Parallelisation of greedy algorithms for compressive sensing reconstruction

Compressive Sensing (CS) is a technique which allows a signal to be compressed at the same time as it is captured. The process of capturing and simultaneously compressing the signal is represented as linear sampling, which can encompass a variety of physical processes or signal processing. Instead of explicitly identifying redundancies in the source signal, CS relies on the property of sparsity in order to reconstruct the compressed signal. While linear sampling is much less burdensome than conventional compression, this is more than made up for by the high computational cost of reconstructing a signal which has been captured using CS. Even when using some of the fastest reconstruction techniques, known as greedy pursuits, reconstruction of large problems can pose a significant burden, consuming a great deal of memory as well as compute time. Parallel computing is the foundation of the field of High Performance Computing (HPC). Modern supercomputers are generally composed of large clusters of standard servers, with a dedicated low-latency high-bandwidth interconnect network. On such a cluster, an appropriately written program can harness vast quantities of memory and computational power. However, in order to exploit a parallel compute resource, an algorithm usually has to be redesigned from the ground up. In this thesis I describe the development of parallel variants of two algorithms commonly used in CS reconstruction, Matching Pursuit (MP) and Orthogonal Matching Pursuit (OMP), resulting in the new distributed compute algorithms DistMP and DistOMP. I present the results from experiments showing how DistMP and DistOMP can utilise a compute cluster to solve CS problems much more quickly than a single computer could alone. Speed-up of as much as a factor of 76 is observed with DistMP when utilising 210 workers across 14 servers, compared to a single worker. Finally, I demonstrate how DistOMP can solve a problem with a 429GB equivalent sampling matrix in as little as 62 minutes using a 16-node compute cluster.Funded by an ICASE award from the Engineering and Physical Sciences Research Council, with sponsorship provided by Thales Research and Technology

Geo-tagging and privacy-preservation in mobile cloud computing

With the emerge of the cloud computing service and the explosive growth of the mobile devices and applications, mobile computing technologies and cloud computing technologies have been drawing significant attentions. Mobile cloud computing, with the synergy between the cloud and mobile technologies, has brought us new opportunities to develop novel and practical systems such as mobile multimedia systems and cloud systems that provide collaborative data-mining services for data from disparate owners (e.g., mobile users). However, it also creates new challenges, e.g., the algorithms deployed in the computationally weak mobile device require higher efficiency, and introduces new problems such as the privacy concern when the private data is shared in the cloud for collaborative data-mining. The main objectives of this dissertation are: 1. to develop practical systems based on the unique features of mobile devices (i.e., all-in-one computing platform and sensors) and the powerful computing capability of the cloud; 2. to propose solutions protecting the data privacy when the data from disparate owners are shared in the cloud for collaborative data-mining. We first propose a mobile geo-tagging system. It is a novel, accurate and efficient image and video based remote target localization and tracking system using the Android smartphone. To cope with the smartphones' computational limitation, we design light-weight image/video processing algorithms to achieve a good balance between estimation accuracy and computational complexity. Our system is first of its kind and we provide first hand real-world experimental results, which demonstrate that our system is feasible and practicable. To address the privacy concern when data from disparate owners are shared in the cloud for collaborative data-mining, we then propose a generic compressive sensing (CS) based secure multiparty computation (MPC) framework for privacy-preserving collaborative data-mining in which data mining is performed in the CS domain. We perform the CS transformation and reconstruction processes with MPC protocols. We modify the original orthogonal matching pursuit algorithm and develop new MPC protocols so that the CS reconstruction process can be implemented using MPC. Our analysis and experimental results show that our generic framework is capable of enabling privacy preserving collaborative data-mining. The proposed framework can be applied to many privacy preserving collaborative data-mining and signal processing applications in the cloud. We identify an application scenario that requires simultaneously performing secure watermark detection and privacy preserving multimedia data storage. We further propose a privacy preserving storage and secure watermark detection framework by adopting our generic framework to address such a requirement. In our secure watermark detection framework, the multimedia data and secret watermark pattern are presented to the cloud for secure watermark detection in a compressive sensing domain to protect the privacy. We also give mathematical and statistical analysis to derive the expected watermark detection performance in the compressive sensing domain, based on the target image, watermark pattern and the size of the compressive sensing matrix (but without the actual CS matrix), which means that the watermark detection performance in the CS domain can be estimated during the watermark embedding process. The correctness of the derived performance has been validated by our experiments. Our theoretical analysis and experimental results show that secure watermark detection in the compressive sensing domain is feasible. By taking advantage of our mobile geo-tagging system and compressive sensing based privacy preserving data-mining framework, we develop a mobile privacy preserving collaborative filtering system. In our system, mobile users can share their personal data with each other in the cloud and get daily activity recommendations based on the data-mining results generated by the cloud, without leaking the privacy and secrecy of the data to other parties. Experimental results demonstrate that the proposed system is effective in enabling efficient mobile privacy preserving collaborative filtering services.Includes bibliographical references (pages 126-133)

