マルチタスク学習を用いたシーン理解とデータ拡張による複合現実感の向上

早大学位記番号:新9140早稲田大

The 1990 Johnson Space Center bibliography of scientific and technical papers

Abstracts are presented of scientific and technical papers written and/or presented by L. B. Johnson Space Center (JSC) authors, including civil servants, contractors, and grantees, during the calendar year of 1990. Citations include conference and symposium presentations, papers published in proceedings or other collective works, seminars, and workshop results, NASA formal report series (including contractually required final reports), and articles published in professional journals

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Descending premotor target tracking systems in flying insects

The control of behaviour in all animals requires efficient transformation of sensory signals into the task-specific activation of muscles. Predation offers an advantageous model behaviour to study the computational organisation underlying sensorimotor control. Predators are optimised through diverse evolutionary arms races to outperform their prey in terms of sensorimotor coordination, leading to highly specialised anatomical adaptations and hunting behaviours, which are often innate and highly stereotyped. Predatory flying insects present an extreme example, performing complex visually-guided pursuits of small, often fast flying prey over extremely small timescales. Furthermore, this behaviour is controlled by a tiny nervous system, leading to pressure on neuronal organisation to be optimised for coding efficiency. In dragonflies, a population of eight pairs of bilaterally symmetric Target Selective Descending Neurons (TSDNs) relay visual information about small moving objects from the brain to the thoracic motor centres. These neurons encode the movement of small moving objects across the dorsal fovea region of the eye which is fixated on prey during predatory pursuit, and are thought to constitute the commands necessary for actuating an interception flight path. TSDNs are characterised by their receptive fields, with responses of each TSDN type spatially confined to a specific portion of the dorsal fovea visual field and tuned to a specific direction of object motion. To date, little is known about the descending representations mediating target tracking in other insects. This dissertation presents a comparative report of descending neurons in a variety of flying insects. The results are organised into three chapters: Chapter 3 identifies TSDNs in demoiselle damselflies and compares their response properties to those previously described in dragonflies. Demoiselle TSDNs are also found to integrate binocular information, which is further elaborated with prism and eyepatch experiments. Chapter 4 describes TSDNs in two dipteran species, the robberfly Holcocephala fusca and the killerfly Coenosia attenuata. Chapter 5 describes an interaction between small- and wide-field visual features in TSDNs of both predatory and nonpredatory dipterans, finding functional similarity of these neurons for prey capture and conspecific pursuit. Dipteran TSDN responses are repressed by background motion in a direction dependent manner, suggesting a control architecture in which target tracking and optomotor stabilization pathways operate in parallel during pursuit.echnology and Biological Sciences ResearchCouncil (BB/M011194/1

Physics-Based Probabilistic Motion Compensation of Elastically Deformable Objects

A predictive tracking approach and a novel method for visual motion compensation are introduced, which accurately reconstruct and compensate the deformation of the elastic object, even in the case of complete measurement information loss. The core of the methods involves a probabilistic physical model of the object, from which all other mathematical models are systematically derived. Due to flexible adaptation of the models, the balance between their complexity and their accuracy is achieved

A review of visualisation-as-explanation techniques for convolutional neural networks and their evaluation

Visualisation techniques are powerful tools to understand the behaviour of Artificial Intelligence (AI) systems. They can be used to identify important features contributing to the network decisions, investigate biases in datasets, and find weaknesses in the system's structure (e.g., network architectures). Lawmakers and regulators may not allow the use of smart systems if these systems cannot explain the logic underlying a decision or action taken. These systems are required to offer a high level of 'transparency' to be approved for deployment. Model transparency is vital for safety-critical applications such as autonomous navigation and operation systems (e.g., autonomous trains or cars), where prediction errors may have serious implications. Thus, being highly accurate without explaining the basis of their performance is not enough to satisfy regulatory requirements. The lack of system interpretability is a major obstacle to the wider adoption of AI in safety-critical applications. Explainable Artificial Intelligence (XAI) techniques applied to intelligent systems to justify their decisions offers a possible solution. In this review, we present state-of-the-art explanation techniques in detail. We focus our presentation and critical discussion on visualisation methods for the most adopted architecture in use, the Convolutional Neural Networks (CNNs), applied to the domain of image classification. Further, we discuss the evaluation techniques for different explanation methods, which shows that some of the most visually appealing methods are unreliable and can be considered a simple feature or edge detector. In contrast, robust methods can give insights into the model behaviour, which helps to enhance the model performance and boost the confidence in the model's predictions. Besides, the applications of XAI techniques show their importance in many fields such as medicine and industry. We hope that this review proves a valuable contribution for researchers in the field of XAI

Interdisciplinarity in the Age of the Triple Helix: a Film Practitioner's Perspective

This integrative chapter contextualises my research including articles I have published as well as one of the creative artefacts developed from it, the feature film The Knife That Killed Me. I review my work considering the ways in which technology, industry methods and academic practice have evolved as well as how attitudes to interdisciplinarity have changed, linking these to Etzkowitz and Leydesdorff’s ‘Triple Helix’ model (1995). I explore my own experiences and observations of opportunities and challenges that have been posed by the intersection of different stakeholder needs and expectations, both from industry and academic perspectives, and argue that my work provides novel examples of the applicability of the ‘Triple Helix’ to the creative industries. The chapter concludes with a reflection on the evolution and direction of my work, the relevance of the ‘Triple Helix’ to creative practice, and ways in which this relationship could be investigated further

A Posture Sequence Learning System for an Anthropomorphic Robotic Hand

The paper presents a cognitive architecture for posture learning of an anthropomorphic robotic hand. Our approach is aimed to allow the robotic system to perform complex perceptual operations, to interact with a human user and to integrate the perceptions by a cognitive representation of the scene and the observed actions. The anthropomorphic robotic hand imitates the gestures acquired by the vision system in order to learn meaningful movements, to build its knowledge by different conceptual spaces and to perform complex interaction with the human operator