La perception d'attributs visuels de premier et deuxième ordres

Thèse numérisée par la Division de la gestion de documents et des archives de l'Université de Montréal.

PLANT LEAF IDENTIFICATION BASED ON VOLUMETRIC FRACTAL DIMENSION

Texture is an important visual attribute used to describe the pixel organization in an image. As well as it being easily identified by humans, its analysis process demands a high level of sophistication and computer complexity. This paper presents a novel approach for texture analysis, based on analyzing the complexity of the surface generated from a texture, in order to describe and characterize it. The proposed method produces a texture signature which is able to efficiently characterize different texture classes. The paper also illustrates a novel method performance on an experiment using texture images of leaves. Leaf identification is a difficult and complex task due to the nature of plants, which presents a huge pattern variation. The high classification rate yielded shows the potential of the method, improving on traditional texture techniques, such as Gabor filters and Fourier analysis.

Visual attention and attribute attendance in multi-attribute choice experiments

Decision strategies in multi-attribute Choice Experiments are investigated using eye-tracking. The visual attention towards, and attendance of, attributes is examined. Stated attendance is found to diverge substantively from visual attendance of attributes. However, stated and visual attendance are shown to be informative, non-overlapping sources of information about respondent utility functions when incorporated into model estimation. Eye-tracking also reveals systematic nonattendance of attributes only by a minority of respondents. Most respondents visually attend most attributes most of the time. We find no compelling evidence that the level of attention is related to respondent certainty, or that higher or lower value attributes receive more or less attention

Cortical organization of environmental sounds by attribute

Semantic knowledge is supported by a widely distributed neuronal network, with differential patterns of activation depending upon experimental stimulus or task demands. Despite a wide body of knowledge on semantic object processing from the visual modality, the response of this semantic network to environmental sounds remains relatively unknown. Here, we used fMRI to investigate how access to different conceptual attributes from environmental sound input modulates this semantic network. Using a range of living and manmade sounds, we scanned participants whilst they carried out an object attribute verification task. Specifically, we tested visual perceptual, encyclopedic, and categorical attributes about living and manmade objects relative to a high-level auditory perceptual baseline to investigate the differential patterns of response to these contrasting types of object-related attributes, whilst keeping stimulus input constant across conditions. Within the bilateral distributed network engaged for processing environmental sounds across all conditions, we report here a highly significant dissociation within the left hemisphere between the processing of visual perceptual and encyclopedic attributes of objects.

Visual openness in urban environments: Measuring visual openness and visibility to natural landscapes in a changing urban landscape in Gold Coast, Australia

The visual characteristics of urban environments have been changing dramatically with the growth of cities around the world. Protection and enhancement of landscape character in urban environments have been one of the challenges for policy makers in addressing sustainable urban growth. Visual openness and enclosure in urban environments are important attributes in perception of visual space which affect the human interaction with physical space and which can be often modified by new developments. Measuring visual openness in urban areas results in more accurate, reliable, and systematic approach to manage and control visual qualities in growing cities. Recent advances in techniques in geographic information systems (GIS) and survey systems make it feasible to measure and quantify this attribute with a high degree of realism and precision. Previous studies in this field do not take full advantage of these improvements. This paper proposes a method to measure the visual openness and enclosure in a changing urban landscape in Australia, on the Gold Coast, by using the improved functionality in GIS. Using this method, visual openness is calculated and described for all publicly accessible areas in the selected study area. A final map is produced which shows the areas with highest visual openness and visibility to natural landscape resources. The output of this research can be used by planners and decision-makers in managing and controlling views in complex urban landscapes. Also, depending on the availability of GIS data, this method can be applied to any region including non-urban landscapes to help planners and policy-makers manage views and visual qualities.

A Structured Model of Video Reproduces Primary Visual Cortical Organisation

The visual system must learn to infer the presence of objects and features in the world from the images it encounters, and as such it must, either implicitly or explicitly, model the way these elements interact to create the image. Do the response properties of cells in the mammalian visual system reflect this constraint? To address this question, we constructed a probabilistic model in which the identity and attributes of simple visual elements were represented explicitly and learnt the parameters of this model from unparsed, natural video sequences. After learning, the behaviour and grouping of variables in the probabilistic model corresponded closely to functional and anatomical properties of simple and complex cells in the primary visual cortex (V1). In particular, feature identity variables were activated in a way that resembled the activity of complex cells, while feature attribute variables responded much like simple cells. Furthermore, the grouping of the attributes within the model closely parallelled the reported anatomical grouping of simple cells in cat V1. Thus, this generative model makes explicit an interpretation of complex and simple cells as elements in the segmentation of a visual scene into basic independent features, along with a parametrisation of their moment-by-moment appearances. We speculate that such a segmentation may form the initial stage of a hierarchical system that progressively separates the identity and appearance of more articulated visual elements, culminating in view-invariant object recognition.

