Hong Qin – författare

From Design Methods and Generation Schemes to State-of-the-Art ApplicationsWavelets are powerful tools for functional analysis and geometry processing, enabling researchers to determine the structure of data and analyze 3D shapes. Suitable for researchers in computer graphics, computer vision, visualization, medical imaging, and geometric modeling as well as graduate and senior undergraduate students in computer science, Diffusion-Driven Wavelet Design for Shape Analysis presents recent research results in wavelet designs on 3D shapes and their applications in shape analysis. It explains how to apply the design methods to various types of 3D data, such as polygonal meshes, point clouds, manifolds, and volumetric images.Extensions of Wavelet Generation on Volumetric and Manifold DataThe first part of the book introduces design methods of wavelets on manifold data, incorporating interdisciplinary knowledge from differential geometry, functional analysis, Fourier transform, spectral graph theory, and stochastic processes. The authors show how wavelets are purely determined by the shape geometry and how wavelet transforms are computed as inner products of wavelet kernels and input functions.Wavelets for Solving Computer Graphics ProblemsThe second part presents applications in shape analysis/representation. The book looks at wavelets as spectral tools for geometry processing with filters in a joint space-frequency domain and examines wavelets as detail extractors for shape feature definition and detection. Going beyond these fundamental applications, the book also covers middle- and high-level applications, including shape matching, shape registration, and shape retrieval.Easy-to-Understand Implementations and AlgorithmsUnlike many other wavelet books, this one does not involve complicated mathematics. Instead, the book uses simplified formulations and illustrative example

The volume explores a wide-range of applications of computer vision in the field of healthcare. Computer vision, an interdisciplinary field that combines image processing, pattern recognition, and artificial intelligence, has the potential to revolutionize healthcare by enabling automated analysis and interpretation of medical images and videos. This chapter will provide an overview of the current advancements and potential future directions of computer vision applications in healthcare. Specifically, the book examines the application of computer vision techniques in various healthcare domains, including diagnostic imaging, surgical interventions, clinical decision support, and healthcare management. It reviews the challenges and limitations in implementing computer vision systems in real-world healthcare settings and discusses potential solutions.Overall, this book aims to shed light on the current and potential applications of computer vision in healthcare. It will summarize the state-of-the-art techniques, their benefits, challenges, and implications, while also discussing potential ethical considerations. The objective is to provide healthcare professionals, researchers, and practitioners with an understanding of the transformative impact of computer vision in the healthcare sector and inspire further research and development in this exciting field.

From Design Methods and Generation Schemes to State-of-the-Art ApplicationsWavelets are powerful tools for functional analysis and geometry processing, enabling researchers to determine the structure of data and analyze 3D shapes. Suitable for researchers in computer graphics, computer vision, visualization, medical imaging, and geometric modeling as well as graduate and senior undergraduate students in computer science, Diffusion-Driven Wavelet Design for Shape Analysis presents recent research results in wavelet designs on 3D shapes and their applications in shape analysis. It explains how to apply the design methods to various types of 3D data, such as polygonal meshes, point clouds, manifolds, and volumetric images.Extensions of Wavelet Generation on Volumetric and Manifold DataThe first part of the book introduces design methods of wavelets on manifold data, incorporating interdisciplinary knowledge from differential geometry, functional analysis, Fourier transform, spectral graph theory, and stochastic processes. The authors show how wavelets are purely determined by the shape geometry and how wavelet transforms are computed as inner products of wavelet kernels and input functions.Wavelets for Solving Computer Graphics ProblemsThe second part presents applications in shape analysis/representation. The book looks at wavelets as spectral tools for geometry processing with filters in a joint space-frequency domain and examines wavelets as detail extractors for shape feature definition and detection. Going beyond these fundamental applications, the book also covers middle- and high-level applications, including shape matching, shape registration, and shape retrieval.Easy-to-Understand Implementations and AlgorithmsUnlike many other wavelet books, this one does not involve complicated mathematics. Instead, the book uses simplified formulations and illustrative example

Predicting traffic patterns is done by analyzing the gathered data using machine learning algorithms. To forecast parking availability based on past data, the time of day, and special events, apply machine learning algorithms.