Remote Sensing Data Compression

A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin

Adaptive Sensing and Processing for Some Computer Vision Problems

This dissertation is concerned with adaptive sensing and processing in computer vision, specifically through the application of computer vision techniques to non-standard sensors. In the first part, we adapt techniques designed to solve the classical computer vision problem of gradient-based surface reconstruction to the problem of phase unwrapping that presents itself in applications such as interferometric synthetic aperture radar. Specifically, we propose a new formulation of and solution to the classical two-dimensional phase unwrapping problem. As is usually done, we use the wrapped principal phase gradient field as a measurement of the absolute phase gradient field. Since this model rarely holds in practice, we explicitly enforce integrability of the gradient measurements through a sparse error-correction model. Using a novel energy-minimization functional, we formulate the phase unwrapping task as a generalized lasso problem. We then jointly estimate the absolute phase and the sparse measurement errors using the alternating direction method of multipliers (ADMM) algorithm. Using an interferometric synthetic aperture radar noise model, we evaluate our technique for several synthetic surfaces and compare the results to recently-proposed phase unwrapping techniques. Our method applies new ideas from convex optimization and sparse regularization to this well-studied problem. In the second part, we consider the problem of controlling and processing measurements from a non-traditional, compressive sensing (CS) camera in real time. We focus on how to control the number of measurements it acquires such that this number remains proportional to the amount of foreground information currently present in the scene under observations. To this end, we provide two novel adaptive-rate CS strategies for sparse, time-varying signals using side information. The first method utilizes extra cross-validation measurements, and the second exploits extra low-resolution measurements. Unlike the majority of current CS techniques, we do not assume that we know an upper bound on the number of significant coefficients pertaining to the images that comprise the video sequence. Instead, we use the side information to predict this quantity for each upcoming image. Our techniques specify a fixed number of spatially-multiplexed CS measurements to acquire, and they adjust this quantity from image to image. Our strategies are developed in the specific context of background subtraction for surveillance video, and we experimentally validate the proposed methods on real video sequences. Finally, we consider a problem motivated by the application of active pan-tilt-zoom (PTZ) camera control in response to visual saliency. We extend the classical notion of this concept to multi-image data collected using a stationary PTZ camera by requiring consistency: the property that each saliency map in the set of those that are generated should assign the same saliency value to distinct regions of the environment that appear in more than one image. We show that processing each image independently will often fail to provide a consistent measure of saliency, and that using an image mosaic to quantify saliency suffers from several drawbacks. We then propose ray saliency: a mosaic-free method for calculating a consistent measure of bottom-up saliency. Experimental results demonstrating the effectiveness of the proposed approach are presented

ADDRESSING THREE PROBLEMS IN EMBEDDED SYSTEMS VIA COMPRESSIVE SENSING BASED METHODS

Compressive sensing is a mathematical theory concerning exact/approximate recovery of sparse/compressible vectors using the minimum number of measurements called projections. Its theory covers topics such as l1 optimisation, dimensionality reduction, information preserving projection matrices, random projection matrices and others. In this thesis we extend and use the theory of compressive sensing to address the challenges of limited computation power and energy supply in embedded systems. Three different problems are addressed. The first problem is to improve the efficiency of data gathering in wireless sensor networks. Many wireless sensor networks exhibit heterogeneity because of the environment. We leverage this heterogeneity and extend the theory of compressive sensing to cover non-uniform sampling to derive a new data collection protocol. We show that this protocol can realise a more accurate temporal-spatial profile for a given level of energy consumption. The second problem is to realise realtime background subtraction in embedded cameras. Background subtraction algorithms are normally computationally expensive because they use complex models to deal with subtle changes in background. Therefore existing background subtraction algorithms cannot provide realtime performance on embedded cameras which have limited processing power. By leveraging information preserving projection matrices, we derive a new background subtraction algorithm which is 4.6 times faster and more accurate than existing methods. We demonstrate that our background subtraction algorithm can realise realtime background subtraction and tracking in an embedded camera network. The third problem is to enable efficient and accurate face recognition on smartphones. The state-of-the-art face recognition algorithm is inspired by compressive sensing and is based on l1 optimisation. It also uses random projection matrices for dimensionality reduction. A key problem of using random projection matrices is that they give highly variable recognition accuracy. We propose an algorithm to optimise projection matrix to remove this performance variability. This means we can use fewer projections to achieve the same accuracy. This translates to a smaller l1 optimisation problem and reduces the computation time needed on smartphones, which have limited computation power. We demonstrate the performance of our proposed method on smartphones

Compressed Sensing in Multi-Signal Environments.

Technological advances and the ability to build cheap high performance sensors make it possible to deploy tens or even hundreds of sensors to acquire information about a common phenomenon of interest. The increasing number of sensors allows us to acquire ever more detailed information about the underlying scene that was not possible before. This, however, directly translates to increasing amounts of data that needs to be acquired, transmitted, and processed. The amount of data can be overwhelming, especially in applications that involve high-resolution signals such as images or videos. Compressed sensing (CS) is a novel acquisition and reconstruction scheme that is particularly useful in scenarios when high resolution signals are difficult or expensive to encode. When applying CS in a multi-signal scenario, there are several aspects that need to be considered such as the sensing matrix, the joint signal model, and the reconstruction algorithm. The purpose of this dissertation is to provide a complete treatment of these aspects in various multi-signal environments. Specific applications include video, multi-view imaging, and structural health monitoring systems. For each application, we propose a novel joint signal model that accurately captures the joint signal structure, and we tailor the reconstruction algorithm to each signal model to successfully recover the signals of interest.PHDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/98007/1/jaeypark_1.pd

TOWARD 3D RECONSTRUCTION OF STATIC AND DYNAMIC OBJECTS

The goal of image-based 3D reconstruction is to construct a spatial understanding of the world from a collection of images. For applications that seek to model generic real-world scenes, it is important that the reconstruction methods used are able to characterize both static scene elements (e.g. trees and buildings) as well as dynamic objects (e.g. cars and pedestrians). However, due to many inherent ambiguities in the reconstruction problem, recovering this 3D information with accuracy, robustness, and efficiency is a considerable challenge. To advance the research frontier for image-based 3D modeling, this dissertation focuses on three challenging problems in static scene and dynamic object reconstruction. We first target the problem of static scene depthmap estimation from crowd-sourced datasets (i.e. photos collected from the Internet). While achieving high-quality depthmaps using images taken under a controlled environment is already a difficult task, heterogeneous crowd-sourced data presents a unique set of challenges for multi-view depth estimation, including varying illumination and occasional occlusions. We propose a depthmap estimation method that demonstrates high accuracy, robustness, and scalability on a large number of photos collected from the Internet. Compared to static scene reconstruction, the problem of dynamic object reconstruction from monocular images is fundamentally ambiguous when not imposing any additional assumptions. This is because having only a single observation of an object is insufficient for valid 3D triangulation, which typically requires concurrent observations of the object from multiple viewpoints. Assuming that dynamic objects of the same class (e.g. all the pedestrians walking on a sidewalk) move in a common path in the real world, we develop a method that estimates the 3D positions of the dynamic objects from unstructured monocular images. Experiments on both synthetic and real datasets illustrate the solvability of the problem and the effectiveness of our approach. Finally, we address the problem of dynamic object reconstruction from a set of unsynchronized videos capturing the same dynamic event. This problem is of great interest because, due to the increased availability of portable capture devices, captures using multiple unsynchronized videos are common in the real world. To resolve the challenges that arises from non-concurrent captures and unknown temporal overlap among video streams, we propose a self-expressive dictionary learning framework, where the dictionary entries are defined as the collection of temporally varying structures. Experiments demonstrate the effectiveness of this approach to the previously unsolved problem.Doctor of Philosoph

Proceedings of the 35th WIC Symposium on Information Theory in the Benelux and the 4th joint WIC/IEEE Symposium on Information Theory and Signal Processing in the Benelux, Eindhoven, the Netherlands May 12-13, 2014

Compressive sensing (CS) as an approach for data acquisition has recently received much attention. In CS, the signal recovery problem from the observed data requires the solution of a sparse vector from an underdetermined system of equations. The underlying sparse signal recovery problem is quite general with many applications and is the focus of this talk. The main emphasis will be on Bayesian approaches for sparse signal recovery. We will examine sparse priors such as the super-Gaussian and student-t priors and appropriate MAP estimation methods. In particular, re-weighted l2 and re-weighted l1 methods developed to solve the optimization problem will be discussed. The talk will also examine a hierarchical Bayesian framework and then study in detail an empirical Bayesian method, the Sparse Bayesian Learning (SBL) method. If time permits, we will also discuss Bayesian methods for sparse recovery problems with structure; Intra-vector correlation in the context of the block sparse model and inter-vector correlation in the context of the multiple measurement vector problem

Sensor Signal and Information Processing II

In the current age of information explosion, newly invented technological sensors and software are now tightly integrated with our everyday lives. Many sensor processing algorithms have incorporated some forms of computational intelligence as part of their core framework in problem solving. These algorithms have the capacity to generalize and discover knowledge for themselves and learn new information whenever unseen data are captured. The primary aim of sensor processing is to develop techniques to interpret, understand, and act on information contained in the data. The interest of this book is in developing intelligent signal processing in order to pave the way for smart sensors. This involves mathematical advancement of nonlinear signal processing theory and its applications that extend far beyond traditional techniques. It bridges the boundary between theory and application, developing novel theoretically inspired methodologies targeting both longstanding and emergent signal processing applications. The topic ranges from phishing detection to integration of terrestrial laser scanning, and from fault diagnosis to bio-inspiring filtering. The book will appeal to established practitioners, along with researchers and students in the emerging field of smart sensors processing