Exploring the World's Visual History

Thesis (Ph.D.)--University of Washington, 2016-08

Understanding the Patient Experience Through the Power of Film: a Mixed Method Qualitative Research Study

Background For decades film has proved to be a powerful form of communication. Whether produced as entertainment, art or documentary, films have the capacity to inform and move us. Films are a highly attractive teaching instrument and an appropriate teaching method in health education. It is a valuable tool for studying situations most transcendental to human beings such as pain, disease and death. Objectives The objectives were to determine how this helps students engage with their role as health care professionals; to determine how they view the personal experience of illness, disease, disability or death; and to determine how this may impact upon their provision of patient care. Design, Setting and Participants The project was underpinned by the film selection determined by considerate review, intensive scrutiny, contemplation and discourse by the research team. 7 films were selected, ranging from animation; foreign, documentary, biopic and Hollywood drama. Each film was shown discretely, in an acoustic lecture theatre projected onto a large screen to pre-registration student nurses (adult, child and mental health) across each year of study from different cohorts (n = 49). Method A mixed qualitative method approach consisted of audio-recorded 5-minute reactions post film screening; coded questionnaires; and focus group. Findings were drawn from the impact of the films through thematic analysis of data sets and subjective text condensation categorised as: new insights looking through patient eyes; evoking emotion in student nurses; spiritual care; going to the moves to learn about the patient experience; self discovery through films; using films to link theory to practice. Results Deeper learning through film as a powerful medium was identified in meeting the objectives of the study. Integration of film into pre registration curriculum, pedagogy, teaching and learning is recommended. Conclusion The teaching potential of film stems from the visual process linked to human emotion and experience. Its impact has the power to not only help in learning the values that underpin nursing, but also for respecting the patient experience of disease, disability, death and its reality.

Obrazy muzyki w twórczości Janiny Kraupe-Świderskiej

Filologii Polskiej i Klasycznej: Zakład Dydaktyki Literatury i Języka Polskiego

Datasets, features, learning, and models in visual recognition

Visual recognition is a fundamental research topic in computer vision. This dissertation explores datasets, features, learning, and models used for visual recognition. In order to train visual models and evaluate different recognition algorithms, this dissertation develops an approach to collect object image datasets on web pages using an analysis of text around the image and of image appearance. This method exploits established online knowledge resources (Wikipedia pages for text; Flickr and Caltech data sets for images). The resources provide rich text and object appearance information. This dissertation describes results on two datasets. The first is Berg’s collection of 10 animal categories; on this dataset, we significantly outperform previous approaches. On an additional set of 5 categories, experimental results show the effectiveness of the method. Images are represented as features for visual recognition. This dissertation introduces a text-based image feature and demonstrates that it consistently improves performance on hard object classification problems. The feature is built using an auxiliary dataset of images annotated with tags, downloaded from the Internet. Image tags are noisy. The method obtains the text features of an unannotated image from the tags of its k-nearest neighbors in this auxiliary collection. A visual classifier presented with an object viewed under novel circumstances (say, a new viewing direction) must rely on its visual examples. This text feature may not change, because the auxiliary dataset likely contains a similar picture. While the tags associated with images are noisy, they are more stable when appearance changes. The performance of this feature is tested using PASCAL VOC 2006 and 2007 datasets. This feature performs well; it consistently improves the performance of visual object classifiers, and is particularly effective when the training dataset is small. With more and more collected training data, computational cost becomes a bottleneck, especially when training sophisticated classifiers such as kernelized SVM. This dissertation proposes a fast training algorithm called Stochastic Intersection Kernel Machine (SIKMA). This proposed training method will be useful for many vision problems, as it can produce a kernel classifier that is more accurate than a linear classifier, and can be trained on tens of thousands of examples in two minutes. It processes training examples one by one in a sequence, so memory cost is no longer the bottleneck to process large scale datasets. This dissertation applies this approach to train classifiers of Flickr groups with many group training examples. The resulting Flickr group prediction scores can be used to measure image similarity between two images. Experimental results on the Corel dataset and a PASCAL VOC dataset show the learned Flickr features perform better on image matching, retrieval, and classification than conventional visual features. Visual models are usually trained to best separate positive and negative training examples. However, when recognizing a large number of object categories, there may not be enough training examples for most objects, due to the intrinsic long-tailed distribution of objects in the real world. This dissertation proposes an approach to use comparative object similarity. The key insight is that, given a set of object categories which are similar and a set of categories which are dissimilar, a good object model should respond more strongly to examples from similar categories than to examples from dissimilar categories. This dissertation develops a regularized kernel machine algorithm to use this category dependent similarity regularization. Experiments on hundreds of categories show that our method can make significant improvement for categories with few or even no positive examples.

