209 research outputs found
Multi-sensor data fusion techniques for RPAS detect, track and avoid
Accurate and robust tracking of objects is of growing interest amongst the computer vision scientific community. The ability of a multi-sensor system to detect and track objects, and accurately predict their future trajectory is critical in the context of mission- and safety-critical applications. Remotely Piloted Aircraft System (RPAS) are currently not equipped to routinely access all classes of airspace since certified Detect-and-Avoid (DAA) systems are yet to be developed. Such capabilities can be achieved by incorporating both cooperative and non-cooperative DAA functions, as well as providing enhanced communications, navigation and surveillance (CNS) services. DAA is highly dependent on the performance of CNS systems for Detection, Tacking and avoiding (DTA) tasks and maneuvers. In order to perform an effective detection of objects, a number of high performance, reliable and accurate avionics sensors and systems are adopted including non-cooperative sensors (visual and thermal cameras, Laser radar (LIDAR) and acoustic sensors) and cooperative systems (Automatic Dependent Surveillance-Broadcast (ADS-B) and Traffic Collision Avoidance System (TCAS)). In this paper the sensors and system information candidates are fully exploited in a Multi-Sensor Data Fusion (MSDF) architecture. An Unscented Kalman Filter (UKF) and a more advanced Particle Filter (PF) are adopted to estimate the state vector of the objects based for maneuvering and non-maneuvering DTA tasks. Furthermore, an artificial neural network is conceptualised/adopted to exploit the use of statistical learning methods, which acts to combined information obtained from the UKF and PF. After describing the MSDF architecture, the key mathematical models for data fusion are presented. Conceptual studies are carried out on visual and thermal image fusion architectures
Vision Sensors and Edge Detection
Vision Sensors and Edge Detection book reflects a selection of recent developments within the area of vision sensors and edge detection. There are two sections in this book. The first section presents vision sensors with applications to panoramic vision sensors, wireless vision sensors, and automated vision sensor inspection, and the second one shows image processing techniques, such as, image measurements, image transformations, filtering, and parallel computing
Intelligent video object tracking in large public environments
This Dissertation addresses the problem of video object tracking in large public environments, and was developed within the context of a partnership between ISCTE-IUL and THALES1 object.
This partnership aimed at developing a new approach to video tracking, based on a simple tracking algorithm aided by object position estimations to deal with the harder cases of video object tracking. This proposed approach has been applied
successfully in the TRAPLE2 project developed at THALES where the main focus is the real-time monitoring of public spaces and the tracking of moving objects (i.e., persons).
The proposed low-processing tracking solution woks as follows: after the detection step, the various objects in the visual scene are tracked through their centres of mass (centroids) that, typically, exhibit little variations along close apart video frames. After this step, some heuristics are applied to the results to maintain coherent the identification of the
video objects and estimate their positions in cases of uncertainties, e.g., occlusions, which is one of the major novelties proposed in this Dissertation.
The proposed approach was tested with relevant test video sequences representing real video monitoring scenes and the obtained results showed that this approach is able to track multiple persons in real-time with reasonable computational power.Esta dissertação aborda o problema do seguimento de objectos vídeo em ambientes públicos de grande dimensão e foi desenvolvida no contexto de uma parceria entre o ISCTE-IUL e a THALES. Esta parceria visou o desenvolvimento de uma nova abordagem
ao seguimento de objectos de vídeo baseada num processamento de vídeo simples em conjunto com a estimação da posição dos objectos nos casos mais difíceis de efectuar o seguimento. Esta abordagem foi aplicada com sucesso no âmbito do projecto TRAPLE
desenvolvido pela THALES onde um dos principais enfoques é o seguimento de múltiplos objectos de vídeo em tempo real em espaços públicos, tendo como objectivo o seguimento de pessoas que se movam ao longo desse espaço.
A solução de baixo nível de processamento proposta funciona do seguinte modo: após o passo de detecção de objectos, os diversos objectos detectados na cena são seguidos através dos seus centros de massa que, normalmente, apresentam poucas variações ao longo
de imagens consecutivas de vídeo. Após este passo, algumas heurísticas são aplicadas aos resultados mantendo a identificação dos objectos de vídeo coerente e estimando as suas posições em casos de incertezas (e.g., oclusões) que é uma das principais novidades
propostas nesta dissertação.
A abordagem proposta foi testada com várias sequências de vídeo de teste representando cenas reais de videovigilância e os resultados obtidos mostraram que esta abordagem é capaz de seguir várias pessoas em tempo real com um nível de processamento moderado
Face recognition by means of advanced contributions in machine learning
Face recognition (FR) has been extensively studied, due to both scientific fundamental challenges and current and potential applications where human identification is needed. FR systems have the benefits of their non intrusiveness, low cost of equipments and no useragreement requirements when doing acquisition, among the most important ones.