Semantic transform: Weakly supervised semantic inference for relating visual attributes

Relative (comparative) attributes are promising for thematic ranking of visual entities, which also aids in recognition tasks. However, attribute rank learning often requires a substantial amount of relational supervision, which is highly tedious, and apparently impractical for real-world applications. In this paper, we introduce the Semantic Transform, which under minimal supervision, adaptively finds a semantic feature space along with a class ordering that is related in the best possible way. Such a semantic space is found for every attribute category. To relate the classes under weak supervision, the class ordering needs to be refined according to a cost function in an iterative procedure. This problem is ideally NP-hard, and we thus propose a constrained search tree formulation for the same. Driven by the adaptive semantic feature space representation, our model achieves the best results to date for all of the tasks of relative, absolute and zero-shot classification on two popular datasets. © 2013 IEEE.

Visual cortex frontend: integrating lines, edges, keypoints and disparaty

We present a 3D representation that is based on the pro- cessing in the visual cortex by simple, complex and end-stopped cells. We improved multiscale methods for line/edge and keypoint detection, including a method for obtaining vertex structure (i.e. T, L, K etc). We also describe a new disparity model. The latter allows to attribute depth to detected lines, edges and keypoints, i.e., the integration results in a 3D \wire-frame" representation suitable for object recognition.

Beyond the analogon; the "transfer of the referent" : the relationship between the external world experienced through culturally generated perceptual models constructed in the human mind, and the transfer of these images, via visual language, onto referen

Within 20th. Century art, the concept of the ‘normative’ image, as an attribute of things, has been challenged. As a consequence, paintings must now picture the ‘real’ world in other ways, incorporating knowledge and meaning beyond the analogon. Such descriptive representations were revealed as paradigmatic, rather than incontrovertible fact. Dependent on pre-conceived notions of stereotypicality, these descriptive images relied on surface illumination. My thesis explores images of things in the world as culturally inspired and information based. I examine paintings and sculptures of other cultures, such as black African, and other historical periods such as the Medieval, which reveal metonymously the basis for variations in representations of the ‘real’ world. The new enhanced representations which Modern artists created in their work were denigrated as deviant from the absolute ‘normative’ or regarded as distortions for purely mannerist and stylistic reasons. Postmodern research has reassessed them as multiple or extended imagings in whose facture new knowledge and human responses can be incorporated. These new forms of representation can be regarded as theoretical constructs rather than stylised depictions of appearance. In this way referents are transferred through the mind onto objects and vistas in the real world to align with our developed view of the physical world and better our understanding of humanity’s symbiotic relationship with nature.

A method for the visual representation of historic multivariate point data

The visual representation of multivariate spatial and temporal data is important for interpreting and analyzing historical geographic patterns that change over time. The introduction of geospatial technologies in historical scholarship has challenged the suitability of current visual representations due to the need for greater temporal emphasis and the tracking of historical events over time. This research presents a holistic multivariate approach to historical visual representation for point based historical data. The method has been developed through extending the spatial presence in information graphics and through meaningful spatial classification. This paper demonstrates the benefits gained from integrating historical, geographic, temporal, and attribute data through the development of a case study on the history of Melbourne’s cinema venues between 1946 and 1986.

Multidimensional Projections for Visual Analysis of Social Networks

Visual analysis of social networks is usually based on graph drawing algorithms and tools. However, social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context. Context, in its turn, is given by attributes associated with graph elements, such as individual nodes, edges, and groups of edges, as well as by the nature of the connections between individuals. In most systems, attributes of individuals and communities are not taken into consideration during graph layout, except to derive weights for force-based placement strategies. This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings. These properties are employed to layout nodes on the plane via multidimensional projection techniques. For the attribute mapping, we show that node proximity in the layout corresponds to similarity in attribute, leading to easiness in locating similar groups of nodes. The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm, reaching a meaningful layout in one pass. When a force algorithm is then applied to this initial mapping, the final layout presents better properties than conventional force-based approaches. Numerical evaluations show a number of advantages of pre-mapping points via projections. User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone. In order to allow better space usage for complex networks, a graph mapping on the surface of a sphere is also implemented.