Dense light field coding: a survey

Light Field (LF) imaging is a promising solution for providing more immersive and closer to reality multimedia experiences to end-users with unprecedented creative freedom and flexibility for applications in different areas, such as virtual and augmented reality. Due to the recent technological advances in optics, sensor manufacturing and available transmission bandwidth, as well as the investment of many tech giants in this area, it is expected that soon many LF transmission systems will be available to both consumers and professionals. Recognizing this, novel standardization initiatives have recently emerged in both the Joint Photographic Experts Group (JPEG) and the Moving Picture Experts Group (MPEG), triggering the discussion on the deployment of LF coding solutions to efficiently handle the massive amount of data involved in such systems. Since then, the topic of LF content coding has become a booming research area, attracting the attention of many researchers worldwide. In this context, this paper provides a comprehensive survey of the most relevant LF coding solutions proposed in the literature, focusing on angularly dense LFs. Special attention is placed on a thorough description of the different LF coding methods and on the main concepts related to this relevant area. Moreover, comprehensive insights are presented into open research challenges and future research directions for LF coding.info:eu-repo/semantics/publishedVersio

Block-classified bidirectional motion compensation scheme for wavelet-decomposed digital video

In this paper the authors introduce a block-classified bidirectional motion compensation scheme for the previously developed wavelet-based video codec, where multiresolution motion estimation is performed in the wavelet domain. The frame classification structure described in this paper is similar to that used in the MPEG standard. Specifically, the I-frames are intraframe coded, the P-frames are interpolated from a previous I- or a P-frame, and the B-frames are bidirectional interpolated frames. They apply this frame classification structure to the wavelet domain with variable block sizes and multiresolution representation. They use a symmetric bidirectional scheme for the B-frames and classify the motion blocks as intraframe, compensated either from the preceding or the following frame, or bidirectional (i.e., compensated based on which type yields the minimum energy). They also introduce the concept of F-frames, which are analogous to P-frames but are predicted from the following frame only. This improves the overall quality of the reconstruction in a group of pictures (GOP) but at the expense of extra buffering. They also study the effect of quantization of the I-frames on the reconstruction of a GOP, and they provide intuitive explanation for the results. In addition, the authors study a variety of wavelet filter-banks to be used in a multiresolution motion-compensated hierarchical video codec

Using High-Resolution Glider Data and Biogeochemical Modeling to Investigate Phytoplankton Variability in the Ross Sea

