DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications

Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because of their success, CNN-based methods are becoming increasingly popular in Cosmology. Cosmological data often comes as spherical maps, which make the use of the traditional CNNs more complicated. The commonly used pixelization scheme for spherical maps is the Hierarchical Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere. The spherical CNN is constructed by representing the sphere as a graph. Graphs are versatile data structures that can act as a discrete representation of a continuous manifold. Using the graph-based representation, we define many of the standard CNN operations, such as convolution and pooling. With filters restricted to being radial, our convolutions are equivariant to rotation on the sphere, and DeepSphere can be made invariant or equivariant to rotation. This way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix sampling of the sphere. This approach is computationally more efficient than using spherical harmonics to perform convolutions. We demonstrate the method on a classification problem of weak lensing mass maps from two cosmological models and compare the performance of the CNN with that of two baseline classifiers. The results show that the performance of DeepSphere is always superior or equal to both of these baselines. For high noise levels and for data covering only a smaller fraction of the sphere, DeepSphere achieves typically 10% better classification accuracy than those baselines. Finally, we show how learned filters can be visualized to introspect the neural network.Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other author

Model-based Behavioural Tracking and Scale Invariant Features in Omnidirectional Matching

Two classical but crucial and unsolved problems in Computer Vision are treated in this thesis: tracking and matching. The first part of the thesis deals with tracking, studying two of its main difficulties: object representation model drift and total occlusions. The second part considers the problem of point matching between omnidirectional images and between omnidirectional and planar images. Model drift is a major problem of tracking when the object representation model is updated on-line. In this thesis, we have developed a visual tracking algorithm that simultaneously tracks and builds a model of the tracked object. The model is computed using an incremental PCA algorithm that allows to weight samples. Thus, model drift is avoided by weighting samples added to the model according to a measure of confidence on the tracked patch. Furthermore, we have introduced also spatial weights for weighting pixels and increasing tracking accuracy in some regions of the tracked object. Total occlusions are another major problem in visual tracking. Indeed, a total occlusion hides completely the tracked object, making visual information unavailable for tracking. For handling this kind of situations, common in unconstrained scenarios, the Model cOrruption and Total Occlusion Handling (MOTOH) framework is introduced. In this framework, in addition to the model drift avoidance scheme described above, a total occlusion detection procedure is introduced. When a total occlusion is detected, the tracker switches to behavioural-based tracking, where instead of guiding the tracker with visual information, a behavioural model of motion is employed. Finally, a Scale Invariant Feature Transform (SIFT) for omnidirectional images is developed. The proposed algorithm generates two types of local descriptors, Local Spherical Descriptors and Local Planar Descriptors. With the first ones, point matching between omnidirectional images can be performed, and with the second ones, the same matching process can be done but between omnidirectional and planar images. Furthermore, a planar to spherical mapping is introduced and an algorithm for its estimation is given. This mapping allows to extract objects from an omnidirectional image given their SIFT descriptors in a planar image

