A Survey on Joint Object Detection and Pose Estimation using Monocular Vision

In this survey we present a complete landscape of joint object detection and pose estimation methods that use monocular vision. Descriptions of traditional approaches that involve descriptors or models and various estimation methods have been provided. These descriptors or models include chordiograms, shape-aware deformable parts model, bag of boundaries, distance transform templates, natural 3D markers and facet features whereas the estimation methods include iterative clustering estimation, probabilistic networks and iterative genetic matching. Hybrid approaches that use handcrafted feature extraction followed by estimation by deep learning methods have been outlined. We have investigated and compared, wherever possible, pure deep learning based approaches (single stage and multi stage) for this problem. Comprehensive details of the various accuracy measures and metrics have been illustrated. For the purpose of giving a clear overview, the characteristics of relevant datasets are discussed. The trends that prevailed from the infancy of this problem until now have also been highlighted.Comment: Accepted at the International Joint Conference on Computer Vision and Pattern Recognition (CCVPR) 201

Storage and retrieval of vector beams of light in a multiple-degree-of-freedom quantum memory

The full structuration of light in the transverse plane, including intensity, phase and polarization, holds the promise of unprecedented capabilities for applications in classical optics as well as in quantum optics and information sciences. Harnessing special topologies can lead to enhanced focusing, data multiplexing or advanced sensing and metrology. Here we experimentally demonstrate the storage of such spatio-polarization-patterned beams into an optical memory. A set of vectorial vortex modes is generated via liquid crystal cell with topological charge in the optic axis distribution, and preservation of the phase and polarization singularities is demonstrated after retrieval, at the single-photon level. The realized multiple-degree-of-freedom memory can find applications in classical data processing but also in quantum network scenarios where structured states have been shown to provide promising attributes, such as rotational invariance

Multiscale Functional and Molecular Photoacoustic Tomography

Photoacoustic tomography (PAT) combines rich optical absorption contrast with the high spatial resolution of ultrasound at depths in tissue. The high scalability of PAT has enabled anatomical imaging of biological structures ranging from organelles to organs. The inherent functional and molecular imaging capabilities of PAT have further allowed it to measure important physiological parameters and track critical cellular activities. Integration of PAT with other imaging technologies provides complementary capabilities and can potentially accelerate the clinical translation of PAT

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Boosting solar energy harvesting with ordered nanostructures fabricated by anodic aluminum oxide templates

To date, technical development has boosted the efficiencies of solar energy conversion devices with conventional planar architectures to be close to the respective theoretical values, which are hard to be further improved without reforming the device structures. Alternatively, ordered nanostructure arrays have recently emerged as efficacious scaffolds to construct devices for converting energy more efficiently due to their advantageous optical effects. To meet the global energy requirements for producing renewable energy efficiently, a general approach is needed to fabricate diverse ordered nanostructure arrays. In the meantime, the approach should allow for fine tuning in every set of nanounits towards obtaining desired properties. Herein, we utilized anodic aluminum oxide (AAO) templates to provide a versatile method for constructing ordered nanostructure arrays from one to two dimensions. Firstly, arrays of one-dimensional Au nanowires comprising two components of pillar and truncated pyramid were fabricated. Then, periodic one-dimensional Janus hetero-nanostructures with programmable morphologies, compositions, dimensions, and interfacial junctions were realized. Finally, two-dimensional superlattice photonic crystals with two sets of nanopores were constructed via a combination of the AAO template and the structural replication technique. Subsequently, these as-obtained nanostructures were integrated into photoelectrochemical water-splitting cells and solar-to-thermal conversion systems, which significantly boosted solar energy harvesting performance. In conjunction with theoretical simulations, we further elucidated that the enhanced light harvesting ability can be ascribed to twofold facts: photonic effects and surface plasmon resonance which thus provide a route to manipulate light at the nanoscale.In dieser Dissertation habe ich drei Arten von hochgeordneten Nanostrukturen realisiert, einschließlich 1D-PTP-Au-Core / CdS-Shell-Array, Au-NW / TiO2-NT-Janus-Hetero-Nanostruktur-Array und 2D-Metall-SPhCs. Diese fortschrittlichen Architekturen könnten als vielseitige Gerüste zum Aufbau energiebezogener Geräte eingesetzt werden und haben ein großes Potenzial, die Gesamtleistung drastisch zu verbessern und die durch die planare Konfiguration auferlegten Grenzen zu durchbrechen. Insbesondere die geordneten Nanostruktur-Arrays mit mehreren Komponenten sind von großer Bedeutung, und die entsprechenden Geräte können die Vorteile dieser nanostrukturierten Komponenten kombinieren, wodurch die relevante Leistung systematisch verbessert wird. Darüber hinaus ermöglichen die hohe Regelmäßigkeit der Nanostrukturverteilung, die Gleichmäßigkeit der Nanounits sowie die steuerbaren Größen und Profile der Nanostruktur die resultierenden Architekturen als leistungsfähige Plattform, um die spezifischen Energieumwandlungsreaktionen mikroskopisch zu untersuchen. Diese Ergebnisse könnten wiederum die weitere Entwicklung der relevanten Geräte leiten

Holographic particle localization under multiple scattering

We introduce a novel framework that incorporates multiple scattering for large-scale 3D particle-localization using single-shot in-line holography. Traditional holographic techniques rely on single-scattering models which become inaccurate under high particle-density. We demonstrate that by exploiting multiple-scattering, localization is significantly improved. Both forward and back-scattering are computed by our method under a tractable recursive framework, in which each recursion estimates the next higher-order field within the volume. The inverse scattering is presented as a nonlinear optimization that promotes sparsity, and can be implemented efficiently. We experimentally reconstruct 100 million object voxels from a single 1-megapixel hologram. Our work promises utilization of multiple scattering for versatile large-scale applications