Re-viewing history: antiquaries, the graphic arts and Scotland's lost geographies, c.1660-1820

This thesis examines topographical art depicting Scotland’s natural scenery and built environments, architecture, antiquities and signs of modern improvement, made during the period 1660 to 1820. It sets out to demonstrate that topography and topographical art was not exclusively antiquarian in nature, but ranged across various fields of learning and practice. It included the work of artists, geographers, cartographers, travel writers, poets, landscape gardeners, military surveyors, naturalists and historians who were concerned with representing the country’s varied, and often contentious, histories within an increasingly modernising present. The visual images that are considered here were forms of knowledge that found expression in drawings, paintings and engravings, elevations, views and plans. They were made on military surveys and picturesque tours, and were often intended to be included alongside written texts, both published and unpublished, frequently connecting with travels, tours, memoirs, essays and correspondence. It will also be argued that topography was a social practice, involving networks of artists, collectors, publishers and writers, who exchanged information in drawings and letters in a nationwide, and often increasingly commercial enterprise. This thesis will explore some of the strands of such a vast network of picture-making that existed in Scotland, and Britain, between 1660 and 1820, as visual images were circulated, copied, recycled and adapted, and topographical and antiquarian visual culture emerges as a complex, synoptic form of inquiry.

A narrativa histórica de Alexandre Wollner sobre o design brasileiro em sua relação com arte, indústria e tecnologia

This work aims to investigate the historical narratives in which the graphic designer Alexandre Wollner assembled about the development of its own profession in Brazil, focusing the ways in which his discourse points relations among design (with greater emphasis in graphic design) and visual arts, the industrial development and notions about technology. Firstly, the theoretical setup searched for dialogues with design historians, with Mikhail Bakhtin, specially his concepts about “ideology” and “discourse’, and the theory of Field Autonomy by Pierre Bourdieu applied in the artistic practice. Following, the relation between Wollner’s own journey and the Brazilian industrial development is shown, and, at last, three of his historical texts are studied, which are written in different moments (1964; 1983; 1998), being those in which the analyzed author wished to point out the origens, events and names that are more remarkable. Throughout the work, it is pointed the importance of Wollner’s contact with the modernist european ideologies that share an abstract and rationalist matrix found at Hochschule für Gestaltung Ulm (HfG Ulm), the german design school from the city of Ulm, in the 1950s. Such modernist discourse understood the practice of design as a method with scientific character, being then different of some other more recurring artistic professional practices in some productive sectors. Wollner aimed to apply such ideals in his professional practice, being the foundation of the paulista office forminform, in 1958, one of his first expressions of such posture, and in his academic practice, helping the foundation of the Escola Superior de Desenho Industrial (ESDI), in Rio de Janeiro, in 1963. Such modernist ideals went along with moments of the Brazilian industrial development during the government of Juscelino Kubitschek (1956–1961) and the “Economical Miracle” from the military government (1968–1973). Wollner argued about the need for the development of national design as a technological and productive differential that would help the growth of national industry, based on Ulm’s project model concept. It is defended that Wollner’s professional and intelectual path, in his efforts of thinking a history of Brazilian design through the choice of pioneers in the area, was founded on an “ideal model” of design, leaving aside the modernist experiences from the 1950s. Such posture would indicate a search for validation of his own profession that was beginning to become more evident in Brazilian productive means, aiming the creation of a differential space in comparison with pre-established practices, usually link to graphic artists from the time.

