Consistent Correspondences for Shape and Image Problems

Establish consistent correspondences between different objects is a classic problem in computer science/vision. It helps to match highly similar objects in both 3D and 2D domain. Inthe 3D domain, finding consistent correspondences has been studying for more than 20 yearsand it is still a hot topic. In 2D domain, consistent correspondences can also help in puzzlesolving. However, only a few works are focused on this approach. In this thesis, we focuson finding consistent correspondences and extend to develop robust matching techniques inboth 3D shape segments and 2D puzzle solving. In the 3D domain, segment-wise matching isan important research problem that supports higher-level understanding of shapes in geometryprocessing. Many existing segment-wise matching techniques assume perfect input segmentation and would suffer from imperfect or over-segmented input. To handle this shortcoming,we propose multi-layer graphs (MLGs) to represent possible arrangements of partially mergedsegments of input shapes. We then adapt the diffusion pruning technique on the MLGs to findconsistent segment-wise matching. To obtain high-quality matching, we develop our own voting step which is able to remove inconsistent results, for finding hierarchically consistent correspondences as final output. We evaluate our technique with both quantitative and qualitativeexperiments on both man-made and deformable shapes. Experimental results demonstrate theeffectiveness of our technique when compared to two state-of-art methods. In the 2D domain,solving jigsaw puzzles is also a classic problem in computer vision with various applications.Over the past decades, many useful approaches have been introduced. Most existing worksuse edge-wise similarity measures for assembling puzzles with square pieces of the same size, and recent work innovates to use the loop constraint to improve efficiency and accuracy. Weobserve that most existing techniques cannot be easily extended to puzzles with rectangularpieces of arbitrary sizes, and no existing loop constraints can be used to model such challenging scenarios. We propose new matching approaches based on sub-edges/corners, modelledusing the MatchLift or diffusion framework to solve square puzzles with cycle consistency.We demonstrate the robustness of our approaches by comparing our methods with state-of-artmethods. We also show how puzzles with rectangular pieces of arbitrary sizes, or puzzles withtriangular and square pieces can be solved by our techniques

Quantifying Standing Dead Tree Volume and Structural Loss with Voxelized Terrestrial Lidar Data

Standing dead trees (SDTs) are an important forest component and impact a variety of ecosystem processes, yet the carbon pool dynamics of SDTs are poorly constrained in terrestrial carbon cycling models. The ability to model wood decay and carbon cycling in relation to detectable changes in tree structure and volume over time would greatly improve such models. The overall objective of this study was to provide automated aboveground volume estimates of SDTs and automated procedures to detect, quantify, and characterize structural losses over time with terrestrial lidar data. The specific objectives of this study were: 1) develop an automated SDT volume estimation algorithm providing accurate volume estimates for trees scanned in dense forests; 2) develop an automated change detection methodology to accurately detect and quantify SDT structural loss between subsequent terrestrial lidar observations; and 3) characterize the structural loss rates of pine and oak SDTs in southeastern Texas. A voxel-based volume estimation algorithm, “TreeVolX”, was developed and incorporates several methods designed to robustly process point clouds of varying quality levels. The algorithm operates on horizontal voxel slices by segmenting the slice into distinct branch or stem sections then applying an adaptive contour interpolation and interior filling process to create solid reconstructed tree models (RTMs). TreeVolX estimated large and small branch volume with an RMSE of 7.3% and 13.8%, respectively. A voxel-based change detection methodology was developed to accurately detect and quantify structural losses and incorporated several methods to mitigate the challenges presented by shifting tree and branch positions as SDT decay progresses. The volume and structural loss of 29 SDTs, composed of Pinus taeda and Quercus stellata, were successfully estimated using multitemporal terrestrial lidar observations over elapsed times ranging from 71 – 753 days. Pine and oak structural loss rates were characterized by estimating the amount of volumetric loss occurring in 20 equal-interval height bins of each SDT. Results showed that large pine snags exhibited more rapid structural loss in comparison to medium-sized oak snags in this study

Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

Conception and development of a mobile mixed reality medium for environment-related storytelling – a novel approach to virtual heritage

