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research:texture [2010/06/10 15:13]
rosenholtz
research:texture [2010/06/10 15:14] (current)
rosenholtz
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-=== It's Not Just for Segmentation and Shape from Texture ===+===== It's Not Just for Segmentation and Shape from Texture =====
In the past, texture perception has largely been studied because changes in texture can signal either a boundary between objects (texture segmentation) or a change in orientation of a surface (shape from texture).  While we have studied these problems, we also believe that texture perception gives us insight into a broader class of visual phenomena, including visual crowding, visual search, object recognition, and set perception. In the past, texture perception has largely been studied because changes in texture can signal either a boundary between objects (texture segmentation) or a change in orientation of a surface (shape from texture).  While we have studied these problems, we also believe that texture perception gives us insight into a broader class of visual phenomena, including visual crowding, visual search, object recognition, and set perception.
-=== Understanding Texture Perception is Critical for Understanding Visual Crowding, Visual Search, and Perhaps Perception in General ===+===== Understanding Texture Perception is Critical for Understanding Visual Crowding, Visual Search, and Perhaps Perception in General =====
(More detail, and pictures, to follow shortly.  Work done in conjunction with [[:people:benjamin_balas|Benjamin Balas]], [[:people:alvin_raj|Alvin Raj]], Lisa Nakano, [[:people:livia_ilie|Livia Ilie]], Ronald van den Berg, and Stephanie Chan.) (More detail, and pictures, to follow shortly.  Work done in conjunction with [[:people:benjamin_balas|Benjamin Balas]], [[:people:alvin_raj|Alvin Raj]], Lisa Nakano, [[:people:livia_ilie|Livia Ilie]], Ronald van den Berg, and Stephanie Chan.)
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Furthermore, if this texture representation is all that is available in the periphery, which is used to plan future eye movements, then perhaps the top-down information we can use to guide search for a target is limited by a texture representation of the target.  Early results show that this is predictive of search difficulty in real world scenes. Furthermore, if this texture representation is all that is available in the periphery, which is used to plan future eye movements, then perhaps the top-down information we can use to guide search for a target is limited by a texture representation of the target.  Early results show that this is predictive of search difficulty in real world scenes.
-=== Set Perception ===+===== Set Perception =====
(Work done in conjunction with George Alvarez.) (Work done in conjunction with George Alvarez.)
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We demonstrate that modeling is critical for understanding set perception. To aid predictions and experimental design to further explore set perception, we make modeling code available at http://dspace.mit.edu/handle/1721.1/7508 We demonstrate that modeling is critical for understanding set perception. To aid predictions and experimental design to further explore set perception, we make modeling code available at http://dspace.mit.edu/handle/1721.1/7508
-=== Texture Segmentation ===+===== Texture Segmentation =====
Is the human visual system designed to compute statistical tests like t-tests?  Experiments and modeling in our lab show that this is a good model of a range of “pre-attentive” texture segmentation results.  In this model, the visual system first extracts the equivalent of mean and variance of various features like orientation on each side of a candidate texture boundary.  The variance includes internal noise in the feature estimates, which may depend upon the viewing time as well as the expertise of the observer.  The size of the area over which these statistics are computed seems more inherent to the system, as we have found it varies little between observers.  The observer then detects a texture boundary if the equivalent of a t-test reveals a “significant” difference between the samples on the two sides of the boundary. Is the human visual system designed to compute statistical tests like t-tests?  Experiments and modeling in our lab show that this is a good model of a range of “pre-attentive” texture segmentation results.  In this model, the visual system first extracts the equivalent of mean and variance of various features like orientation on each side of a candidate texture boundary.  The variance includes internal noise in the feature estimates, which may depend upon the viewing time as well as the expertise of the observer.  The size of the area over which these statistics are computed seems more inherent to the system, as we have found it varies little between observers.  The observer then detects a texture boundary if the equivalent of a t-test reveals a “significant” difference between the samples on the two sides of the boundary.
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    * R. Rosenholtz, "Significantly different textures: A computational model of pre-attentive texture segmentation."  Proc. European Conference on Computer Vision, D. Vernon (Ed.), Springer Verlag, LNCS 1843, Dublin, Ireland, pp. 197-211, June 2000.     * R. Rosenholtz, "Significantly different textures: A computational model of pre-attentive texture segmentation."  Proc. European Conference on Computer Vision, D. Vernon (Ed.), Springer Verlag, LNCS 1843, Dublin, Ireland, pp. 197-211, June 2000.
-=== Shape-From-Texture ===+===== Shape-From-Texture =====
An image of a regularly textured surface will contain texture elements of irregular size and shape.  Texture that is farther away will appear smaller and more dense, and the shape of the texture elements will vary with the orientation and curvature of the textured surface.  We model this change in texture appearance, locally, by an affine transformation, and relate the affine transformations between a patch and its neighbors to the orientation and curvature of the local surface. An image of a regularly textured surface will contain texture elements of irregular size and shape.  Texture that is farther away will appear smaller and more dense, and the shape of the texture elements will vary with the orientation and curvature of the textured surface.  We model this change in texture appearance, locally, by an affine transformation, and relate the affine transformations between a patch and its neighbors to the orientation and curvature of the local surface.
 
research/texture.1276197196.txt.gz · Last modified: 2010/06/10 15:13 by rosenholtz