Estimates of genetic parameters for visual scores and their correlation with production and reproductive traits in Brahman cattle

The objective of this study was to evaluate the association of visual scores of body structure, precocity and muscularity with production (body weight at 18 months and average daily gain) and reproductive (scrotal circumference) traits in Brahman cattle in order to determine the possible use of these scores as selection criteria to improve carcass quality. Covariance components were estimated by the restricted maximum likelihood method using an animal model that included contemporary group as fixed effect. A total of 1,116 observations of body structure, precocity and muscularity were used. Heritability was 0.39, 043 and 0.40 for body structure, precocity and muscularity, respectively. The genetic correlations were 0.79 between body structure and precocity, 0.87 between body structure and muscularity, and 0.91 between precocity and muscularity. The genetic correlations between visual scores and body weight at 18 months were positive (0.77, 0.57 and 0.59 for body structure, precocity and muscularity, respectively). Similar genetic correlations were observed between average daily gain and visual scores (0.60, 0.57 and 0.48, respectively), whereas the genetic correlations between scrotal circumference and these scores were low (0.13, 0.02, and 0.13). The results indicate that visual scores can be used as selection criteria in Brahman breeding programs. Favorable correlated responses should be seen in average daily gain and body weight at 18 months. However, no correlated response is expected for scrotal circumference.

Integrated visual perception architecture for robotic clothes perception and manipulation

This thesis proposes a generic visual perception architecture for robotic clothes perception and manipulation. This proposed architecture is fully integrated with a stereo vision system and a dual-arm robot and is able to perform a number of autonomous laundering tasks. Clothes perception and manipulation is a novel research topic in robotics and has experienced rapid development in recent years. Compared to the task of perceiving and manipulating rigid objects, clothes perception and manipulation poses a greater challenge. This can be attributed to two reasons: firstly, deformable clothing requires precise (high-acuity) visual perception and dexterous manipulation; secondly, as clothing approximates a non-rigid 2-manifold in 3-space, that can adopt a quasi-infinite configuration space, the potential variability in the appearance of clothing items makes them difficult to understand, identify uniquely, and interact with by machine. From an applications perspective, and as part of EU CloPeMa project, the integrated visual perception architecture refines a pre-existing clothing manipulation pipeline by completing pre-wash clothes (category) sorting (using single-shot or interactive perception for garment categorisation and manipulation) and post-wash dual-arm flattening. To the best of the author’s knowledge, as investigated in this thesis, the autonomous clothing perception and manipulation solutions presented here were first proposed and reported by the author. All of the reported robot demonstrations in this work follow a perception-manipulation method- ology where visual and tactile feedback (in the form of surface wrinkledness captured by the high accuracy depth sensor i.e. CloPeMa stereo head or the predictive confidence modelled by Gaussian Processing) serve as the halting criteria in the flattening and sorting tasks, respectively. From scientific perspective, the proposed visual perception architecture addresses the above challenges by parsing and grouping 3D clothing configurations hierarchically from low-level curvatures, through mid-level surface shape representations (providing topological descriptions and 3D texture representations), to high-level semantic structures and statistical descriptions. A range of visual features such as Shape Index, Surface Topologies Analysis and Local Binary Patterns have been adapted within this work to parse clothing surfaces and textures and several novel features have been devised, including B-Spline Patches with Locality-Constrained Linear coding, and Topology Spatial Distance to describe and quantify generic landmarks (wrinkles and folds). The essence of this proposed architecture comprises 3D generic surface parsing and interpretation, which is critical to underpinning a number of laundering tasks and has the potential to be extended to other rigid and non-rigid object perception and manipulation tasks. The experimental results presented in this thesis demonstrate that: firstly, the proposed grasp- ing approach achieves on-average 84.7% accuracy; secondly, the proposed flattening approach is able to flatten towels, t-shirts and pants (shorts) within 9 iterations on-average; thirdly, the proposed clothes recognition pipeline can recognise clothes categories from highly wrinkled configurations and advances the state-of-the-art by 36% in terms of classification accuracy, achieving an 83.2% true-positive classification rate when discriminating between five categories of clothes; finally the Gaussian Process based interactive perception approach exhibits a substantial improvement over single-shot perception. Accordingly, this thesis has advanced the state-of-the-art of robot clothes perception and manipulation.

