Spatial Elements in Visual Awareness. Challenges for an Intrinsic “Geometry” of the Visible

Un enjeu majeur pour les recherches actuelles dans les sciences de la vision consiste à mettre au point une approche dépendante de l’observateur – une science des apparences visuelles située au-delà de leur véridicité. L’espace dont nous faisons l’expérience subjective est en réalité hautement « illusoire», et les éléments de base du champ visuel sont des structures qualitatives, contextuelles et relationnelles, et non des indices métriques et dépendants du stimulus. Sur la base de nombreux résultats disponibles dans la littérature traitant de la manière dont fonctionnent les divers constituants de l’espace (formes, surfaces, etc.), l’article décrit les éléments qualitatifs de base d’un tel espace et pose la question de la « géométrie» des apparences visuelles. Il formule enfin un ensemble de propositions pour d’éventuelles recherches poursuivant l’examen de l’espace visuel d’un point de vue expérimental.A challenge for current vision science is to develop an observer-dependent science—a science of visual appearances beyond veridicalism. The space that we subjectively experience in vision is, in fact, highly ``illusory’’, and the primitives of the visual field are qualitative, contextual, and relational patterns rather than metric or stimuli-dependent cues. Drawing on the extensive evidence that the experimental literature on visual space perception offers on the behavior of the various constituents of that space such as shapes and surfaces, the paper describes the qualitative primitives of such a space and addresses the question of the intrinsic ``geometry’’ of visual appearances. The paper also makes suggestions for potential future developments of examining visual space from an experimental viewpoint

Logic and phenomenology of incompleteness in illusory figures: new cases and hypotheses

Why is it relevant to analyze the role of incompleteness in illusory figure formation? Incompleteness probes the general problems of organization of the visual world and object segregation. The organization problem is one of the most important problems in visual neuroscience; namely: How and why are a very large numebr of unorganized elements of the retinal image combined, reduced, grouped and segregated to create visual objects? Within the problem of organizaiton, illusory figures are often considered to be one of the best examples to understand how and why the visual system segregates objects with a particular shape, color, and depth stratification. Understanding the role played by incompleteness in inducing illusory figures can thus be useful for understanding the principles of organization (the How) of perceptual forms and the more general logic of perception (the Why). To this purpose, incompletenss is here studied by analyzing its underlying organization principles and its inner logic

Contributions to image-based object reconstruction : geometric and photometric aspects

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Stereoscopic Surface Interpolation from Illusory Contours

Stereoscopic Kanizsa figures are an example of stereoscopic interpolation of an illusory surface. In such stimuli, luminance-defined disparity signals exist only along the edges of inducing elements, but observers reliably perceive a coherent surface that extends across the central region in depth. The aim of this series of experiments was to understand the nature of the disparity signal that underlies the perception of illusory stereoscopic surfaces. I systematically assessed the accuracy and precision of suprathreshold depth percepts using a collection of Kanizsa figures with a wide range of 2D and 3D properties. For comparison, I assessed similar perceptually equated figures with luminance-defined surfaces, with and without inducing elements. A cue combination analysis revealed that observers rely on ordinal depth cues in conjunction with stereopsis when making depth judgements. Thus, 2D properties (e.g. occlusion features and luminance relationships) contribute rich information about 3D surface structure by influencing perceived depth from binocular disparity