As Earth’s climate changes, polar environments experience a disproportionate share of extreme shifts. Because the Ross Sea shelf has the highest annual productivity of any Antarctic continental shelf, this region is of particular interest when striving to characterize current and future changes in Antarctic systems. However, understanding of mesoscale variability of biogeochemical patterns in the Ross Sea and how this variability affects assemblage dynamics is incomplete. Furthermore, it is unknown how the Ross Sea may respond to projected warming, reduced summer sea ice concentrations, and shallower mixed layers during the next century. to investigate these dynamics and explore their consequences over the next century, high-resolution glider observations were analyzed and used in conjunction with a one-dimensional, data-assimilative biogeochemical-modeling framework. An analysis of glider observations from two latitudinal sections in the Ross Sea characterized mesoscale variability associated with the phytoplankton bloom and highlighted potential mechanisms driving change in the assemblage. In particular, an observed increase in the ratio of carbon to chlorophyll (C:Chl) suggested a marked transition from a phytoplankton assemblage dominated by Phaeocystis antarctica- to one dominated by diatoms. The expected control of phytoplankton variability by Modified Circumpolar Deep Water and mixed layer depth were shown to be insignificant relative to the effects of wind and sea surface temperature on the temporal/spatial scales measured by the glider. Additional glider measurements were used to force the Model of Ecosystem Dynamics, nutrient Utilisation, Sequestration and Acidification, which was adapted for use in the Ross Sea (MEDUSA-RS) to include both solitary and colonial forms of Phaeocystis antarctica. The impacts of climate-induced changes on Ross Sea phytoplankton were investigated with MEDUSA-RS using projections of physical drivers for mid- and late-21st century, and these experiments indicated increases of primary productivity and carbon export flux. Additional scenario experiments demonstrated that earlier availability of low light due to reduction of sea ice early in the growing season was the primary driver of simulated productivity increases over the next century; shallower mixed layer depths additionally contributed to changes of phytoplankton composition and export. Glider data were assimilated into MEDUSA-RS using the Marine Model Optimization Testbed (MarMOT) to optimize eight phytoplankton model parameters. Assimilation experiments that used different data subsets suggest that assimilating observations at the surface alone, as are typically available from remote-sensing platforms, may underestimate carbon export to depth and overestimate primary production. Experiments assimilating observations characteristic of a cruise-based sampling frequency produced a wide range of solutions, depending on which days were sampled, suggesting the potential for large errors in productivity and export. Finally, assimilating data from different spatial areas resulted in less variation of optimal solutions than assimilating data from different time periods in the bloom progression; these temporal differences are primarily driven by decreasing colonial P. antarctica growth rates, increasing colonial P. antarctica C:Chl, and faster sinking of colonies as the bloom progresses from the accumulation stage through dissipation. Overall, this dissertation research demonstrates the value of using bio-optical glider observations in conjunction with modeling to characterize phytoplankton dynamics in a remote marine ecosystem. High-resolution glider data are better able to resolve mesoscale physical-biological relationships, which are typically not discernible from lower frequency data, but it can be difficult to identify mechanistic relationships from in situ measurements alone. In addition, biogeochemical models can be used to extend insights gained by empirical observation, but application is often limited by the quantity and type of in situ data appropriate for evaluation and forcing. The use of gliders for facilitating development and operation of a lower trophic level model demonstrated the effectiveness of a synthetic approach that partly overcomes the individual limitations of these otherwise distinct approaches. Finally, the combination of these approaches is especially useful for gaining a better understanding of ecosystem dynamics in regions similar to the Ross Sea that are undergoing substantive climate-induced changes and where harsh conditions make other means of access difficult

An Insect-Inspired Target Tracking Mechanism for Autonomous Vehicles

Target tracking is a complicated task from an engineering perspective, especially where targets are small and seen against complex natural environments. Due to the high demand for robust target tracking algorithms a great deal of research has focused on this area. However, most engineering solutions developed for this purpose are often unreliable in real world conditions or too computationally expensive to be used in real-time applications. While engineering methods try to solve the problem of target detection and tracking by using high resolution input images, fast processors, with typically computationally expensive methods, a quick glance at nature provides evidence that practical real world solutions for target tracking exist. Many animals track targets for predation, territorial or mating purposes and with millions of years of evolution behind them, it seems reasonable to assume that these solutions are highly efficient. For instance, despite their low resolution compound eyes and tiny brains, many flying insects have evolved superb abilities to track targets in visual clutter even in the presence of other distracting stimuli, such as swarms of prey and conspecifics. The accessibility of the dragonfly for stable electrophysiological recordings makes this insect an ideal and tractable model system for investigating the neuronal correlates for complex tasks such as target pursuit. Studies on dragonflies identified and characterized a set of neurons likely to mediate target detection and pursuit referred to as ‘small target motion detector’ (STMD) neurons. These neurons are selective for tiny targets, are velocity-tuned, contrast-sensitive and respond robustly to targets even against the motion of background. These neurons have shown several high-order properties which can contribute to the dragonfly’s ability to robustly pursue prey with over a 97% success rate. These include the recent electrophysiological observations of response ‘facilitation’ (a slow build-up of response to targets that move on long, continuous trajectories) and ‘selective attention’, a competitive mechanism that selects one target from alternatives. In this thesis, I adopted a bio-inspired approach to develop a solution for the problem of target tracking and pursuit. Directly inspired by recent physiological breakthroughs in understanding the insect brain, I developed a closed-loop target tracking system that uses an active saccadic gaze fixation strategy inspired by insect pursuit. First, I tested this model in virtual world simulations using MATLAB/Simulink. The results of these simulations show robust performance of this insect-inspired model, achieving high prey capture success even within complex background clutter, low contrast and high relative speed of pursued prey. Additionally, these results show that inclusion of facilitation not only substantially improves success for even short-duration pursuits, it also enhances the ability to ‘attend’ to one target in the presence of distracters. This inspect-inspired system has a relatively simple image processing strategy compared to state-of-the-art trackers developed recently for computer vision applications. Traditional machine vision approaches incorporate elaborations to handle challenges and non-idealities in the natural environments such as local flicker and illumination changes, and non-smooth and non-linear target trajectories. Therefore, the question arises as whether this insect inspired tracker can match their performance when given similar challenges? I investigated this question by testing both the efficacy and efficiency of this insect-inspired model in open-loop, using a widely-used set of videos recorded under natural conditions. I directly compared the performance of this model with several state-of-the-art engineering algorithms using the same hardware, software environment and stimuli. This insect-inspired model exhibits robust performance in tracking small moving targets even in very challenging natural scenarios, outperforming the best of the engineered approaches. Furthermore, it operates more efficiently compared to the other approaches, in some cases dramatically so. Computer vision literature traditionally test target tracking algorithms only in open-loop. However, one of the main purposes for developing these algorithms is implementation in real-time robotic applications. Therefore, it is still unclear how these algorithms might perform in closed-loop real-world applications where inclusion of sensors and actuators on a physical robot results in additional latency which can affect the stability of the feedback process. Additionally, studies show that animals interact with the target by changing eye or body movements, which then modulate the visual inputs underlying the detection and selection task (via closed-loop feedback). This active vision system may be a key to exploiting visual information by the simple insect brain for complex tasks such as target tracking. Therefore, I implemented this insect-inspired model along with insect active vision in a robotic platform. I tested this robotic implementation both in indoor and outdoor environments against different challenges which exist in real-world conditions such as vibration, illumination variation, and distracting stimuli. The experimental results show that the robotic implementation is capable of handling these challenges and robustly pursuing a target even in highly challenging scenarios.Thesis (Ph.D.) -- University of Adelaide, School of Mechanical Engineering, 201

Cartographie dense basée sur une représentation compacte RGB-D dédiée à la navigation autonome

Our aim is concentrated around building ego-centric topometric maps represented as a graph of keyframe nodes which can be efficiently used by autonomous agents. The keyframe nodes which combines a spherical image and a depth map (augmented visual sphere) synthesises information collected in a local area of space by an embedded acquisition system. The representation of the global environment consists of a collection of augmented visual spheres that provide the necessary coverage of an operational area. A "pose" graph that links these spheres together in six degrees of freedom, also defines the domain potentially exploitable for navigation tasks in real time. As part of this research, an approach to map-based representation has been proposed by considering the following issues : how to robustly apply visual odometry by making the most of both photometric and ; geometric information available from our augmented spherical database ; how to determine the quantity and optimal placement of these augmented spheres to cover an environment completely ; how tomodel sensor uncertainties and update the dense infomation of the augmented spheres ; how to compactly represent the information contained in the augmented sphere to ensure robustness, accuracy and stability along an explored trajectory by making use of saliency maps.Dans ce travail, nous proposons une représentation efficace de l’environnement adaptée à la problématique de la navigation autonome. Cette représentation topométrique est constituée d’un graphe de sphères de vision augmentées d’informations de profondeur. Localement la sphère de vision augmentée constitue une représentation égocentrée complète de l’environnement proche. Le graphe de sphères permet de couvrir un environnement de grande taille et d’en assurer la représentation. Les "poses" à 6 degrés de liberté calculées entre sphères sont facilement exploitables par des tâches de navigation en temps réel. Dans cette thèse, les problématiques suivantes ont été considérées : Comment intégrer des informations géométriques et photométriques dans une approche d’odométrie visuelle robuste ; comment déterminer le nombre et le placement des sphères augmentées pour représenter un environnement de façon complète ; comment modéliser les incertitudes pour fusionner les observations dans le but d’augmenter la précision de la représentation ; comment utiliser des cartes de saillances pour augmenter la précision et la stabilité du processus d’odométrie visuelle