Bücher der Reihe SpringerBriefs in Signal Processing

This SpringerBrief presents novel methods of approaching challenging problems in the reconstruction of accurate 3D models and serves as an introduction for further 3D reconstruction methods. It develops a 3D reconstruction system that produces accurate results by cascading multiple novel loop detection, sifting, and optimization methods.The authors offer a fast point cloud registration method that utilizes optimized randomness in random sample consensus for surface loop detection. The text also proposes two methods  for surface-loop sifting. One is supported by a sparse-feature-based optimization graph. This graph is more robust to different scan patterns than earlier methods and can cope with tracking failure and recovery. The other is an offline algorithm that can sift loop detections based on their impact on loop optimization results and which is enabled by a dense map posterior metric for 3D reconstruction and mapping performance evaluation works without any costly ground-truth data.The methods presented in Towards Optimal Point Cloud Processing for 3D Reconstruction will be of assistance to researchers developing 3D modelling methods and to workers in the wide variety of fields that exploit such technology including metrology, geological animation and mass customization in smart manufacturing.

The objective of this book is to provide the reader with a comprehensive survey of the topic compressed sensing in information retrieval and signal detection with privacy preserving functionality without compromising the performance of the embedding in terms of accuracy or computational efficiency.

This book gives a comprehensive and systematic review of secure compressive sensing (CS) for applications in various fields such as image processing, pattern recognition, Internet of things (IoT), and cloud computing.

This book demonstrates how nonlinear/non-Gaussian Bayesian time series estimation methods were used to produce a probability distribution of potential MH370 flight paths.

This book offers an overview of traditional big visual data analysis approaches and provides state-of-the-art solutions for several scene comprehension problems, indoor/outdoor classification, outdoorscene classification, and outdoor scene layout estimation.

This book first introduces the background of spatial audio reproduction, with different types of audio content and for different types of playback systems.

The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods.

This book explains how to measure the security of a watermarking scheme, how to design secure schemes but also how to attack popular watermarking schemes. This book is useful to industrial practitioners who would like to increase the security of their watermarking applications and for academics to quickly master this fascinating domain.

This book introduces various signal processing approaches to enhance physical layer secrecy in multi-antenna wireless systems.

This book provides readers a complete and self-contained set of knowledge about dependent source separation, including the latest development in this field. The book further focuses on the non-negativity based methods, the time-frequency analysis based methods, and the pre-coding based methods, respectively.

This book provides the new results in wavelet filter banks based feature extraction, and the classifier in the field of iris image recognition. This book contains the compilation of basic material on the design of wavelets that avoids reading many different books.

This book presents a survey of the state-of-the art in the exciting and timely topic of compressed sensing for distributed systems. To help the reader become familiar with the theory and algorithms presented, accompanying software is made available on the authors' web site, implementing several of the algorithms described in the book.

This book will provide a comprehensive overview on human action analysis with randomized trees. We describe how the randomized trees can be used for action classification, action detection, action search, and action prediction.

This book will first introduce classic graph-cut segmentation algorithms and then discuss state-of-the-art techniques, including graph matching methods, region merging and label propagation, clustering methods, and segmentation methods based on edge detection.