Robust Stereo Visual Odometry through a Probabilistic Combination of Points and Line Segments

Most approaches to stereo visual odometry reconstruct the motion based on the tracking of point features along a sequence of images. However, in low-textured scenes it is often difficult to encounter a large set of point features, or it may happen that they are not well distributed over the image, so that the behavior of these algorithms deteriorates. This paper proposes a probabilistic approach to stereo visual odometry based on the combination of both point and line segment that works robustly in a wide variety of scenarios. The camera motion is recovered through non-linear minimization of the projection errors of both point and line segment features. In order to effectively combine both types of features, their associated errors are weighted according to their covariance matrices, computed from the propagation of Gaussian distribution errors in the sensor measurements. The method, of course, is computationally more expensive that using only one type of feature, but still can run in real-time on a standard computer and provides interesting advantages, including a straightforward integration into any probabilistic framework commonly employed in mobile robotics.

A three-dimensional representation method for noisy point clouds based on growing self-organizing maps accelerated on GPUs

The research described in this thesis was motivated by the need of a robust model capable of representing 3D data obtained with 3D sensors, which are inherently noisy. In addition, time constraints have to be considered as these sensors are capable of providing a 3D data stream in real time. This thesis proposed the use of Self-Organizing Maps (SOMs) as a 3D representation model. In particular, we proposed the use of the Growing Neural Gas (GNG) network, which has been successfully used for clustering, pattern recognition and topology representation of multi-dimensional data. Until now, Self-Organizing Maps have been primarily computed offline and their application in 3D data has mainly focused on free noise models, without considering time constraints. It is proposed a hardware implementation leveraging the computing power of modern GPUs, which takes advantage of a new paradigm coined as General-Purpose Computing on Graphics Processing Units (GPGPU). The proposed methods were applied to different problem and applications in the area of computer vision such as the recognition and localization of objects, visual surveillance or 3D reconstruction.

A Visual Analytics Approach to Comparing Cohorts of Event Sequences

Sequences of timestamped events are currently being generated across nearly every domain of data analytics, from e-commerce web logging to electronic health records used by doctors and medical researchers. Every day, this data type is reviewed by humans who apply statistical tests, hoping to learn everything they can about how these processes work, why they break, and how they can be improved upon. To further uncover how these processes work the way they do, researchers often compare two groups, or cohorts, of event sequences to find the differences and similarities between outcomes and processes. With temporal event sequence data, this task is complex because of the variety of ways single events and sequences of events can differ between the two cohorts of records: the structure of the event sequences (e.g., event order, co-occurring events, or frequencies of events), the attributes about the events and records (e.g., gender of a patient), or metrics about the timestamps themselves (e.g., duration of an event). Running statistical tests to cover all these cases and determining which results are significant becomes cumbersome. Current visual analytics tools for comparing groups of event sequences emphasize a purely statistical or purely visual approach for comparison. Visual analytics tools leverage humans' ability to easily see patterns and anomalies that they were not expecting, but is limited by uncertainty in findings. Statistical tools emphasize finding significant differences in the data, but often requires researchers have a concrete question and doesn't facilitate more general exploration of the data. Combining visual analytics tools with statistical methods leverages the benefits of both approaches for quicker and easier insight discovery. Integrating statistics into a visualization tool presents many challenges on the frontend (e.g., displaying the results of many different metrics concisely) and in the backend (e.g., scalability challenges with running various metrics on multi-dimensional data at once). I begin by exploring the problem of comparing cohorts of event sequences and understanding the questions that analysts commonly ask in this task. From there, I demonstrate that combining automated statistics with an interactive user interface amplifies the benefits of both types of tools, thereby enabling analysts to conduct quicker and easier data exploration, hypothesis generation, and insight discovery. The direct contributions of this dissertation are: (1) a taxonomy of metrics for comparing cohorts of temporal event sequences, (2) a statistical framework for exploratory data analysis with a method I refer to as high-volume hypothesis testing (HVHT), (3) a family of visualizations and guidelines for interaction techniques that are useful for understanding and parsing the results, and (4) a user study, five long-term case studies, and five short-term case studies which demonstrate the utility and impact of these methods in various domains: four in the medical domain, one in web log analysis, two in education, and one each in social networks, sports analytics, and security. My dissertation contributes an understanding of how cohorts of temporal event sequences are commonly compared and the difficulties associated with applying and parsing the results of these metrics. It also contributes a set of visualizations, algorithms, and design guidelines for balancing automated statistics with user-driven analysis to guide users to significant, distinguishing features between cohorts. This work opens avenues for future research in comparing two or more groups of temporal event sequences, opening traditional machine learning and data mining techniques to user interaction, and extending the principles found in this dissertation to data types beyond temporal event sequences.