Das Ziel dieser Arbeit ist die Entwicklung eines neuartigen Virtual Heritage Mediums das User mit Hilfe von interaktiven Mixed-Reality Umgebungen und raumbezogenem Erzählen nicht nur räumlich mitten in eine Geschichte hineinversetzt, sondern auch aktiv in diese einbezieht. Dies wird erreicht, indem das Videobild eines getrackten Smartphones mit perspektivisch stimmigen Echtzeit-3D Inhalten überlagert wird. Der User kann diese Mixed-Reality Umgebung erkunden, die Handlungen von 3D Charakteren beobachten sowie mit ihnen und virtuellen Artefakten interagieren. Dieses Medium bietet folglich die Möglichkeit mediale Geschichten in echten Räumen zu erzählen sowie ein immersives und user-involvierendes Medienerlebnis. Diese Arbeit wird den Einsatz dieses Mediums speziell für Kulturvermittlungszwecke fokussieren. Diese Arbeit wird zunächst die technischen Anforderungen und Umsetzungsmöglichkeiten dieses Vorhabens mittels Unity für iPhone / iOS 7 untersuchen. Die Belegung der Ergebnisse erfolgt durch die anschließende Realisierung eines Prototypen.The goal of this thesis is to develop a novel type of virtual heritage medium that utilises the combined immersive and engaging potentials of interactive mixed reality environments and spatial narratives. Concretely, this is achieved through depth-sensitive compositing of real-time 3D content into the live-video of a tracked smartphone. The user can explore this mixed reality environment, watch the actions of staged 3D characters as well as interact with them and virtual artifacts. This medium would therefore provide possibilities for telling stories in direct context with existing environments along with an immersive and engaging media experience. This work will mainly focus on how this medium can be used as an edutainment medium in sites of cultural heritage. This thesis will focus on establishing the technical requirements and realisation possibilities for implementation in Unity on iPhone 5 / iOS 7. Subsequently, a prototype is implemented in order to prove the research results

OSS (Outer Solar System): A fundamental and planetary physics mission to Neptune, Triton and the Kuiper Belt

The present OSS mission continues a long and bright tradition by associating the communities of fundamental physics and planetary sciences in a single mission with ambitious goals in both domains. OSS is an M-class mission to explore the Neptune system almost half a century after flyby of the Voyager 2 spacecraft. Several discoveries were made by Voyager 2, including the Great Dark Spot (which has now disappeared) and Triton's geysers. Voyager 2 revealed the dynamics of Neptune's atmosphere and found four rings and evidence of ring arcs above Neptune. Benefiting from a greatly improved instrumentation, it will result in a striking advance in the study of the farthest planet of the Solar System. Furthermore, OSS will provide a unique opportunity to visit a selected Kuiper Belt object subsequent to the passage of the Neptunian system. It will consolidate the hypothesis of the origin of Triton as a KBO captured by Neptune, and improve our knowledge on the formation of the Solar system. The probe will embark instruments allowing precise tracking of the probe during cruise. It allows to perform the best controlled experiment for testing, in deep space, the General Relativity, on which is based all the models of Solar system formation. OSS is proposed as an international cooperation between ESA and NASA, giving the capability for ESA to launch an M-class mission towards the farthest planet of the Solar system, and to a Kuiper Belt object. The proposed mission profile would allow to deliver a 500 kg class spacecraft. The design of the probe is mainly constrained by the deep space gravity test in order to minimise the perturbation of the accelerometer measurement.Comment: 43 pages, 10 figures, Accepted to Experimental Astronomy, Special Issue Cosmic Vision. Revision according to reviewers comment

