The complex visual search involved in baggage screening requires operators to determine quickly whether a bag contains threatening objects that are embedded in a high degree of visual clutter. Methods for calculating visual clutter have been developed, and research has demonstrated the negative impact of clutter on search performance. The current study examined whether leveraging visual clutter information on the display during search could improve baggage screening performance above and beyond the conventional screening process. Ninety undergraduates searched x-ray images of bags for weapon items in a low fidelity baggage screening simulation; two clutter-based preview conditions displayed a limited portion of the bag to the participant before the entire bag was displayed. Eye movement data confirmed that the preview process guided the participant's attention to the corresponding previewed region. However, analysis of the baggage screening performance data showed there were no significant benefits associated with either clutter-based preview conditions compared with a control condition in which the entire bag was displayed for the duration of the trial. Thus, the results suggest that using clutter-based preview to guide visual attention does not substantially improve weapon detection performance. Despite this null effect, the current study provides additional evidence regarding the impact of visual clutter on complex search performance by demonstrating significant reductions in weapon detection accuracy and search efficiency due to increasing levels of visual clutter. Further research should explore methods for improving complex visual search by considering the negative impacts of visual clutter and ensuring that both attention guidance and object recognition processes are facilitated during search.

Electronically displayed images are becoming increasingly important as an interface between man and information systems. Lengthy periods of intense observation are no longer unusual. There is a growing awareness that specific demands should be made on displayed images in order to achieve an optimum match with the perceptual properties of the human visual system. These demands may vary greatly, depending on the task for which the displayed image is to be used and the ambient conditions. Optimal image specifications are clearly not the same for a home TV, a radar signal monitor or an infrared targeting image display. There is, therefore, a growing need for means of objective measurement of image quality, where "image quality" is used in a very broad sense and is defined in the thesis, but includes any impact of image properties on human performance in relation to specified visual tasks. The aim of this thesis is to consolidate and comment on the image measure literatures, and to find through experiment the salient properties of electronically displayed real world complex imagery that impacts on human performance. These experiments were carried out for well specified visual tasks (of real relevance), and the appropriate application of image measures to this imagery, to predict human performance, was considered. An introduction to certain aspects of image quality measures is given, and clutter metrics are integrated into this concept. A very brief and basic introduction to the human visual system (HVS) is given, with some basic models. The literature on image measures is analysed, with a resulting classification of image measures, according to which features they were attempting to quantify. A series of experiments were performed to evaluate the effects of image properties on human performance, using appropriate measures of performance. The concept of image similarity was explored, by objectively measuring the subjective perception of imagery of the same scene, as obtained through different sensors, and which underwent different luminance transformations. Controlled degradations were introduced, by using image compression. Both still and video compression were used to investigate both spatial and temporal aspects of HVS processing. The effects of various compression schemes on human target acquisition performance were quantified. A study was carried out to determine the "local" extent, to which the clutter around a target, affects its detectability. It was found in this case, that the excepted wisdom, of setting the local domain (support of the metric) to twice the expected target size, was incorrect. The local extent of clutter was found to be much greater, with this having implications for the application of clutter metrics. An image quality metric called the gradient energy measure (GEM), for quantifying the affect of filtering on Nuclear Medicine derived images, was developed and evaluated. This proved to be a reliable measure of image smoothing and noise level, which in preliminary studies agreed with human perception. The final study discussed in this thesis determined the performance of human image analysts, in terms of their receiver-operating characteristic, when using Synthetic Aperture Radar (SAR) derived images in the surveillance context. In particular, the effects of target contrast and background clutter on human analyst target detection performance were quantified. In the final chapter, suggestions to extend the work of this thesis are made, and in this context a system to predict human visual performance, based on input imagery, is proposed. This system intelligently uses image metrics based on the particular visual task and human expectations and human visual system performance parameters. / Thesis (Ph.D.)--Medical School; School of Computer Science, 1999.

