Face superresolution from image sequence

Táto práce se zabývá použitím hlubokého učení neuronových sítí ke zvýšení rozlišení obrázků, které obsahují obličeje. Tato metoda najde uplatnění v různých oblastech, zejména v bezpečnosti, například, při bezpečnostním incidentu, kdy policie potřebuje identifikovat podezřelého z nahraného videa ze sledovací kamery. Cílem této práce je navrhnout minimálně dvě architektury neuronových sítí, které budou pracovat se sekvencí snímků, a porovnat je s metodami zpracování jediného snímku. Pro tento účel je také vytvořena nová trénovací množina, obsahující sekvenci snímku obličeje. Metody zpracování jednoho snímku jsou natrénované na nové množině. Dále jsou navrženy nové metody zvětšení obrázků na základě sekvence snímků. Tyto metody jsou založené na U-Net modelu, který je úspěšný v segmentaci, ale také v superrozlišení. Pro zlepšení architektury byly použity reziduální bloky a jejich modifikace, a navíc také percepční ztrátová funkce, která dovoluje vyhnout se rozmazání a získání více detailů. První čast této práce je věnovana popisu neuronových sítí a některých architektur, jejichž modifikace mohou být použity v superrozlišení. Druhá část se poté zabývá popisem metod pro zvýšení rozlišení obrazu pomocí jednoho snímku, několika snímků a videa. Ve třetí části jsou popsány navržené metody a experimenty a v poslední části porovnaná metod založených na jednom snímku a několika snímcích. Navržené metody jsou schopny získat více detailů v obraze, ale mohou produkovat artefakty. Ty lze ale poté eliminovat pomocí filtru, například Gaussova. Nové metody méně selhávají při detekci obličejů, a to je podstatné u identifikace člověka v případě incidentu.This work is focused on application of deep learning in increasing resolution of images containing face. This can be applied in different fields, including security. For example, in case of incident, the police needs to identify a culprit from the records of security camera. The aim of this work is to propose neural network models, which would work with sequence of frames, and to compare these models with existing methods for a single image super-resolution. For this purpose, a new dataset with sequences of the images with faces is created. The methods for the single super-resolution are trained on the new dataset. The new architectures for multiframe super-resolution are proposed. They are based on U-Net model. This model is successful for segmentation tasks, but it can be also applied for super-resolution tasks. To improve this architecture, the residual blocks and its modification are used. To avoid blurring effect and recover more details, the perceptual loss function is applied. In the first part of this work, the description of neural networks and overview of the architectures, which can be applied in super-resolution, is provided. The second part contains the methods for super-resolution of a single frame, multiframe, video. In the next section, there is a description of proposed architectures and description of the experiment. In the last part of the work, multiframe methods and single frame methods are compared. In the result, the proposed methods recover more details, however, some architectures produce artefacts, which can be reduced using a filter, for example, Gaussian. New methods allow to reduce the number of failed face recognition. This fact is necessary for person identification in case of incidents.

Machine learning methods for 3D object classification and segmentation

Field of study: Computer science.Dr. Ye Duan, Thesis Supervisor.Includes vita."July 2018."Object understanding is a fundamental problem in computer vision and it has been extensively researched in recent years thanks to the availability of powerful GPUs and labelled data, especially in the context of images. However, 3D object understanding is still not on par with its 2D domain and deep learning for 3D has not been fully explored yet. In this dissertation, I work on two approaches, both of which advances the state-of-the-art results in 3D classification and segmentation. The first approach, called MVRNN, is based multi-view paradigm. In contrast to MVCNN which does not generate consistent result across different views, by treating the multi-view images as a temporal sequence, our MVRNN correlates the features and generates coherent segmentation across different views. MVRNN demonstrated state-of-the-art performance on the Princeton Segmentation Benchmark dataset. The second approach, called PointGrid, is a hybrid method which combines points and regular grid structure. 3D points can retain fine details but irregular, which is challenge for deep learning methods. Volumetric grid is simple and has regular structure, but does not scale well with data resolution. Our PointGrid, which is simple, allows the fine details to be consumed by normal convolutions under a coarser resolution grid. PointGrid achieved state-of-the-art performance on ModelNet40 and ShapeNet datasets in 3D classification and object part segmentation.Includes bibliographical references (pages 116-140)

Surveyor lunar roving vehicle, phase I. Volume III - Preliminary design and system description. Book 2 - Validation of preliminary design, sections 7-13 Final technical report

Systems design validation of Surveyor lunar roving vehicle - navigation, control and display, television, telecommunications, power supply, and thermal contro

Aerial Vehicles

This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space

The Art of Movies

Movie is considered to be an important art form; films entertain, educate, enlighten and inspire audiences. Film is a term that encompasses motion pictures as individual projects, as well as — in metonymy — the field in general. The origin of the name comes from the fact that photographic film (also called filmstock) has historically been the primary medium for recording and displaying motion pictures. Many other terms exist — motion pictures (or just pictures or “picture”), the silver screen, photoplays, the cinema, picture shows, flicks — and commonly movies

Hybridizing 3-dimensional multiple object tracking with neurofeedback to enhance preparation, performance, and learning

