161,422 research outputs found

    Perception Driven Texture Generation

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    This paper investigates a novel task of generating texture images from perceptual descriptions. Previous work on texture generation focused on either synthesis from examples or generation from procedural models. Generating textures from perceptual attributes have not been well studied yet. Meanwhile, perceptual attributes, such as directionality, regularity and roughness are important factors for human observers to describe a texture. In this paper, we propose a joint deep network model that combines adversarial training and perceptual feature regression for texture generation, while only random noise and user-defined perceptual attributes are required as input. In this model, a preliminary trained convolutional neural network is essentially integrated with the adversarial framework, which can drive the generated textures to possess given perceptual attributes. An important aspect of the proposed model is that, if we change one of the input perceptual features, the corresponding appearance of the generated textures will also be changed. We design several experiments to validate the effectiveness of the proposed method. The results show that the proposed method can produce high quality texture images with desired perceptual properties.Comment: 7 pages, 4 figures, icme201

    Visual Learning In The Perception Of Texture: Simple And Contingent Aftereffects Of Texture Density

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    Novel results elucidating the magnitude, binocularity and retinotopicity of aftereffects of visual texture density adaptation are reported as is a new contingent aftereffect of texture density which suggests that the perception of visual texture density is quite malleable. Texture aftereffects contingent upon orientation, color and temporal sequence are discussed. A fourth effect is demonstrated in which auditory contingencies are shown to produce a different kind of visual distortion. The merits and limitations of error-correction and classical conditioning theories of contingent adaptation are reviewed. It is argued that a third kind of theory which emphasizes coding efficiency and informational considerations merits close attention. It is proposed that malleability in the registration of texture information can be understood as part of the functional adaptability of perception

    The Objectivity Of Touch

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    Assumption: Sensory modalities are individuated by their proper objects. Hearing is the direct perception of sounds, sight the direct perception of colours, etc. Objection: There is no single type of proper sensibles in the case of touch (temperature, solidity, hardness, humidity, texture, weight, vibration...). Answer : 1. accept to distinguish the sense of pressure (touch strictly speaking) from the sense of temperature. 2. argue that pressures are the direct perceptual objects through which one perceives weight, texture, solidity

    Complexity, rate, and scale in sliding friction dynamics between a finger and textured surface.

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    Sliding friction between the skin and a touched surface is highly complex, but lies at the heart of our ability to discriminate surface texture through touch. Prior research has elucidated neural mechanisms of tactile texture perception, but our understanding of the nonlinear dynamics of frictional sliding between the finger and textured surfaces, with which the neural signals that encode texture originate, is incomplete. To address this, we compared measurements from human fingertips sliding against textured counter surfaces with predictions of numerical simulations of a model finger that resembled a real finger, with similar geometry, tissue heterogeneity, hyperelasticity, and interfacial adhesion. Modeled and measured forces exhibited similar complex, nonlinear sliding friction dynamics, force fluctuations, and prominent regularities related to the surface geometry. We comparatively analysed measured and simulated forces patterns in matched conditions using linear and nonlinear methods, including recurrence analysis. The model had greatest predictive power for faster sliding and for surface textures with length scales greater than about one millimeter. This could be attributed to the the tendency of sliding at slower speeds, or on finer surfaces, to complexly engage fine features of skin or surface, such as fingerprints or surface asperities. The results elucidate the dynamical forces felt during tactile exploration and highlight the challenges involved in the biological perception of surface texture via touch

    Preattentive texture discrimination with early vision mechanisms

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    We present a model of human preattentive texture perception. This model consists of three stages: (1) convolution of the image with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses modeling outputs of V1 simple cells, (2) inhibition, localized in space, within and among the neural-response profiles that results in the suppression of weak responses when there are strong responses at the same or nearby locations, and (3) texture-boundary detection by using wide odd-symmetric mechanisms. Our model can predict the salience of texture boundaries in any arbitrary gray-scale image. A computer implementation of this model has been tested on many of the classic stimuli from psychophysical literature. Quantitative predictions of the degree of discriminability of different texture pairs match well with experimental measurements of discriminability in human observers

    Haptic perception of virtual roughness

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    The texture of a virtual surface can both increase the sense of realism of an object as well as convey information about object identity, type, location, function, and so on. It is crucial therefore that interface designers know the range of textural information available through the haptic modality in virtual environments. The current study involves participants making roughness judgments on pairs of haptic textures experienced through a force-feedback device. The effect of texture frequency on roughness perception is analysed. The potential range and resolution of textural information available through force-feedback interaction are discussed

    A computational model of texture segmentation

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    An algorithm for finding texture boundaries in images is developed on the basis of a computational model of human texture perception. The model consists of three stages: (1) the image is convolved with a bank of even-symmetric linear filters followed by half-wave rectification to give a set of responses; (2) inhibition, localized in space, within and among the neural response profiles results in the suppression of weak responses when there are strong responses at the same or nearby locations; and (3) texture boundaries are detected using peaks in the gradients of the inhibited response profiles. The model is precisely specified, equally applicable to grey-scale and binary textures, and is motivated by detailed comparison with psychophysics and physiology. It makes predictions about the degree of discriminability of different texture pairs which match very well with experimental measurements of discriminability in human observers. From a machine-vision point of view, the scheme is a high-quality texture-edge detector which works equally on images of artificial and natural scenes. The algorithm makes the use of simple local and parallel operations, which makes it potentially real-time
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