Nevertheless, despite the progress made in last years and the different solutions proposed, FR performance is not yet satisfactory when more demanding conditions are required (different viewpoints, blocked effects, illumination changes, strong lighting states, etc). Particularly, the effect of such non-controlled lighting conditions on face images leads to one of the strongest distortions in facial appearance.
This dissertation addresses the problem of FR when dealing with less constrained illumination situations. In order to approach the problem, a new multi-session and multi-spectral face database has been acquired in visible, Near-infrared (NIR) and Thermal infrared (TIR) spectra,
under different lighting conditions.
A theoretical analysis using information theory to demonstrate the complementarities between different spectral bands have been firstly carried out. The optimal exploitation of the information provided by the set of multispectral images has been subsequently addressed by
using multimodal matching score fusion techniques that efficiently synthesize complementary meaningful information among different spectra.
Due to peculiarities in thermal images, a specific face segmentation algorithm has been required and developed. In the final proposed system, the Discrete Cosine Transform as dimensionality reduction tool and a fractional distance for matching were used, so that the cost in processing time and memory was significantly reduced. Prior to this classification task, a selection of the relevant frequency bands is proposed in order to optimize the overall system, based on identifying and maximizing independence relations by means of discriminability
criteria. The system has been extensively evaluated on the multispectral face database specifically performed for our purpose. On this regard, a new visualization procedure has been suggested in order to combine different bands for establishing valid comparisons and giving statistical information about the significance of the results. This experimental framework has more easily enabled the improvement of robustness against training and testing illumination mismatch. Additionally, focusing problem in thermal spectrum has been also addressed, firstly, for the more general case of the thermal images (or thermograms), and then for the case of facialthermograms from both theoretical and practical point of view. In order to analyze the quality of such facial thermograms degraded by blurring, an appropriate algorithm has been successfully developed.
Experimental results strongly support the proposed multispectral facial image fusion, achieving very high performance in several conditions. These results represent a new advance in providing a robust matching across changes in illumination, further inspiring highly accurate
FR approaches in practical scenarios.El reconeixement facial (FR) ha estat àmpliament estudiat, degut tant als reptes fonamentals científics que suposa com a les aplicacions actuals i futures on requereix la identificació de les persones. Els sistemes de reconeixement facial tenen els avantatges de ser no intrusius,presentar un baix cost dels equips d’adquisició i no la no necessitat d’autorització per part de l’individu a l’hora de realitzar l'adquisició, entre les més importants. De totes maneres i malgrat els avenços aconseguits en els darrers anys i les diferents solucions proposades, el rendiment del FR encara no resulta satisfactori quan es requereixen condicions més exigents (diferents punts de vista,
efectes de bloqueig, canvis en la il·luminació, condicions de llum extremes, etc.). Concretament, l'efecte d'aquestes variacions no controlades en les condicions d'il·luminació sobre les imatges facials condueix a una de les distorsions més accentuades sobre l'aparença
facial.
Aquesta tesi aborda el problema del FR en condicions d'il·luminació menys restringides. Per tal d'abordar el problema, hem adquirit una nova base de dades de cara multisessió i multiespectral en l'espectre infraroig visible, infraroig proper (NIR) i tèrmic (TIR), sota diferents condicions d'il·luminació. En primer lloc s'ha dut a terme una anàlisi teòrica utilitzant la teoria de la informació per demostrar la complementarietat entre les diferents bandes espectrals objecte d’estudi. L'òptim aprofitament de la informació proporcionada pel conjunt d'imatges multiespectrals s'ha abordat posteriorment mitjançant l'ús de tècniques de fusió de puntuació multimodals, capaces de sintetitzar de manera eficient el conjunt d’informació significativa complementària entre els diferents espectres. A causa de les característiques particulars de les imatges tèrmiques, s’ha requerit del desenvolupament d’un algorisme específic per la segmentació de les mateixes. En el sistema proposat final, s’ha utilitzat com a eina de reducció de la dimensionalitat de les imatges, la Transformada del Cosinus Discreta i una distància fraccional per realitzar les tasques de classificació de manera que el cost en temps de processament i de memòria es va reduir de
forma significa. Prèviament a aquesta tasca de classificació, es proposa una selecció de les bandes de freqüències més rellevants, basat en la identificació i la maximització de les relacions d'independència per mitjà de criteris discriminabilitat, per tal d'optimitzar el conjunt del
sistema. El sistema ha estat àmpliament avaluat sobre la base de dades de cara multiespectral, desenvolupada pel nostre propòsit. En aquest sentit s'ha suggerit l’ús d’un nou procediment de visualització per combinar diferents bandes per poder establir comparacions vàlides i donar informació estadística sobre el significat dels resultats. Aquest marc experimental ha permès més fàcilment la millora de la robustesa quan les condicions d’il·luminació eren diferents entre els processos d’entrament i test.
