Visual attention has long been described in terms of the spotlight metaphor, which assumes that two-dimensional regions of the visual field are selectively processed. However, evidence suggests that attention can be distributed to depth in addition to two-dimensional space (Andersen and Kramer, 1993; Gawryszewski, Riggio, Rizzolatti, and Umiltà, 1987). Research supporting this idea has induced depth through binocular disparity. Thus, the results of previous research may be specific to stereoscopic stimuli and not apply generally to the perception of depth. Three experiments were conducted in order to determine if visual attention could be distributed to a non-stereoscopic apparent depth. In these experiments, the perceptual experience of depth was induced in a visual scene using only pictorial depth cues. Subjects were required to attend either a near or far depth in this scene. Experiments 1 and 2 employed electrophysiological recordings and found a reliable modulation in the amplitude of the attention sensitive visual component, P1, when subjects directed attention to far depths. Behavioral measurements in Experiment 3 supported this result, finding speeded reaction time to attended far depth stimuli. No P1 modulation or reaction time facilitation was found when the pictorial depth cues of the visual scene were attenuated. These results suggest that visual attention may be distributed to pictorial depth and are further consistent with a viewer-centered asymmetry in attending to depth.

In this work we study a depth prediction problem where we provide a narrow field of view depth image and a wide field of view RGB image to a deep network tasked with predicting the depth for the entire RGB image. We show that by providing a narrow field of view depth image, we improve results for the area outside the provided depth compared to an earlier approach only utilizing a single RGB image for depth prediction. We also show that larger depth maps provide a greater advantage than smaller ones and that the accuracy of the model decreases with the distance from the provided depth. Further, we investigate several architectures as well as study the effect of adding noise and lowering the resolution of the provided depth image. Our results show that models provided low resolution noisy data performs on par with the models provided unaltered depth. / I det här arbetet studerar vi ett djupapproximationsproblem där vi tillhandahåller en djupbild med smal synvinkel och en RGB-bild med bred synvinkel till ett djupt nätverk med uppgift att förutsäga djupet för hela RGB-bilden. Vi visar att genom att ge djupbilden till nätverket förbättras resultatet för området utanför det tillhandahållna djupet jämfört med en existerande metod som använder en RGB-bild för att förutsäga djupet. Vi undersöker flera arkitekturer och storlekar på djupbildssynfält och studerar effekten av att lägga till brus och sänka upplösningen på djupbilden. Vi visar att större synfält för djupbilden ger en större fördel och även att modellens noggrannhet minskar med avståndet från det angivna djupet. Våra resultat visar också att modellerna som använde sig av det brusiga lågupplösta djupet presterade på samma nivå som de modeller som använde sig av det omodifierade djupet.

For a long time stereo-cameras have been deployed in visual Simultaneous Localization And Mapping (SLAM) systems to gain 3D information. Even though stereo-cameras show good performance, the main disadvantage is the complex and expensive hardware setup it requires, which limits the use of the system. A simpler and cheaper alternative are monocular cameras, however monocular images lack the important depth information. Recent works have shown that having access to depth maps in monocular SLAM system is beneficial since they can be used to improve the 3D reconstruction. This work proposes a deep neural network that predicts dense high-resolution depth maps from monocular RGB images by casting the problem as a supervised regression task. The network architecture follows an encoder-decoder structure in which multi-scale information is captured and skip-connections are used to recover details. The network is trained and evaluated on the KITTI dataset achieving results comparable to state-of-the-art methods. With further development, this network shows good potential to be incorporated in a monocular SLAM system to improve the 3D reconstruction.

L'objectif général de cette thèse est d'introduire de nouveaux concepts ou d'étendre certaines procédures statistiques déjà existantes touchant à la notion de profondeur statistique. <p><p>Celle-ci, originellement introduite afin de généraliser la notion de médiane et de fournir naturellement un ordre (depuis un centre, vers l'extérieur) dans un contexte multivarié, a, depuis son développement, démontré ses nombreuses qualités, tant en termes de robustesse, que d'utilité dans de nombreuses procédures inférentielles.<p>Les résultats proposés dans ce travail se développent le long de trois axes.<p><p>Pour commencer, la thèse s'intéresse à la classification supervisée. La profondeur a, en effet, déjà été utilisée avec succès dans ce contexte. Cependant, jusqu'ici, les outils développés restaient limités aux distributions elliptiques, constituant ainsi une sévère restriction des méthodes utilisant les fonctions de profondeur, qui, pour la plupart, sont par essence nonparamétrique. La première partie de cette thèse propose donc une nouvelle méthode de classification, fondée sur la profondeur, dont on montrera qu'elle est essentiellement universellement convergente. En particulier, la règle de discrimination proposée se fonde sur les idées utilisées dans la classification par plus proches voisins, en introduisant cependant des voisinages fondés sur la profondeur, mieux à même de cerner le comportement des populations sous-jacentes.<p><p>Ces voisinages d'un point quelconque, et surtout l'information sur le comportement local de la distribution en ce point qu'ils apportent, ont été réutilisés dans la seconde partie de ce travail. Plusieurs auteurs ont en effet reconnu certaines limitations aux fonctions de profondeur, de par leur caractère global et la difficulté d'étudier par leur biais des distributions multimodales ou à support convexe. Une nouvelle définition de profondeur locale est donc développée et étudiée. Son utilité dans différents problèmes d'inférence est également explorée.<p><p>Enfin, la thèse s'intéresse au paramètre de forme pour les distributions elliptiques. Ce paramètre d'importance est utilisé dans de nombreuses procédures statistiques (analyse en composantes principales, analyse en corrélations canoniques, entre autres) et aucune fonction de profondeur pour celui-ci n'existait à ce jour. La profondeur de forme est donc définie et ses propriétés sont étudiées. En particulier, on montrera que le cadre général de la profondeur paramétrique n'est pas suffisant en raison de la présence du paramètre de nuisance (d'influence non nulle) qu'est l'échelle. Une application inférentielle est présentée dans le cadre des tests d'hypothèses. / Doctorat en Sciences / info:eu-repo/semantics/nonPublished

Call number: LD2668 .T4 1978 V58 / Master of Science

This thesis reviews the role of emotions in one’s choice of information, information processing, and political attitudes. Theoretical and empirical endeavors to date have focused primarily on how emotions influence attitudes and information processing, leaving the actual processes guiding these outcomes in the margins. Specifically, it has been largely unexplored how emotions influence individuals’ information search behavior and then attitudes and information processing. Noting that the purposeful selection of likeminded information, often referred to as selective exposure, is commonly enacted when an individual first initiates information processing, and is also likely influenced by emotions, this study explores how emotions may affect people’s tendency to seek out congruent information. In addition, this study examines how the relationship between emotions and selective exposure in turn may affect aspects of information processing and attitudes. By designing an online experiment, I first tested how certain negative emotions (anger/fear) affected one’s pursuit of certain types of information (consistent/inconsistent) and second, I investigated how these emotions and information selections influenced subsequent information processing and attitudes. Results showed that while anger motivated more likeminded exposure for Republicans than fear, fear promoted more likeminded exposure for Democrats than anger. Further, anger prompted people to process messages more closely and to develop more polarized attitudes compared to fear. In addition, pro-attitudinal exposure produced more message-relevant thoughts for Republicans than counter-attitudinal message exposure, while it was counter-attitudinal exposure that yielded more message-relevant thoughts for Democrats. No such effect, however, was shown for attitudinal polarization. / text

Contemporary computational models of motion perception assume that in processing continuous or near-continuous motion information, the visual system measures spatial displacement in retinal coordinates over a series of time-varying images. Additional three-dimensional information possessed by the system purportedly does not influence this low-level motion analysis. The present research investigated the influence of static three-dimensional distance information recovered from binocular disparity on the perceived direction of motion. It was assumed that if a stereoscopic display context influenced perceived motion direction, the apparent velocity of a moving element would increase in order to traverse the greater apparent distance. This would be reflected in a predictable pattern of errors when the true angular velocity was the same, slower or faster than that of the standard. The stimuli consisted of random-dot stereograms depicting surfaces at varying distances and orientations. In one stereoscopic display, the disparity information indicated a surface sloping smoothly in depth from crossed to uncrossed disparity. The second display contained two fronto-parallel planes at discrete distances from the observed. Motion stimuli were single element translating horizontally and presented monocularly to the observer's right eye. Experiment 1 compared differential velocity judgments in the contexts of the sloped surface and a control condition at zero disparity. The results indicated an overall increase in the perceived velocity of the element moving in the context of the sloped surface. The pattern of results was replicated in experiment 2, but an additional effect of the relative positions of the two surfaces also was obtained. Experiment 3 explored the case of two discrete fronto-parallel planes, one at crossed disparity and the other at uncrossed disparity. This experiment also produced a position effect, but indicated that the perceived distance of the two planes did not differentially affect observer's velocity judgments. It was concluded that in some cases, the metric of motion analysis could be affected by three-dimensional information recovered from binocular disparity. The particular case discovered in these experiments was a surface that appeared to slope smoothly in depth. Discrete depth planes produced no such effect.

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Approved for public release; distribution is unlimited / NOAA-7 AVHRR data from April 1982 and 1983 were used to perform a global scale analysis of aerosol particle characteristics. Several improvements were incorporated into an AVHRR multichannel satellite data technique developed by Pfeil (1986). This included better cloud and sunglint discrimination, removal of Rayleigh radiance and accounting for ozone absorption. The characteristics analyzed were optical depth and Aerosol Particle Size Index (S₁₂). S₁₂ provides the slope of the aerosol particle size distribution curve. Both of these parameters were evaluated during several naturally occurring events, foremost of which were the 1982 El Chicon eruption and the 1982-1983 El Nino-Southern Oscillation event. The results provided evidence that a significant amount of aerosol particles over marine regions are from land-derived sources. However, the results also provided evidence that some marine aerosol particles may be of biogenic origins, / http://archive.org/details/globalscaleestim00fros / Lieutenant, United States Navy