Le vaste domaine de l’amélioration cognitive traverse les applications comportementales, biochimiques et physiques. Aussi nombreuses sont les techniques que les limites de ces premières : des études de pauvre méthodologie, des pratiques éthiquement ambiguës, de faibles effets positifs, des effets secondaires significatifs, des couts financiers importants, un investissement de temps significatif, une accessibilité inégale, et encore un manque de transfert. L’objectif de cette thèse est de proposer une méthode novatrice d’intégration de l’une de ces techniques, le neurofeedback, directement dans un paradigme d’apprentissage afin d’améliorer la performance cognitive et l’apprentissage. Cette thèse propose les modalités, les fondements empiriques et des données à l’appui de ce paradigme efficace d’apprentissage ‘bouclé’. En manipulant la difficulté dans une tâche en fonction de l’activité cérébrale en temps réel, il est démontré que dans un paradigme d’apprentissage traditionnel (3-dimentional multiple object tracking), la vitesse et le degré d’apprentissage peuvent être améliorés de manière significative lorsque comparés au paradigme traditionnel ou encore à un groupe de contrôle actif. La performance améliorée demeure observée même avec un retrait du signal de rétroaction, ce qui suggère que les effets de l’entrainement amélioré sont consolidés et ne dépendent pas d’une rétroaction continue. Ensuite, cette thèse révèle comment de tels effets se produisent, en examinant les corrélés neuronaux des états de préparation et de performance à travers les conditions d’état de base et pendant la tâche, de plus qu’en fonction du résultat (réussite/échec) et de la difficulté (basse/moyenne/haute vitesse). La préparation, la performance et la charge cognitive sont mesurées via des liens robustement établis dans un contexte d’activité cérébrale fonctionnelle mesurée par l’électroencéphalographie quantitative. Il est démontré que l’ajout d’une assistance- à-la-tâche apportée par la fréquence alpha dominante est non seulement appropriée aux conditions de ce paradigme, mais influence la charge cognitive afin de favoriser un maintien du sujet dans sa zone de développement proximale, ce qui facilite l’apprentissage et améliore la performance. Ce type de paradigme d’apprentissage peut contribuer à surmonter, au minimum, un des limites fondamentales du neurofeedback et des autres techniques d’amélioration cognitive : le manque de transfert, en utilisant une méthode pouvant être intégrée directement dans le contexte dans lequel l’amélioration de la performance est souhaitée.The domain of cognitive enhancement is vast, spanning behavioral, biochemical and physical applications. The techniques are as numerous as are the limitations: poorly conducted studies, ethically ambiguous practices, limited positive effects, significant side-effects, high financial costs, significant time investment, unequal accessibility, and lack of transfer. The purpose of this thesis is to propose a novel way of integrating one of these techniques, neurofeedback, directly into a learning context in order to enhance cognitive performance and learning. This thesis provides the framework, empirical foundations, and supporting evidence for a highly efficient ‘closed-loop’ learning paradigm. By manipulating task difficulty based on a measure of cognitive load within a classic learning scenario (3-dimentional multiple object tracking) using real-time brain activity, results demonstrate that over 10 sessions, speed and degree of learning can be substantially improved compared with a classic learning system or an active sham-control group. Superior performance persists even once the feedback signal is removed, which suggests that the effects of enhanced training are consolidated and do not rely on continued feedback. Next, this thesis examines how these effects occur, exploring the neural correlates of the states of preparedness and performance across baseline and task conditions, further examining correlates related to trial results (correct/incorrect) and task difficulty (slow/medium/fast speeds). Cognitive preparedness, performance and load are measured using well-established relationships between real-time quantified brain activity as measured by quantitative electroencephalography. It is shown that the addition of neurofeedback-based task assistance based on peak alpha frequency is appropriate to task conditions and manages to influence cognitive load, keeping the subject in the zone of proximal development more often, facilitating learning and improving performance. This type of learning paradigm could contribute to overcoming at least one of the fundamental limitations of neurofeedback and other cognitive enhancement techniques : a lack of observable transfer effects, by utilizing a method that can be directly integrated into the context in which improved performance is sought

Cybertheatres: Emergent Networked Performance Practices

This thesis explores the emergent genre of cybertheatres or networked performance, that is, performance that employs the Internet and/or other types of networking technologies (telecommunication, mobile) and attitudes. I argue that networking technologies produce hybrid spacetimes or heterotopias (Foucault), which function as stages for networked performances, a novel and increasingly popular field of practice and research. The aims of this project are to a) articulate networked performance as a distinct genre, which is a hybrid between theatre/performance and networking technologies, b) situate this within a lineage of performance practice, c) identify and analyse its principal ontological and dramaturgical elements and, d) explore appropriate curatorial strategies for its presentation to a spectrum of audiences. To achieve these aims I undertake a critical analysis of cybertheatres, starting from 1967 and focusing on current practices. My analysis unfolds through engagement with discussions along two pivotal conceptual vectors, and through applied exploration of two core elements of practice: The conceptual vectors along which this thesis develops are: 1. Space: I examine the spatial nature of the networks that host cybertheatres, employing British group Blast Theory as my case-study. 2. Presence: I question the validity of the presence vs. absence dichotomy within networked environments. I investigate this through the work of Belgian duo Entropy8Zuper!, relaunched as Tale of Tales. Further on, I undertake a practical exploration relating to the subject of the curation of cybertheatres. I reflect upon and evaluate the three-day event Intimacy: Across Digital and Visceral Pelformance (December 2007), which I initiated, produced, co-directed and cocurated, to propose curatorial strategies that are appropriate to emergent practices in general and cybertheatres in particular. I close by a shift of voice from the author to the collective: I join the collaborative project Deptford. TV as a method of studying artistic, curatorial and social platforms that demonstrate Web 2.0 attitudes, and argue for the genre's particular potential for new forms of social engagement within a computer-mediated culture

NASA patent abstracts bibliography: A continuing bibliography. Section 2: Indexes (supplement 04)

For abstract, see N74-20609

NASA patent abstracts bibliography: A continuing bibliography. Section 2: Indexes (supplement 05)

For abstract, see N75-13677

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 375)

This bibliography lists 212 reports, articles, and other documents recently introduced into the NASA Scientific and Technical Information System database. Subject coverage includes the following: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance