Uma nova abordagem do método Level Set baseada em conhecimento a e priori da forma

The analysis of fluid behavior in multiphase flow is very relevant to guarantee system safety. The use of equipment to describe such behavior is subjected to factors such as the high level of investments and of specialized labor. The application of image processing techniques to flow analysis can be a good alternative, however, very little research has been developed. In this subject, this study aims at developing a new approach to image segmentation based on Level Set method that connects the active contours and prior knowledge. In order to do that, a model shape of the targeted object is trained and defined through a model of point distribution and later this model is inserted as one of the extension velocity functions for the curve evolution at zero level of level set method. The proposed approach creates a framework that consists in three terms of energy and an extension velocity function λLg(θ)+vAg(θ)+muP(0)+θf. The first three terms of the equation are the same ones introduced in (LI CHENYANG XU; FOX, 2005) and the last part of the equation θf is based on the representation of object shape proposed in this work. Two method variations are used: one restricted (Restrict Level Set - RLS) and the other with no restriction (Free Level Set - FLS). The first one is used in image segmentation that contains targets with little variation in shape and pose. The second will be used to correctly identify the shape of the bubbles in the liquid gas two phase flows. The efficiency and robustness of the approach RLS and FLS are presented in the images of the liquid gas two phase flows and in the image dataset HTZ (FERRARI et al., 2009). The results confirm the good performance of the proposed algorithm (RLS and FLS) and indicate that the approach may be used as an efficient method to validate and/or calibrate the various existing equipment used as meters for two phase flow properties, as well as in other image segmentation problems.

The relationship between early and intermediate level spatial vision during typical development and in autism spectrum disorder

Certaines recherches ont investigué le traitement visuel de bas et de plus hauts niveaux chez des personnes neurotypiques et chez des personnes ayant un trouble du spectre de l’autisme (TSA). Cependant, l’interaction développementale entre chacun de ces niveaux du traitement visuel n’est toujours pas bien comprise. La présente thèse a donc deux objectifs principaux. Le premier objectif (Étude 1) est d’évaluer l’interaction développementale entre l’analyse visuelle de bas niveaux et de niveaux intermédiaires à travers différentes périodes développementales (âge scolaire, adolescence et âge adulte). Le second objectif (Étude 2) est d’évaluer la relation fonctionnelle entre le traitement visuel de bas niveaux et de niveaux intermédiaires chez des adolescents et des adultes ayant un TSA. Ces deux objectifs ont été évalué en utilisant les mêmes stimuli et procédures. Plus précisément, la sensibilité de formes circulaires complexes (Formes de Fréquences Radiales ou FFR), définies par de la luminance ou par de la texture, a été mesurée avec une procédure à choix forcés à deux alternatives. Les résultats de la première étude ont illustré que l’information locale des FFR sous-jacents aux processus visuels de niveaux intermédiaires, affecte différemment la sensibilité à travers des périodes développementales distinctes. Plus précisément, lorsque le contour est défini par de la luminance, la performance des enfants est plus faible comparativement à celle des adolescents et des adultes pour les FFR sollicitant la perception globale. Lorsque les FFR sont définies par la texture, la sensibilité des enfants est plus faible comparativement à celle des adolescents et des adultes pour les conditions locales et globales. Par conséquent, le type d’information locale, qui définit les éléments locaux de la forme globale, influence la période à laquelle la sensibilité visuelle atteint un niveau développemental similaire à celle identifiée chez les adultes. Il est possible qu’une faible intégration visuelle entre les mécanismes de bas et de niveaux intermédiaires explique la sensibilité réduite des FFR chez les enfants. Ceci peut être attribué à des connexions descendantes et horizontales immatures ainsi qu’au sous-développement de certaines aires cérébrales du système visuel. Les résultats de la deuxième étude ont démontré que la sensibilité visuelle en autisme est influencée par la manipulation de l’information locale. Plus précisément, en présence de luminance, la sensibilité est seulement affectée pour les conditions sollicitant un traitement local chez les personnes avec un TSA. Cependant, en présence de texture, la sensibilité est réduite pour le traitement visuel global et local. Ces résultats suggèrent que la perception de formes en autisme est reliée à l’efficacité à laquelle les éléments locaux (luminance versus texture) sont traités. Les connexions latérales et ascendantes / descendantes des aires visuelles primaires sont possiblement tributaires d’un déséquilibre entre les signaux excitateurs et inhibiteurs, influençant ainsi l’efficacité à laquelle l’information visuelle de luminance et de texture est traitée en autisme. Ces résultats supportent l’hypothèse selon laquelle les altérations de la perception visuelle de bas niveaux (local) sont à l’origine des atypies de plus hauts niveaux chez les personnes avec un TSA.

Optimizing countershading camouflage

Countershading, the widespread tendency of animals to be darker on the side that receives strongest illumination, has classically been explained as an adaptation for camouflage: obliterating cues to 3D shape and enhancing background matching. However, there have only been two quantitative tests of whether the patterns observed in different species match the optimal shading to obliterate 3D cues, and no tests of whether optimal countershading actually improves concealment or survival. We use a mathematical model of the light field to predict the optimal countershading for concealment that is specific to the light environment and then test this prediction with correspondingly patterned model “caterpillars” exposed to avian predation in the field. We show that the optimal countershading is strongly illumination-dependent. A relatively sharp transition in surface patterning from dark to light is only optimal under direct solar illumination; if there is diffuse illumination from cloudy skies or shade, the pattern provides no advantage over homogeneous background-matching coloration. Conversely, a smoother gradation between dark and light is optimal under cloudy skies or shade. The demonstration of these illumination-dependent effects of different countershading patterns on predation risk strongly supports the comparative evidence showing that the type of countershading varies with light environment.

Edge enhancement improves disruptive camouflage by emphasising false edges and creating pictorial relief

Disruptive colouration is a visual camouflage composed of false edges and boundaries. Many disruptively camouflaged animals feature enhanced edges; light patches are surrounded by a lighter outline and/or a dark patches are surrounded by a darker outline. This camouflage is particularly common in amphibians, reptiles and lepidopterans. We explored the role that this pattern has in creating effective camouflage. In a visual search task utilising an ultra-large display area mimicking search tasks that might be found in nature, edge enhanced disruptive camouflage increases crypsis, even on substrates that do not provide an obvious visual match. Specifically, edge enhanced camouflage is effective on backgrounds both with and without shadows; i.e. this is not solely due to background matching of the dark edge enhancement element with the shadows. Furthermore, when the dark component of the edge enhancement is omitted the camouflage still provided better crypsis than control patterns without edge enhancement. This kind of edge enhancement improved camouflage on all background types. Lastly, we show that edge enhancement can create a perception of multiple surfaces. We conclude that edge enhancement increases the effectiveness of disruptive camouflage through mechanisms that may include the improved disruption of the object outline by implying pictorial relief.

Setting boundaries: brain dynamics of modal and amodal illusory shape completion in humans.

Normal visual perception requires differentiating foreground from background objects. Differences in physical attributes sometimes determine this relationship. Often such differences must instead be inferred, as when two objects or their parts have the same luminance. Modal completion refers to such perceptual "filling-in" of object borders that are accompanied by concurrent brightness enhancement, in turn termed illusory contours (ICs). Amodal completion is filling-in without concurrent brightness enhancement. Presently there are controversies regarding whether both completion processes use a common neural mechanism and whether perceptual filling-in is a bottom-up, feedforward process initiating at the lowest levels of the cortical visual pathway or commences at higher-tier regions. We previously examined modal completion (Murray et al., 2002) and provided evidence that the earliest modal IC sensitivity occurs within higher-tier object recognition areas of the lateral occipital complex (LOC). We further proposed that previous observations of IC sensitivity in lower-tier regions likely reflect feedback modulation from the LOC. The present study tested these proposals, examining the commonality between modal and amodal completion mechanisms with high-density electrical mapping, spatiotemporal topographic analyses, and the local autoregressive average distributed linear inverse source estimation. A common initial mechanism for both types of completion processes (140 msec) that manifested as a modulation in response strength within higher-tier visual areas, including the LOC and parietal structures, is demonstrated, whereas differential mechanisms were evident only at a subsequent time period (240 msec), with amodal completion relying on continued strong responses in these structures.

Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

Perception de la vitesse : les bases psychophysiques et neuronales

David Katz a fait l’observation que le mouvement entre la peau et l’objet est aussi important pour le sens du toucher que la lumière l’est pour la vision. Un stimulus tactile déplacé sur la peau active toutes les afférences cutanées. Les signaux résultants sont très complexes, covariant avec différents facteurs dont la vitesse, mais aussi la texture, la forme et la force. Cette thèse explore la capacité des humains à estimer la vitesse et la rugosité de surfaces en mouvements. Les bases neuronales de la vitesse tactile sont aussi étudiées en effectuant des enregistrements unitaires dans le cortex somatosensoriel primaire (S1) du singe éveillé. Dans la première expérience, nous avons montré que les sujets peuvent estimer la vitesse tactile (gamme de vitesses, 30 à 105 mm/s) de surfaces déplacées sous le doigt, et ceci sans indice de durée. Mais la structure des surfaces était essentielle (difficulté à estimer la vitesse d’une surface lisse). Les caractéristiques physiques des surfaces avaient une influence sur l’intensité subjective de la vitesse. La surface plus rugueuse (8 mm d’espacement entre les points en relief) semblait se déplacer 15% plus lentement que les surfaces moins rugueuses (de 2 et 3 mm d’espacement), pour les surfaces périodiques et non périodiques (rangées de points vs disposition aléatoire). L’effet de la texture sur la vitesse peut être réduit en un continuum monotonique quand les estimés sont normalisés avec l’espacement et présentés en fonction de la fréquence temporelle (vitesse/espacement). L'absence de changement des estimés de vitesse entre les surfaces périodiques et non périodiques suggère que les estimés de rugosité devraient aussi être indépendants de la disposition des points. Dans la deuxième expérience, et tel que prévu, une équivalence perceptuelle entre les deux séries de surfaces est obtenue quand les estimés de la rugosité sont exprimés en fonction de l'espacement moyen entre les points en relief, dans le sens de l'exploration. La troisième expérience consistait à rechercher des neurones du S1 qui pourraient expliquer l’intensité subjective de la vitesse tactile. L’hypothèse est que les neurones impliqués devraient être sensibles à la vitesse tactile (40 à 105 mm/s) et à l’espacement des points (2 à 8 mm) mais être indépendants de leur disposition (périodique vs non périodique). De plus, il est attendu que la fonction neurométrique (fréquence de décharge/espacement en fonction de la fréquence temporelle) montre une augmentation monotonique. Une grande proportion des cellules était sensible à la vitesse (76/119), et 82% d’entres elles étaient aussi sensibles à la texture. La sensibilité à la vitesse a été observée dans les trois aires du S1 (3b, 1 et 2). La grande majorité de cellules sensibles à la vitesse, 94%, avait une relation monotonique entre leur décharge et la fréquence temporelle, tel qu’attendu, et ce surtout dans les aires 1 et 2. Ces neurones pourraient donc expliquer la capacité des sujets à estimer la vitesse tactile de surfaces texturées.

A Note on Object Class Representation and Categorical Perception

We present a novel scheme ("Categorical Basis Functions", CBF) for object class representation in the brain and contrast it to the "Chorus of Prototypes" scheme recently proposed by Edelman. The power and flexibility of CBF is demonstrated in two examples. CBF is then applied to investigate the phenomenon of Categorical Perception, in particular the finding by Bulthoff et al. (1998) of categorization of faces by gender without corresponding Categorical Perception. Here, CBF makes predictions that can be tested in a psychophysical experiment. Finally, experiments are suggested to further test CBF.

Exploring Object Perception with Random Image Structure Evolution

We have developed a technique called RISE (Random Image Structure Evolution), by which one may systematically sample continuous paths in a high-dimensional image space. A basic RISE sequence depicts the evolution of an object's image from a random field, along with the reverse sequence which depicts the transformation of this image back into randomness. The processing steps are designed to ensure that important low-level image attributes such as the frequency spectrum and luminance are held constant throughout a RISE sequence. Experiments based on the RISE paradigm can be used to address some key open issues in object perception. These include determining the neural substrates underlying object perception, the role of prior knowledge and expectation in object perception, and the developmental changes in object perception skills from infancy to adulthood.

Global Depth Perception from Familiar Scene Structure

In the absence of cues for absolute depth measurements as binocular disparity, motion, or defocus, the absolute distance between the observer and a scene cannot be measured. The interpretation of shading, edges and junctions may provide a 3D model of the scene but it will not inform about the actual "size" of the space. One possible source of information for absolute depth estimation is the image size of known objects. However, this is computationally complex due to the difficulty of the object recognition process. Here we propose a source of information for absolute depth estimation that does not rely on specific objects: we introduce a procedure for absolute depth estimation based on the recognition of the whole scene. The shape of the space of the scene and the structures present in the scene are strongly related to the scale of observation. We demonstrate that, by recognizing the properties of the structures present in the image, we can infer the scale of the scene, and therefore its absolute mean depth. We illustrate the interest in computing the mean depth of the scene with application to scene recognition and object detection.

Shape complexity based on mutual information

Shape complexity has recently received attention from different fields, such as computer vision and psychology. In this paper, integral geometry and information theory tools are applied to quantify the shape complexity from two different perspectives: from the inside of the object, we evaluate its degree of structure or correlation between its surfaces (inner complexity), and from the outside, we compute its degree of interaction with the circumscribing sphere (outer complexity). Our shape complexity measures are based on the following two facts: uniformly distributed global lines crossing an object define a continuous information channel and the continuous mutual information of this channel is independent of the object discretisation and invariant to translations, rotations, and changes of scale. The measures introduced in this paper can be potentially used as shape descriptors for object recognition, image retrieval, object localisation, tumour analysis, and protein docking, among others

Reverse correlation reveals how observers sample visual information when estimating three-dimensional shape

Human observers exhibit large systematic distance-dependent biases when estimating the three-dimensional (3D) shape of objects defined by binocular image disparities. This has led some to question the utility of disparity as a cue to 3D shape and whether accurate estimation of 3D shape is at all possible. Others have argued that accurate perception is possible, but only with large continuous perspective transformations of an object. Using a stimulus that is known to elicit large distance-dependent perceptual bias (random dot stereograms of elliptical cylinders) we show that contrary to these findings the simple adoption of a more naturalistic viewing angle completely eliminates this bias. Using behavioural psychophysics, coupled with a novel surface-based reverse correlation methodology, we show that it is binocular edge and contour information that allows for accurate and precise perception and that observers actively exploit and sample this information when it is available.

Disparity-defined objects moving in depth do not elicit three-dimensional shape constancy

Observers generally fail to recover three-dimensional shape accurately from binocular disparity. Typically, depth is overestimated at near distances and underestimated at far distances [Johnston, E. B. (1991). Systematic distortions of shape from stereopsis. Vision Research, 31, 1351–1360]. A simple prediction from this is that disparity-defined objects should appear to expand in depth when moving towards the observer, and compress in depth when moving away. However, additional information is provided when an object moves from which 3D Euclidean shape can be recovered, be this through the addition of structure from motion information [Richards, W. (1985). Structure from stereo and motion. Journal of the Optical Society of America A, 2, 343–349], or the use of non-generic strategies [Todd, J. T., & Norman, J. F. (2003). The visual perception of 3-D shape from multiple cues: Are observers capable of perceiving metric structure? Perception and Psychophysics, 65, 31–47]. Here, we investigated shape constancy for objects moving in depth. We found that to be perceived as constant in shape, objects needed to contract in depth when moving toward the observer, and expand in depth when moving away, countering the effects of incorrect distance scaling (Johnston, 1991). This is a striking example of the failure of shape con- stancy, but one that is predicted if observers neither accurately estimate object distance in order to recover Euclidean shape, nor are able to base their responses on a simpler processing strategy.

Investigating human audio-visual object perception with a combination of hypothesis-generating and hypothesis-testing fMRI analysis tools

Primate multisensory object perception involves distributed brain regions. To investigate the network character of these regions of the human brain, we applied data-driven group spatial independent component analysis (ICA) to a functional magnetic resonance imaging (fMRI) data set acquired during a passive audio-visual (AV) experiment with common object stimuli. We labeled three group-level independent component (IC) maps as auditory (A), visual (V), and AV, based on their spatial layouts and activation time courses. The overlap between these IC maps served as definition of a distributed network of multisensory candidate regions including superior temporal, ventral occipito-temporal, posterior parietal and prefrontal regions. During an independent second fMRI experiment, we explicitly tested their involvement in AV integration. Activations in nine out of these twelve regions met the max-criterion (A < AV > V) for multisensory integration. Comparison of this approach with a general linear model-based region-of-interest definition revealed its complementary value for multisensory neuroimaging. In conclusion, we estimated functional networks of uni- and multisensory functional connectivity from one dataset and validated their functional roles in an independent dataset. These findings demonstrate the particular value of ICA for multisensory neuroimaging research and using independent datasets to test hypotheses generated from a data-driven analysis.