Single and multiple stereo view navigation for planetary rovers

© Cranfield UniversityThis thesis deals with the challenge of autonomous navigation of the ExoMars rover. The absence of global positioning systems (GPS) in space, added to the limitations of wheel odometry makes autonomous navigation based on these two techniques - as done in the literature - an inviable solution and necessitates the use of other approaches. That, among other reasons, motivates this work to use solely visual data to solve the robot’s Egomotion problem. The homogeneity of Mars’ terrain makes the robustness of the low level image processing technique a critical requirement. In the first part of the thesis, novel solutions are presented to tackle this specific problem. Detection of robust features against illumination changes and unique matching and association of features is a sought after capability. A solution for robustness of features against illumination variation is proposed combining Harris corner detection together with moment image representation. Whereas the first provides a technique for efficient feature detection, the moment images add the necessary brightness invariance. Moreover, a bucketing strategy is used to guarantee that features are homogeneously distributed within the images. Then, the addition of local feature descriptors guarantees the unique identification of image cues. In the second part, reliable and precise motion estimation for the Mars’s robot is studied. A number of successful approaches are thoroughly analysed. Visual Simultaneous Localisation And Mapping (VSLAM) is investigated, proposing enhancements and integrating it with the robust feature methodology. Then, linear and nonlinear optimisation techniques are explored. Alternative photogrammetry reprojection concepts are tested. Lastly, data fusion techniques are proposed to deal with the integration of multiple stereo view data. Our robust visual scheme allows good feature repeatability. Because of this, dimensionality reduction of the feature data can be used without compromising the overall performance of the proposed solutions for motion estimation. Also, the developed Egomotion techniques have been extensively validated using both simulated and real data collected at ESA-ESTEC facilities. Multiple stereo view solutions for robot motion estimation are introduced, presenting interesting benefits. The obtained results prove the innovative methods presented here to be accurate and reliable approaches capable to solve the Egomotion problem in a Mars environment

Texture and Colour in Image Analysis

Research in colour and texture has experienced major changes in the last few years. This book presents some recent advances in the field, specifically in the theory and applications of colour texture analysis. This volume also features benchmarks, comparative evaluations and reviews

Pattern Recognition

Pattern recognition is a very wide research field. It involves factors as diverse as sensors, feature extraction, pattern classification, decision fusion, applications and others. The signals processed are commonly one, two or three dimensional, the processing is done in real- time or takes hours and days, some systems look for one narrow object class, others search huge databases for entries with at least a small amount of similarity. No single person can claim expertise across the whole field, which develops rapidly, updates its paradigms and comprehends several philosophical approaches. This book reflects this diversity by presenting a selection of recent developments within the area of pattern recognition and related fields. It covers theoretical advances in classification and feature extraction as well as application-oriented works. Authors of these 25 works present and advocate recent achievements of their research related to the field of pattern recognition

Data catalog of satellite experiments

The availability of space science data, a description of the data, and a description of the services supplied by the National Space Science Data Center (NSSDC) is presented. A series of cumulative indexes that reference the data descriptions contain: (1) a chronological listing of all spacecraft, experiments, and data descriptions; (2) an index of all spacecraft described, identified by common names and alternate names; (3) a listing of the original experiment institutions for experiments described; (4) a listing of the investigators associated with the experiments and their current affiliations; and (5) two displays of information about experiment data coverage for fields and particle data and a listing of all experiments sorted by phenomenon measured

Recent Advances in Signal Processing

The signal processing task is a very critical issue in the majority of new technological inventions and challenges in a variety of applications in both science and engineering fields. Classical signal processing techniques have largely worked with mathematical models that are linear, local, stationary, and Gaussian. They have always favored closed-form tractability over real-world accuracy. These constraints were imposed by the lack of powerful computing tools. During the last few decades, signal processing theories, developments, and applications have matured rapidly and now include tools from many areas of mathematics, computer science, physics, and engineering. This book is targeted primarily toward both students and researchers who want to be exposed to a wide variety of signal processing techniques and algorithms. It includes 27 chapters that can be categorized into five different areas depending on the application at hand. These five categories are ordered to address image processing, speech processing, communication systems, time-series analysis, and educational packages respectively. The book has the advantage of providing a collection of applications that are completely independent and self-contained; thus, the interested reader can choose any chapter and skip to another without losing continuity

Radar Technology

In this book “Radar Technology”, the chapters are divided into four main topic areas: Topic area 1: “Radar Systems” consists of chapters which treat whole radar systems, environment and target functional chain. Topic area 2: “Radar Applications” shows various applications of radar systems, including meteorological radars, ground penetrating radars and glaciology. Topic area 3: “Radar Functional Chain and Signal Processing” describes several aspects of the radar signal processing. From parameter extraction, target detection over tracking and classification technologies. Topic area 4: “Radar Subsystems and Components” consists of design technology of radar subsystem components like antenna design or waveform design

Mixed Structural Models for 3D Audio in Virtual Environments

In the world of ICT, strategies for innovation and development are increasingly focusing on applications that require spatial representation and real-time interaction with and within 3D media environments. One of the major challenges that such applications have to address is user-centricity, reflecting e.g. on developing complexity-hiding services so that people can personalize their own delivery of services. In these terms, multimodal interfaces represent a key factor for enabling an inclusive use of the new technology by everyone. In order to achieve this, multimodal realistic models that describe our environment are needed, and in particular models that accurately describe the acoustics of the environment and communication through the auditory modality. Examples of currently active research directions and application areas include 3DTV and future internet, 3D visual-sound scene coding, transmission and reconstruction and teleconferencing systems, to name but a few. The concurrent presence of multimodal senses and activities make multimodal virtual environments potentially flexible and adaptive, allowing users to switch between modalities as needed during the continuously changing conditions of use situation. Augmentation through additional modalities and sensory substitution techniques are compelling ingredients for presenting information non-visually, when the visual bandwidth is overloaded, when data are visually occluded, or when the visual channel is not available to the user (e.g., for visually impaired people). Multimodal systems for the representation of spatial information will largely benefit from the implementation of audio engines that have extensive knowledge of spatial hearing and virtual acoustics. Models for spatial audio can provide accurate dynamic information about the relation between the sound source and the surrounding environment, including the listener and his/her body which acts as an additional filter. Indeed, this information cannot be substituted by any other modality (i.e., visual or tactile). Nevertheless, today's spatial representation of audio within sonification tends to be simplistic and with poor interaction capabilities, being multimedia systems currently focused on graphics processing mostly, and integrated with simple stereo or multi-channel surround-sound. On a much different level lie binaural rendering approaches based on headphone reproduction, taking into account that possible disadvantages (e.g. invasiveness, non-flat frequency responses) are counterbalanced by a number of desirable features. Indeed, these systems might control and/or eliminate reverberation and other acoustic effects of the real listening space, reduce background noise, and provide adaptable and portable audio displays, which are all relevant aspects especially in enhanced contexts. Most of the binaural sound rendering techniques currently exploited in research rely on the use of Head-Related Transfer Functions (HRTFs), i.e. peculiar filters that capture the acoustic effects of the human head and ears. HRTFs allow loyal simulation of the audio signal that arrives at the entrance of the ear canal as a function of the sound source's spatial position. HRTF filters are usually presented under the form of acoustic signals acquired on dummy heads built according to mean anthropometric measurements. Nevertheless, anthropometric features of the human body have a key role in HRTF shaping: several studies have attested how listening to non-individual binaural sounds results in evident localization errors. On the other hand, individual HRTF measurements on a significant number of subjects result both time- and resource-expensive. Several techniques for synthetic HRTF design have been proposed during the last two decades and the most promising one relies on structural HRTF models. In this revolutionary approach, the most important effects involved in spatial sound perception (acoustic delays and shadowing due to head diffraction, reflections on pinna contours and shoulders, resonances inside the ear cavities) are isolated and modeled separately with a corresponding filtering element. HRTF selection and modeling procedures can be determined by physical interpretation: parameters of each rendering blocks or selection criteria can be estimated from real and simulated data and related to anthropometric geometries. Effective personal auditory displays represent an innovative breakthrough for a plethora of applications and structural approach can also allow for effective scalability depending on the available computational resources or bandwidth. Scenes with multiple highly realistic audiovisual objects are easily managed exploiting parallelism of increasingly ubiquitous GPUs (Graphics Processing Units). Building individual headphone equalization with perceptually robust inverse filtering techniques represents a fundamental step towards the creation of personal virtual auditory displays (VADs). To this regard, several examples might benefit from these considerations: multi-channel downmix over headphones, personal cinema, spatial audio rendering in mobile devices, computer-game engines and individual binaural audio standards for movie and music production. This thesis presents a family of approaches that overcome the current limitations of headphone-based 3D audio systems, aiming at building personal auditory displays through structural binaural audio models for an immersive sound reproduction. The resulting models allow for an interesting form of content adaptation and personalization, since they include parameters related to the user's anthropometry in addition to those related to the sound sources and the environment. The covered research directions converge to a novel framework for synthetic HRTF design and customization that combines the structural modeling paradigm with other HRTF selection techniques (inspired by non-individualized HRTF selection procedures) and represents the main novel contribution of this thesis: the Mixed Structural Modeling (MSM) approach considers the global HRTF as a combination of structural components, which can be chosen to be either synthetic or recorded components. In both cases, customization is based on individual anthropometric data, which are used to either fit the model parameters or to select a measured/simulated component within a set of available responses. The definition and experimental validation of the MSM approach addresses several pivotal issues towards the acquisition and delivery of binaural sound scenes and designing guidelines for personalized 3D audio virtual environments holding the potential of novel forms of customized communication and interaction with sound and music content. The thesis also presents a multimodal interactive system which is used to conduct subjective test on multi-sensory integration in virtual environments. Four experimental scenarios are proposed in order to test the capabilities of auditory feedback jointly to tactile or visual modalities. 3D audio feedback related to user’s movements during simple target following tasks is tested as an applicative example of audio-visual rehabilitation system. Perception of direction of footstep sounds interactively generated during walking and provided through headphones highlights how spatial information can clarify the semantic congruence between movement and multimodal feedback. A real time, physically informed audio-tactile interactive system encodes spatial information in the context of virtual map presentation with particular attention to orientation and mobility (O&M) learning processes addressed to visually impaired people. Finally, an experiment analyzes the haptic estimation of size of a virtual 3D object (a stair-step) whereas the exploration is accompanied by a real-time generated auditory feedback whose parameters vary as a function of the height of the interaction point. The collected data from these experiments suggest that well-designed multimodal feedback, exploiting 3D audio models, can definitely be used to improve performance in virtual reality and learning processes in orientation and complex motor tasks, thanks to the high level of attention, engagement, and presence provided to the user. The research framework, based on the MSM approach, serves as an important evaluation tool with the aim of progressively determining the relevant spatial attributes of sound for each application domain. In this perspective, such studies represent a novelty in the current literature on virtual and augmented reality, especially concerning the use of sonification techniques in several aspects of spatial cognition and internal multisensory representation of the body. This thesis is organized as follows. An overview of spatial hearing and binaural technology through headphones is given in Chapter 1. Chapter 2 is devoted to the Mixed Structural Modeling formalism and philosophy. In Chapter 3, topics in structural modeling for each body component are studied, previous research and two new models, i.e. near-field distance dependency and external-ear spectral cue, are presented. Chapter 4 deals with a complete case study of the mixed structural modeling approach and provides insights about the main innovative aspects of such modus operandi. Chapter 5 gives an overview of number of a number of proposed tools for the analysis and synthesis of HRTFs. System architectural guidelines and constraints are discussed in terms of real-time issues, mobility requirements and customized audio delivery. In Chapter 6, two case studies investigate the behavioral importance of spatial attribute of sound and how continuous interaction with virtual environments can benefit from using spatial audio algorithms. Chapter 7 describes a set of experiments aimed at assessing the contribution of binaural audio through headphones in learning processes of spatial cognitive maps and exploration of virtual objects. Finally, conclusions are drawn and new research horizons for further work are exposed in Chapter 8

Advances in Robotics, Automation and Control

The book presents an excellent overview of the recent developments in the different areas of Robotics, Automation and Control. Through its 24 chapters, this book presents topics related to control and robot design; it also introduces new mathematical tools and techniques devoted to improve the system modeling and control. An important point is the use of rational agents and heuristic techniques to cope with the computational complexity required for controlling complex systems. Through this book, we also find navigation and vision algorithms, automatic handwritten comprehension and speech recognition systems that will be included in the next generation of productive systems developed by man