghua Guo examines a particular type of mingqi -- pottery building. The striking realism of the pottery buildings suggests that they were modelled after actual buildings. They bring to life courtyard houses, manors, towers, granaries and pigsty-privies, as well as cooking ranges and well pavilions. These pottery buildings, previously little known, preserve knowledge of antiquity and demonstrate the architectural quality and structural variety of the period. The author identifies the typology of the pottery buildings they signify in terms of ontology and semiology, in order to provide a conceptual map for classification, and identifies building systems reflected by the mingqi to detect architectonic systems of the Han dynasty. Key features of this volume include: Cross-disciplinary research -- architectural study interlocking with archaeological study; architectural study interlocking with graphic study. The Han pottery buildings are important architectural models from the ancient world, and are contrasted with wooden houses of Middle-Kingdom Egypt and brick buildings of the Minor civilisation, Crete, allowing cross-cultural comparisons.
Eye, Ear, Nose and Throat Disorders include many of the most common conditions encountered in clinical practice, affecting women and men , children and the elderly equally. This book is designed primarily for overseas readers. It aims to provide real-life case studies and references for teachers and students of international TCM, acupuncture colleges, acupuncturists, and biomedical doctors who are interested in TCM and acupuncture. It is also geared to the general reader to familiarize them with the advantages of treating ophthalmic and otolaryngological disorders with TCM. This book is practical and is highly readability. It took more than two years to complete. During the writing process, there were many group discussions where the book was reviewed and revised. However, despite the authors’ best efforts, this book is still a work on progress. We invite the readers to send their comments, corrections and suggestions to supplement, amend, and improve when reprinted.
As one of the world's most important crops, potatoes play an important role in maintaining the stability of the global food supply. Many countries, including China, believe that food supply security is a basic condition for maintaining national stability and development. Therefore, potatoes can not only solve the problem of international food shortage, but also promote the development of international trade. In recent years, with the continuous improvement of planting technology, the global production and trade volume of potatoes have also been continuously increasing. However, the development of traditional potato quality grading technology is relatively slow. Currently, it still relies on manual sorting in many countries and regions. Because workers can not keep their attention for a long time under huge work pressure and their understanding of grading standards is inconsistent, large amount of wrong potato grading often occurs. This result not only affects farmers' income, but also causes serious waste in the potato processing due to unqualified raw potatoes. In addition, with the continuous increase of manual wages, the cost of manual grading of potatoes has under challenge. Therefore, achieving automation of potato quality grading is imperative. Traditional grading system mainly uses cameras to capture potato color images, and achieves potato quality grading through color information analysis. This method can reach high success rate for certain defects detection, such as green skin, surface rot and mechanical damage. Due to the variety of shapes of potatoes growing underground, the appearance defects, such as bending, bump and hollow, are widely existing. These abnormal samples may fail to be detected and grade to wrong quality groups, the 3D appearance information cannot be fully perceived in 2D color images. In response to such issues, we have decided to build a machine vision system based on depth cameras, which can obtain depth images of potatoes with 3D shape information. Unlike each pixel in a color image that stores color information, each pixel in a depth image stores the distance from the target to the camera. Therefore, the potato 3D surface features can be sensed and used for bump and hollow defects detection. To capture high-quality depth images, we have constructed a specialized depth imaging system, and developed the image acquisition software based on OpenCV and OpenNI framework. Then, each potato surface features are analyzed and extracted for shape analysis, defect detection, and overall quality grading. In recent years, machine learning technology has developed rapidly and has been widely applied in fields such as object recognition and feature detection. Hence, we also apply machine learning technology to the field of potato quality grading. By developing a machine learning model based on convolutional neural networks, we can directly input potato depth images and get the corresponding quality level of the samples. The experiment achieved good grading results. Since color and depth images of potatoes are actually collected simultaneously in data collection step, a novel algorithm is developed for potato 3D model rebuilding. The method is based on Point Cloud Library and OpenGL technology, and it shows the advantage in solving the problem of data traceability, especially when users have objections to automatic quality classification results. This model not only displays 3D potato shape model, but also supports scaling and 360-degree rotation operations. Overall, we believe that with the development of machine learning and depth sensing, potato quality grading systems will become more intelligent, efficient and low-cost.
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