NOVEL DENSE STEREO ALGORITHMS FOR HIGH-QUALITY DEPTH ESTIMATION FROM IMAGES

This dissertation addresses the problem of inferring scene depth information from a collection of calibrated images taken from different viewpoints via stereo matching. Although it has been heavily investigated for decades, depth from stereo remains a long-standing challenge and popular research topic for several reasons. First of all, in order to be of practical use for many real-time applications such as autonomous driving, accurate depth estimation in real-time is of great importance and one of the core challenges in stereo. Second, for applications such as 3D reconstruction and view synthesis, high-quality depth estimation is crucial to achieve photo realistic results. However, due to the matching ambiguities, accurate dense depth estimates are difficult to achieve. Last but not least, most stereo algorithms rely on identification of corresponding points among images and only work effectively when scenes are Lambertian. For non-Lambertian surfaces, the brightness constancy assumption is no longer valid. This dissertation contributes three novel stereo algorithms that are motivated by the specific requirements and limitations imposed by different applications. In addressing high speed depth estimation from images, we present a stereo algorithm that achieves high quality results while maintaining real-time performance. We introduce an adaptive aggregation step in a dynamic-programming framework. Matching costs are aggregated in the vertical direction using a computationally expensive weighting scheme based on color and distance proximity. We utilize the vector processing capability and parallelism in commodity graphics hardware to speed up this process over two orders of magnitude. In addressing high accuracy depth estimation, we present a stereo model that makes use of constraints from points with known depths - the Ground Control Points (GCPs) as referred to in stereo literature. Our formulation explicitly models the influences of GCPs in a Markov Random Field. A novel regularization prior is naturally integrated into a global inference framework in a principled way using the Bayes rule. Our probabilistic framework allows GCPs to be obtained from various modalities and provides a natural way to integrate information from various sensors. In addressing non-Lambertian reflectance, we introduce a new invariant for stereo correspondence which allows completely arbitrary scene reflectance (bidirectional reflectance distribution functions - BRDFs). This invariant can be used to formulate a rank constraint on stereo matching when the scene is observed by several lighting configurations in which only the lighting intensity varies

Does semantic size affect size constancy scaling using lexical stimuli?

Binocular disparity allows us to perceive the world in 3-dimensions through the process of stereopsis. In this study, we used binocular disparity to induce the size constancy illusion in lexical stimuli. 47 undergraduate and postgraduate students took part in a within-subjects, repeated measures design. Pairs of words were presented dichoptically using a mirror stereoscope. Results showed a significant interaction between sex, and whether an individual reported perceiving depth. Further analysis showed that in males, the size constancy effect was significantly stronger when the “further” word was presented to the upper visual field, and in females, the effect was significantly stronger when the “further” word was presented to the lower visual field. There was no effect of semantic size, nor of any other semantic variable (concreteness, imageability, semantic category) on the size constancy illusion

Binocular fusion and invariant category learning due to predictive remapping during scanning of a depthful scene with eye movements

How does the brain maintain stable fusion of 3D scenes when the eyes move? Every eye movement causes each retinal position to process a different set of scenic features, and thus the brain needs to binocularly fuse new combinations of features at each position after an eye movement. Despite these breaks in retinotopic fusion due to each movement, previously fused representations of a scene in depth often appear stable. The 3D ARTSCAN neural model proposes how the brain does this by unifying concepts about how multiple cortical areas in the What and Where cortical streams interact to coordinate processes of 3D boundary and surface perception, spatial attention, invariant object category learning, predictive remapping, eye movement control, and learned coordinate transformations. The model explains data from single neuron and psychophysical studies of covert visual attention shifts prior to eye movements. The model further clarifies how perceptual, attentional, and cognitive interactions among multiple brain regions (LGN, V1, V2, V3A, V4, MT, MST, PPC, LIP, ITp, ITa, SC) may accomplish predictive remapping as part of the process whereby view-invariant object categories are learned. These results build upon earlier neural models of 3D vision and figure-ground separation and the learning of invariant object categories as the eyes freely scan a scene. A key process concerns how an object's surface representation generates a form-fitting distribution of spatial attention, or attentional shroud, in parietal cortex that helps maintain the stability of multiple perceptual and cognitive processes. Predictive eye movement signals maintain the stability of the shroud, as well as of binocularly fused perceptual boundaries and surface representations.Published versio

Intermediate-Level Visual Representations and the Construction of Surface Perception

Visual processing has often been divided into three stages—early, intermediate, and high level vision, which roughly correspond to the sensation, perception, and cognition of the visual world. In this paper, we present a network-based model of intermediate-level vision that focuses on how surfaces might be represented in visual cortex. We propose a mechanism for representing surfaces through the establishment of “ownership”—a selective binding of contours and regions. The representation of ownership provides a central locus for visual integration. Our simulations show the ability to segment real and illusory images in a manner consistent with human perception. In addition, through ownership, other processes such as depth, transparency, and surface completion can interact with one another to organize an image into a perceptual scene