339,911 research outputs found
Picture this: researching child workers
Visual methods such as photography are under-used in the active process of sociological research. As rare as visual methods are, it is even rarer for the resultant images to be made by rather than of research participants. Primarily, the paper explores the challenges and contradictions of using photography within a multi-method approach. We consider processes for analysing visual data, different ways of utilising visual methods in sociological research, and the use of primary and secondary data, or, simple illustration versus active visual exploration of the social. The question of triangulation of visual data against text and testimony versus a stand-alone approach is explored in depth
Neurophysiological Responses to Different Product Experiences
It is well known that the evaluation of a product from the shelf considers the simultaneous cerebral and emotional evaluation of
the different qualities of the product such as its colour, the eventual images shown, and the envelope’s texture (hereafter all
included in the term “product experience”). However, the measurement of cerebral and emotional reactions during the interaction
with food products has not been investigated in depth in specialized literature. (e aim of this paper was to investigate
such reactions by the EEG and the autonomic activities, as elicited by the cross-sensory interaction (sight and touch) across several
different products. In addition, we investigated whether (i) the brand (Major Brand or Private Label), (ii) the familiarity (Foreign
or Local Brand), and (iii) the hedonic value of products (Comfort Food or Daily Food) influenced the reaction of a group of
volunteers during their interaction with the products. Results showed statistically significantly higher tendency of cerebral
approach (as indexed by EEG frontal alpha asymmetry) in response to comfort food during the visual exploration and the visual
and tactile exploration phases. Furthermore, for the same index, a higher tendency of approach has been found toward foreign
food products in comparison with local food products during the visual and tactile exploration phase. Finally, the same
comparison performed on a different index (EEG frontal theta) showed higher mental effort during the interaction with foreign
products during the visual exploration and the visual and tactile exploration phases. Results from the present study could deepen
the knowledge on the neurophysiological response to food products characterized by different nature in terms of hedonic value
familiarity; moreover, they could have implications for food marketers and finally lead to further study on how people make food
choices through the interactions with their commercial envelope
De-Placement: Constructing and Mapping Place in Collaboration with Artists
Extension of the formal report using images to present a more visual exploration of project findings.Arts and Humanities Research Counci
The nondual level: the cosmopolitan imagination of liquid modernity as visual midrash.
This thesis is an exploration of the cosmopolitan imagination in liquid modernity. The artworks are responses to text, memory and images as a form of visual midrash. These visual responses are not hierarchal, but rather part of an infinite relationship of Everything and Nothingness
Combining Visual and Textual Features for Semantic Segmentation of Historical Newspapers
The massive amounts of digitized historical documents acquired over the last
decades naturally lend themselves to automatic processing and exploration.
Research work seeking to automatically process facsimiles and extract
information thereby are multiplying with, as a first essential step, document
layout analysis. If the identification and categorization of segments of
interest in document images have seen significant progress over the last years
thanks to deep learning techniques, many challenges remain with, among others,
the use of finer-grained segmentation typologies and the consideration of
complex, heterogeneous documents such as historical newspapers. Besides, most
approaches consider visual features only, ignoring textual signal. In this
context, we introduce a multimodal approach for the semantic segmentation of
historical newspapers that combines visual and textual features. Based on a
series of experiments on diachronic Swiss and Luxembourgish newspapers, we
investigate, among others, the predictive power of visual and textual features
and their capacity to generalize across time and sources. Results show
consistent improvement of multimodal models in comparison to a strong visual
baseline, as well as better robustness to high material variance
Visual Exploration And Information Analytics Of High-Dimensional Medical Images
Data visualization has transformed how we analyze increasingly large and complex data sets. Advanced visual tools logically represent data in a way that communicates the most important information inherent within it and culminate the analysis with an insightful conclusion. Automated analysis disciplines - such as data mining, machine learning, and statistics - have traditionally been the most dominant fields for data analysis. It has been complemented with a near-ubiquitous adoption of specialized hardware and software environments that handle the storage, retrieval, and pre- and postprocessing of digital data. The addition of interactive visualization tools allows an active human participant in the model creation process. The advantage is a data-driven approach where the constraints and assumptions of the model can be explored and chosen based on human insight and confirmed on demand by the analytic system. This translates to a better understanding of data and a more effective knowledge discovery. This trend has become very popular across various domains, not limited to machine learning, simulation, computer vision, genetics, stock market, data mining, and geography.
In this dissertation, we highlight the role of visualization within the context of medical image analysis in the field of neuroimaging. The analysis of brain images has uncovered amazing traits about its underlying dynamics. Multiple image modalities capture qualitatively different internal brain mechanisms and abstract it within the information space of that modality. Computational studies based on these modalities help correlate the high-level brain function measurements with abnormal human behavior. These functional maps are easily projected in the physical space through accurate 3-D brain reconstructions and visualized in excellent detail from different anatomical vantage points. Statistical models built for comparative analysis across subject groups test for significant variance within the features and localize abnormal behaviors contextualizing the high-level brain activity. Currently, the task of identifying the features is based on empirical evidence, and preparing data for testing is time-consuming. Correlations among features are usually ignored due to lack of insight. With a multitude of features available and with new emerging modalities appearing, the process of identifying the salient features and their interdependencies becomes more difficult to perceive. This limits the analysis only to certain discernible features, thus limiting human judgments regarding the most important process that governs the symptom and hinders prediction. These shortcomings can be addressed using an analytical system that leverages data-driven techniques for guiding the user toward discovering relevant hypotheses.
The research contributions within this dissertation encompass multidisciplinary fields of study not limited to geometry processing, computer vision, and 3-D visualization. However, the principal achievement of this research is the design and development of an interactive system for multimodality integration of medical images. The research proceeds in various stages, which are important to reach the desired goal. The different stages are briefly described as follows: First, we develop a rigorous geometry computation framework for brain surface matching. The brain is a highly convoluted structure of closed topology. Surface parameterization explicitly captures the non-Euclidean geometry of the cortical surface and helps derive a more accurate registration of brain surfaces. We describe a technique based on conformal parameterization that creates a bijective mapping to the canonical domain, where surface operations can be performed with improved efficiency and feasibility. Subdividing the brain into a finite set of anatomical elements provides the structural basis for a categorical division of anatomical view points and a spatial context for statistical analysis. We present statistically significant results of our analysis into functional and morphological features for a variety of brain disorders.
Second, we design and develop an intelligent and interactive system for visual analysis of brain disorders by utilizing the complete feature space across all modalities. Each subdivided anatomical unit is specialized by a vector of features that overlap within that element. The analytical framework provides the necessary interactivity for exploration of salient features and discovering relevant hypotheses. It provides visualization tools for confirming model results and an easy-to-use interface for manipulating parameters for feature selection and filtering. It provides coordinated display views for visualizing multiple features across multiple subject groups, visual representations for highlighting interdependencies and correlations between features, and an efficient data-management solution for maintaining provenance and issuing formal data queries to the back end
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