Electronically displayed images are becoming increasingly important as an interface between man and information systems. Lengthy periods of intense observation are no longer unusual. There is a growing awareness that specific demands should be made on displayed images in order to achieve an optimum match with the perceptual properties of the human visual system. These demands may vary greatly, depending on the task for which the displayed image is to be used and the ambient conditions. Optimal image specifications are clearly not the same for a home TV, a radar signal monitor or an infrared targeting image display. There is, therefore, a growing need for means of objective measurement of image quality, where "image quality" is used in a very broad sense and is defined in the thesis, but includes any impact of image properties on human performance in relation to specified visual tasks. The aim of this thesis is to consolidate and comment on the image measure literatures, and to find through experiment the salient properties of electronically displayed real world complex imagery that impacts on human performance. These experiments were carried out for well specified visual tasks (of real relevance), and the appropriate application of image measures to this imagery, to predict human performance, was considered. An introduction to certain aspects of image quality measures is given, and clutter metrics are integrated into this concept. A very brief and basic introduction to the human visual system (HVS) is given, with some basic models. The literature on image measures is analysed, with a resulting classification of image measures, according to which features they were attempting to quantify. A series of experiments were performed to evaluate the effects of image properties on human performance, using appropriate measures of performance. The concept of image similarity was explored, by objectively measuring the subjective perception of imagery of the same scene, as obtained through different sensors, and which underwent different luminance transformations. Controlled degradations were introduced, by using image compression. Both still and video compression were used to investigate both spatial and temporal aspects of HVS processing. The effects of various compression schemes on human target acquisition performance were quantified. A study was carried out to determine the "local" extent, to which the clutter around a target, affects its detectability. It was found in this case, that the excepted wisdom, of setting the local domain (support of the metric) to twice the expected target size, was incorrect. The local extent of clutter was found to be much greater, with this having implications for the application of clutter metrics. An image quality metric called the gradient energy measure (GEM), for quantifying the affect of filtering on Nuclear Medicine derived images, was developed and evaluated. This proved to be a reliable measure of image smoothing and noise level, which in preliminary studies agreed with human perception. The final study discussed in this thesis determined the performance of human image analysts, in terms of their receiver-operating characteristic, when using Synthetic Aperture Radar (SAR) derived images in the surveillance context. In particular, the effects of target contrast and background clutter on human analyst target detection performance were quantified. In the final chapter, suggestions to extend the work of this thesis are made, and in this context a system to predict human visual performance, based on input imagery, is proposed. This system intelligently uses image metrics based on the particular visual task and human expectations and human visual system performance parameters. / Thesis (Ph.D.)--Medical School; School of Computer Science, 1999.

Augmented reality (AR) is seeing a rapid expansion into several domains due to the proliferation of more accessible and powerful hardware. While augmented reality user interfaces (AR UIs) allow the presentation of information atop the real world, this extra visual data potentially comes at a cost of increasing the visual clutter of the users' field of view, which can increase visual search time, error rates, and have an overall negative effect on performance. Visual clutter has been studied for existing display technologies, but there are no established measures of visual clutter for AR UIs which precludes the study of the effects of clutter on performance in AR UIs. The first objective of this research is to determine the applicability of extant image analysis measures of feature congestion, edge density, and sub-band entropy for measuring visual clutter in the head-worn optical see-through AR space and establish a relationship between image analysis measures of clutter and visual search time. These image analysis measures are specifically chosen to quantify clutter, as they can be applied to complex and naturalistic scenes, as is common to experience while using an optical see-through AR UI. The second objective is to examine the effects of AR UIs comprised of multiple apparent depths on user performance through the metric of visual search time. The third objective is to determine the effects of other AR UI properties such as target clutter, target eccentricity, target apparent depth and target total distance on performance as measured through visual search time. These results will then be used to develop a visual clutter score, which will rate different AR UIs against each other. Image analysis measures for clutter of feature congestion, edge density, and sub-band entropy of clutter were correlated to visual search time when they were taken for the overall AR UI and when they were taken for a target object that a participant was searching for. In the case of an AR UI comprised of both projected and AR parts, image analysis measures were not correlated to visual search time for the constituent AR UI parts (projected or AR) but were still correlated to the overall AR UI clutter. Target eccentricity also had an effect on visual search time, while target apparent depth and target total distance from center did not. Target type and AR object percentage also had an effect on visual search time. These results were synthesized into a general model known as the "AR UI Visual Clutter Score Algorithm" using a multiple regression. This model can be used to compare different AR UIs to each other in order to identify the AR UI that is projected to have lower target visual search times. / Doctor of Philosophy / Augmented reality is a novel but growing technology. The ability to project visual information into the real-world comes with many benefits, but at the cost of increasing visual clutter. Visual clutter in existing displays has been shown to negatively affect visual search time, error rates, and general performance, but there are no established measures of visual clutter augmented reality displays, so it is unknown if visual clutter will have the same effects. The first objective of this research is to establish measures of visual clutter for augmented reality displays. The second objective is to better understand the unique properties of augmented reality displays, and how that may affect ease of use. Measures of visual clutter were correlated to visual search time when they were taken for the augmented reality user interface, and when they were taken for a given target object within that a participant was searching for. It was also found that as targets got farther from the center of the field of view, visual search time increased, while the depth of a target from the user and the total distance a target was from the user did not. Study 1 also showed that target type and AR object percentage also had an effect on visual search time. Combining these results gives a model that can be used to compare different augmented reality user interfaces to each other.