Perpectiva linear paramétrica

Tese de Doutoramento em Arquitetura, com a especialização em Comunicação Visual, apresentada na Faculdade de Arquitetura da Universidade de Lisboa, para obtenção do grau de Doutor.

Development of a new wine tasting method based on emotional responses

Mestrado Vinifera Euromaster - Instituto Superior de Agronomia - UL

Dynamic Image Precompensation for Improving Visual Performance of Computer Users with Ocular Aberrations

With the progress of computer technology, computers are expected to be more intelligent in the interaction with humans, presenting information according to the user's psychological and physiological characteristics. However, computer users with visual problems may encounter difficulties on the perception of icons, menus, and other graphical information displayed on the screen, limiting the efficiency of their interaction with computers. In this dissertation, a personalized and dynamic image precompensation method was developed to improve the visual performance of the computer users with ocular aberrations. The precompensation was applied on the graphical targets before presenting them on the screen, aiming to counteract the visual blurring caused by the ocular aberration of the user's eye. A complete and systematic modeling approach to describe the retinal image formation of the computer user was presented, taking advantage of modeling tools, such as Zernike polynomials, wavefront aberration, Point Spread Function and Modulation Transfer Function. The ocular aberration of the computer user was originally measured by a wavefront aberrometer, as a reference for the precompensation model. The dynamic precompensation was generated based on the resized aberration, with the real-time pupil diameter monitored. The potential visual benefit of the dynamic precompensation method was explored through software simulation, with the aberration data from a real human subject. An "artificial eye'' experiment was conducted by simulating the human eye with a high-definition camera, providing objective evaluation to the image quality after precompensation. In addition, an empirical evaluation with 20 human participants was also designed and implemented, involving image recognition tests performed under a more realistic viewing environment of computer use. The statistical analysis results of the empirical experiment confirmed the effectiveness of the dynamic precompensation method, by showing significant improvement on the recognition accuracy. The merit and necessity of the dynamic precompensation were also substantiated by comparing it with the static precompensation. The visual benefit of the dynamic precompensation was further confirmed by the subjective assessments collected from the evaluation participants.

Selection of mango rosa genotypes in a breeding population using the multivariate-biplot method.

Mango (Mangifera indica L.) trees stand out among the main fruit trees cultivated in Brazil. The mango rosa fruit is a very popular local variety (landrace), especially because of their superior technological characteristics such as high contents of Vitamin C and soluble solids (SS), as well as attractive taste and color. The objective of this study was to select a breeding population of mango rosa (polyclonal variety; ≥5 individuals) that can simultaneously meet the fresh and processed fruit Vmarkets, using the multivariate method of principal components and the biplot graphic.