De forma complementària, s’ha tractat la problemàtica de l’enfocament de les imatges en l'espectre tèrmic, en primer lloc, pel cas general de les imatges tèrmiques (o termogrames) i posteriorment pel cas concret dels termogrames facials, des dels punt de vista tant teòric com
pràctic. En aquest sentit i per tal d'analitzar la qualitat d’aquests termogrames facials degradats per efectes de desenfocament, s'ha desenvolupat un últim algorisme. Els resultats experimentals recolzen fermament que la fusió d'imatges facials multiespectrals proposada assoleix un rendiment molt alt en diverses condicions d’il·luminació. Aquests resultats representen un nou avenç en l’aportació de solucions robustes quan es contemplen canvis en la il·luminació, i esperen poder inspirar a futures implementacions de sistemes de reconeixement facial precisos en escenaris no controlats.Postprint (published version
Predictive geohazard mapping using LiDAR and satellite imagery in Missouri and Oklahoma, USA
”Light Detection and Ranging (LiDAR) and satellite imagery have become the most utilized remote sensing technologies for compiling inventories of surficial geologic conditions. Point cloud data obtained from multi-spectral remote sensing methods provide a detailed characterization of the surface features, in particular, the detailed surface manifestations of underlying geologic structures. When combined, point clouds eliminate bias from visual inconsistencies and/or statistical values. This research explores the competence of point clouds derived from LiDAR and Unmanned Aerial Systems (UAS) as a predictive tool in evaluating various geohazards. It combines these data sets with other remote sensing techniques to evaluate the sensitivity of the respective datasets to temporal changes in the earth’s surface (potentially detectable at a centimeter-scale). A two-phase research approach was employed to test several hazard mapping scenarios in three geographic areas in the U.S. Midcontinent as follows: 1) UAS-derived surficial deformations near the epicenter of the 2016 Mw 5.8 Pawnee, Oklahoma earthquake (Paper I); 2) UAS mapping of recent earthquake epicenters in Noble Payne and Pawnee counties of Oklahoma State (Paper II); and, 3) Evaluation of geohazards in Greater Cape Girardeau Southeast Missouri (Paper III). These analyses detected geomorphic changes in the study locations, such as ground subsidence, soil heave and expansion, liquefaction-induced structures, dynamically-induced consolidation, and surface fault rupture. The studies underscore the importance of early hazard identification and providing information to relevant data users to make informed decisions”--Abstract, page iv
Motor learning induced neuroplasticity in minimally invasive surgery
Technical skills in surgery have become more complex and challenging to acquire since the introduction of technological aids, particularly in the arena of Minimally Invasive Surgery. Additional challenges posed by reforms to surgical careers and increased public scrutiny, have propelled identification of methods to assess and acquire MIS technical skills. Although validated objective assessments have been developed to assess motor skills requisite for MIS, they poorly understand the development of expertise. Motor skills learning, is indirectly observable, an internal process leading to relative permanent changes in the central nervous system. Advances in functional neuroimaging permit direct interrogation of evolving patterns of brain function associated with motor learning due to the property of neuroplasticity and has been used on surgeons to identify the neural correlates for technical skills acquisition and the impact of new technology. However significant gaps exist in understanding neuroplasticity underlying learning complex bimanual MIS skills. In this thesis the available evidence on applying functional neuroimaging towards assessment and enhancing operative performance in the field of surgery has been synthesized.
The purpose of this thesis was to evaluate frontal lobe neuroplasticity associated with learning a complex bimanual MIS skill using functional near-infrared spectroscopy an indirect neuroimaging technique. Laparoscopic suturing and knot-tying a technically challenging bimanual skill is selected to demonstrate learning related reorganisation of cortical behaviour within the frontal lobe by shifts in activation from the prefrontal cortex (PFC) subserving attention to primary and secondary motor centres (premotor cortex, supplementary motor area and primary motor cortex) in which motor sequences are encoded and executed. In the cross-sectional study, participants of varying expertise demonstrate frontal lobe neuroplasticity commensurate with motor learning. The longitudinal study involves tracking evolution in cortical behaviour of novices in response to receipt of eight hours distributed training over a fortnight. Despite novices achieving expert like performance and stabilisation on the technical task, this study demonstrates that novices displayed persistent PFC activity. This study establishes for complex bimanual tasks, that improvements in technical performance do not accompany a reduced reliance in attention to support performance. Finally, least-squares support vector machine is used to classify expertise based on frontal lobe functional connectivity. Findings of this thesis demonstrate the value of interrogating cortical behaviour towards assessing MIS skills development and credentialing.Open Acces
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