Visual clutter denotes a disordered collection of graphical entities in information visualization. It can obscure the structure present in the data. Even in a small dataset, visual clutter makes it hard for the viewer to find patterns, relationships and structure. In this thesis, I study visual clutter with four distinct visualization techniques, and present the concept and framework of Clutter-Based Dimension Reordering (CBDR). Dimension order is an attribute that can significantly affect a visualization's expressiveness. By varying the dimension order in a display, it is possible to reduce clutter without reducing data content or modifying the data in any way. Clutter reduction is a display-dependent task. In this thesis, I apply the CBDR framework to four different visualization techniques. For each display technique, I determine what constitutes clutter in terms of display properties, then design a metric to measure visual clutter in this display. Finally I search for an order that minimizes the clutter in a display. Different algorithms for the searching process are discussed in this thesis as well. In order to gather users' responses toward the clutter measures used in the Clutter-Based Dimension Reordering process and validate the usefulness of CBDR, I also conducted an evaluation with two groups of users. The study result proves that users find our approach to be helpful for visually exploring datasets. The users also had many comments and suggestions for the CBDR approach as well as for visual clutter reduction in general. The content and result of the user study are included in this thesis.

With increasing information density and complexity, computer displays may become visually cluttered, adversely affecting overall usability. Text labels can significantly add to visual clutter in graphical user interfaces, but are generally kept legible through specific label placement algorithms that seek visual separation of labels and other objects in the 2D view plane. This work studies an alternative approach: can overlapping labels be visually segregated by distributing them in stereoscopic depth? The fact that we have two forward-looking eyes yields stereoscopic disparity: each eye has a slightly different perspective on objects in the visual field. Disparity is used for depth perception by the human visual system, and is therefore also provided by stereoscopic 3D displays to produce a sense of depth. This work has shown that a stereoscopic label placement algorithm yields user performance comparable with existing algorithms that separate labels in the view plane. At the same time, such stereoscopic label placement is subjectively rated significantly less disturbing than traditional methods. Furthermore, it does not allow for potentially ambiguous spatial relationships between labels and background objects inherent to labels separated in the view plane. These findings are important for display systems where disturbance, distraction and ambiguity of the overlay can negatively impact safety and efficiency of the system, including the reference application of this work: an augmented vision system for Air Traffic Control towers.

The thesis performs an analysis on visuospatial complexity of dynamic scenes, and morespecifically driving scenes in the propose of gaining a knowledge on human visual perception of the visual information present in a typical driving scene. The analysis and measurement of visual complexity is performed by utilizing two different measure modelsfor measuring visual clutter, Feature congestion clutter measure [1] and Subband entropyclutter measure[1] introduced by Rosenholtz, a cognitive science and research. The thesisrepresent the performance of the computational models on a data set consisting of sixepisodes that simulate driving scenes with different settings and combination of visualfeatures. The results of evaluating the measure models are used to introduce a formulafor measuring visual complexity of annotated images by extracting valuable informationfrom the annotated data set using Scalabel[2], an annotation web- based open source tool.

Visuospatial complexity refers to the level of detail or intricacy present within a scene, takinginto account both spatial and visual properties of the dynamic scene or the place (e.g.moving images, everyday driving, video games and other immersive media). There havebeen several studies on measuring visual complexity from various viewpoints, e.g. marketing,psychology, computer vision and cognitive science. This research project aims atanalysing and evaluating different models and tools that have been developed to measurelow-level features of visuospatial complexity such as Structural Similarity Index measurement,Feature Congestion measurement of clutter and Subband Entropy measurement ofclutter. We use two datasets, one focusing on (reflectional) symmetry in static images,and another that consists of real-world driving videos. The results of the evaluation showdifferent correlations between the implemented models such that the nature of the sceneplays a significant role.