NASA Tech Briefs, October 2010

Topics covered include: Hybrid Architecture Active Wavefront Sensing and Control; Carbon-Nanotube-Based Chemical Gas Sensor; Aerogel-Positronium Technology for the Detection of Small Quantities of Organic and/or Toxic Materials; Graphene-Based Reversible Nano-Switch/Sensor Schottky Diode; Inductive Non-Contact Position Sensor; High-Temperature Surface-Acoustic-Wave Transducer; Grid-Sphere Electrodes for Contact with Ionospheric Plasma; Enabling IP Header Compression in COTS Routers via Frame Relay on a Simplex Link; Ka-Band SiGe Receiver Front-End MMIC for Transponder Applications; Robust Optimization Design Algorithm for High-Frequency TWTs; Optimal and Local Connectivity Between Neuron and Synapse Array in the Quantum Dot/Silicon Brain; Method and Circuit for In-Situ Health Monitoring of Solar Cells in Space; BGen: A UML Behavior Network Generator Tool; Platform for Post-Processing Waveform-Based NDE; Electrochemical Hydrogen Peroxide Generator; Fabrication of Single, Vertically Aligned Carbon Nanotubes in 3D Nanoscale Architectures; Process to Create High-Fidelity Lunar Dust Simulants; Lithium-Ion Electrolytes Containing Phosphorous-Based, Flame-Retardant Additives; InGaP Heterojunction Barrier Solar Cells; Straight-Pore Microfilter with Efficient Regeneration; Determining Shear Stress Distribution in a Laminate; Self-Adjusting Liquid Injectors for Combustors; Handling Qualities Prediction of an F-16XL-Based Reduced Sonic Boom Aircraft; Tele-Robotic ATHLETE Controller for Kinematics - TRACK; Three-Wheel Brush-Wheel Sampler; Heterodyne Interferometer Angle Metrology; Aligning Astronomical Telescopes via Identification of Stars; Generation of Optical Combs in a WGM Resonator from a Bichromatic Pump; Large-Format AlGaN PIN Photodiode Arrays for UV Images; Fiber-Coupled Planar Light-Wave Circuit for Seed Laser Control in High Spectral Resolution Lidar Systems; On Calculating the Zero-Gravity Surface Figure of a Mirror; Optical Modification of Casimir Forces for Improved Function of Micro- and Nano-Scale Devices; Analysis, Simulation, and Verification of Knowledge-Based, Rule-Based, and Expert Systems; Core and Off-Core Processes in Systems Engineering; Digital Reconstruction Supporting Investigation of Mishaps; and Template Matching Approach to Signal Prediction

Exploring 3D Data and Beyond in a Low Data Regime

3D object classification of point clouds is an essential task as laser scanners, or other depth sensors, producing point clouds are now a commodity on, e.g., autonomous vehicles, surveying vehicles, service robots, and drones. There have been fewer advances using deep learning methods in the area of point clouds compared to 2D images and videos, partially because the data in a point cloud are typically unordered as opposed to the pixels in a 2D image, which implies standard deep learning architectures are not suitable. Additionally, we identify there is a shortcoming of labelled 3D data in many computer vision tasks, as collecting 3D data is significantly more costly and difficult. This implies using zero- or few-shot learning approaches, where some classes have not been observed often or at all during training. As our first objective, we study the problem of 3D object classification of point clouds in a supervised setting where there are labelled samples for each class in the dataset. To this end, we introduce the {3DCapsule}, which is a 3D extension of the recently introduced Capsule concept by Hinton et al. that makes it applicable to unordered point sets. The 3DCapsule is a drop-in replacement of the commonly used fully connected classifier. It is demonstrated that when the 3DCapsule is applied to contemporary 3D point set classification architectures, it consistently shows an improvement, in particular when subjected to noisy data. We then turn our attention to the problem of 3D object classification of point clouds in a Zero-shot Learning (ZSL) setting, where there are no labelled data for some classes. Several recent 3D point cloud recognition algorithms are adapted to the ZSL setting with some necessary changes to their respective architectures. To the best of our knowledge, at the time, this was the first attempt to classify unseen 3D point cloud objects in a ZSL setting. A standard protocol (which includes the choice of datasets and determines the seen/unseen split) to evaluate such systems is also proposed. In the next contribution, we address the hubness problem on 3D point cloud data, which is when a model is biased to predict only a few particular labels for most of the test instances. To this end, we propose a loss function which is useful for both Zero-Shot and Generalized Zero-Shot Learning. Besides, we tackle 3D object classification of point clouds in a different setting, called the transductive setting, wherein the test samples are allowed to be observed during the training stage but then as unlabelled data. We extend, for the first time, transductive Zero-Shot Learning (ZSL) and Generalized Zero-Shot Learning (GZSL) approaches to the domain of 3D point cloud classification by developing a novel triplet loss that takes advantage of the unlabeled test data. While designed for the task of 3D point cloud classification, the method is also shown to be applicable to the more common use-case of 2D image classification. Lastly, we study the Generalized Zero-Shot Learning (GZSL) problem in the 2D image domain. However, we also demonstrate that our proposed method is applicable to 3D point cloud data. We propose using a mixture of subspaces which represents input features and semantic information in a way that reduces the imbalance between seen and unseen prediction scores. Subspaces define the cluster structure of the visual domain and help describe the visual and semantic domain considering the overall distribution of the data