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Les salles de classe sont des milieux bruyants auditivement et visuellement. Au Québec, le bruit auditif dans les écoles s’avère une problématique qui règne depuis des dizaines d’années et qui reste encore d’actualité aujourd’hui. Le bruit environnant peut impacter négativement le bien-être et les apprentissages des élèves et le bien-être des enseignant·es. D’autant plus, souvent, la source principale de bruit dans les écoles provient de la parole, un comportement qui peut être contrôlé. Les élèves et surtout les enseignant·es surchargent leur système vocal pour pouvoir faire passer leur message oral à travers le bruit auditif, augmentant leur niveau d’effort vocal. Dans les salles de classe, les distractions visuelles telles que les affiches murales contribuent à l’encombrement visuel, qui peut être considéré comme du « bruit visuel ». Celui-ci peut également impacter négativement le bien-être des élèves et leurs apprentissages. Considérant la nature multimodale, ou audiovisuelle, de la parole, nous nous questionnions sur l’influence de l’encombrement visuel sur la parole. Ce mémoire de maîtrise, composé de deux études, avait pour but de 1) étudier la perception subjective des élèves québécois de leur salle de classe en relation avec des données acoustiques objectives, et 2) étudier l’effet de l’encombrement visuel sur l’effort vocal en termes de paramètres acoustiques (SPL, f0 et CPP) et de perception auto-rapportée de locutrices. Les deux études se sont basées sur des méthodes mixtes, intégrant des méthodes quantitatives similaires (ex. : collecte de mesures objectives acoustiques, analyses acoustiques) et des méthodes qualitatives similaires (ex. : collecte de données subjectives perceptuelles, analyse inductive de questions ouvertes). Les analyses quantitatives et qualitatives des deux études révèlent que bien que les résultats relatifs aux données objectives (mesures acoustiques) n’étaient pas statistiquement significatifs, les résultats relatifs aux données subjectives (perception) l’étaient. Par exemple, dans la première étude, les niveaux d’appréciation du son des élèves (EAE) n’étaient pas tous corrélés avec les SPLs, et dans la deuxième étude, le niveau d’encombrement visuel n’était pas corrélé aux paramètres acoustiques de SPL, f0 et CPP. Ces résultats mettent en lumière l’importance de considérer et d’inclure des données subjectives et perceptuelles dans les études portant sur les paysages sonores éducatifs et sur l’effort vocal, car ce sont des expériences vécues par des humains. / Classrooms are acoustically and visually noisy environments. In Quebec, auditory noise in schools is a problem that has existed for decades and is still relevant today. Background noise can negatively impact the well-being and learning of students and the well-being of teachers. Moreover, the main source of noise in schools comes from speech, a behavior that can be controlled. Students and especially teachers put pressure on their vocal system to be able to convey their oral message through auditory noise, increasing their level of vocal effort. In classrooms, visual distractions such as wall posters can be considered as visual clutter, or visual noise, an element that can also negatively impact students’ well-being and learning. Considering the multimodal, or audiovisual, nature of speech, we question the influence of visual clutter on speech. This master's thesis, composed of two studies, aimed to 1) study the subjective perception of Quebec students of their classroom in relation to objective acoustic data, and 2) study the effect of visual clutter on vocal effort in terms of acoustic parameters (SPL, f0 and CPP) and self-reported perception of female speakers. Both studies were based on mixed methods, integrating similar quantitative methods (e.g., collection of objective acoustic measurements, acoustic analyses) and similar qualitative methods (e.g., collection of subjective perceptual data, inductive analysis of open-ended questions). Quantitative and qualitative analyses of the two studies reveal that although the results relating to the objective data (acoustic measurements) were not statistically significant, the results relating to the subjective data (perception) were. For example, in the first study, students' Evaluation of the Acoustic Environment (EAE) were not all correlated with measured SPLs, and in the second study, the level of visual clutter was not correlated with acoustic parameters of SPL, f0 and CPP. These findings highlight the importance of considering and including subjective and perceptual data in studies of educational soundscapes and vocal effort, as the human experience is subjective.