Shadows and shading flow fields

Breton, P.; Zucker, Steven W.

doi:10.1109/cvpr.1996.517161

Cited by 53 publications

(37 citation statements)

References 8 publications

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“…Intuitively, this theory makes sense because the relationship between 2D image properties and 3D surface properties is different for different cues. However, we and others have previously suggested that measurements similar to the ones proposed here likely play a role in the estimation of shape from shading and specular (mirror-like) reflections (26)(27)(28)(29). This theory suggests that at least for the early stages of processing, texture, shading, specular reflections, and possibly some other cues could share more in common than previously thought.…”

Section: Discussionmentioning

confidence: 52%

“…Although additional processing would be required to regularize the orientation field and to derive a complete estimate of the 3D shape from these measurements, the correspondence between the outputs of the filters (which measure local 2D image structure) and the true 3D surface orientations is surprisingly good. We, and others, have argued previously that orientation fields could play an important role in the estimation of shape from shading and specular reflections (26)(27)(28)(29). Here, we suggest that similar mechanisms could also play a role in the estimation of shape from texture.…”

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confidence: 65%

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Estimation of 3D shape from image orientations

Fleming

Holtmann-Rice

Bülthoff

2011

Proc. Natl. Acad. Sci. U.S.A.

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One of the main functions of vision is to estimate the 3D shape of objects in our environment. Many different visual cues, such as stereopsis, motion parallax, and shading, are thought to be involved. One important cue that remains poorly understood comes from surface texture markings. When a textured surface is slanted in 3D relative to the observer, the surface patterns appear compressed in the retinal image, providing potentially important information about 3D shape. What is not known, however, is how the brain actually measures this information from the retinal image. Here, we explain how the key information could be extracted by populations of cells tuned to different orientations and spatial frequencies, like those found in the primary visual cortex. To test this theory, we created stimuli that selectively stimulate such cell populations, by "smearing" (filtering) images of 2D random noise into specific oriented patterns. We find that the resulting patterns appear vividly 3D, and that increasing the strength of the orientation signals progressively increases the sense of 3D shape, even though the filtering we apply is physically inconsistent with what would occur with a real object. This finding suggests we have isolated key mechanisms used by the brain to estimate shape from texture. Crucially, we also find that adapting the visual system's orientation detectors to orthogonal patterns causes unoriented random noise to look like a specific 3D shape. Together these findings demonstrate a crucial role of orientation detectors in the perception of 3D shape.shape perception | surface perception | orientation field | complex cells W hen we look at a textured object, the projection of the surface markings into the retinal image compresses them in ways that can indicate the object's 3D shape. This compression has two distinct causes. The first cause is distance-dependent: when a surface patch is moved further away from the eye, the texture shrinks isotropically in the image as a function of the distance. The second cause of compression is foreshortening: when a surface is slanted relative to the line of sight, the texture is anisotropically compressed along the direction of the slant, with greater slant leading to greater compression.It is well known that the visual system can use these texture compression cues to estimate 3D shape (1-9). What is not known, however, is how the visual system measures the compression at each point in the image. A crucial stage in any theory of 3D vision must include an explanation of how the visual system extracts the key information from the image. At present, there is an explanatory gap between the known response properties of cells early in the visual processing hierarchy, which measure local 2D image features (10-16), and cells higher in the processing stream, which respond to various 3D shape properties (17-24). How does the brain put the measurements made in the primary visual cortex (V1) to good use to arrive at an estimate of 3D shape?Estimating the extent and direction of ...

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Section: Discussionmentioning

confidence: 52%

mentioning

confidence: 65%

Estimation of 3D shape from image orientations

Fleming

Holtmann-Rice

Bülthoff

2011

Proc. Natl. Acad. Sci. U.S.A.

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“…Furthermore, orientation is explicit not only in ODTs but also in motion and optical flows, and therefore the theory developed in this article is directly applicable to motion-based segregation. More importantly, orientation is implicit in other visual cues that take part in the segregation process, in particular, shading (23) and color (24). Therefore, the results presented here imply that the role of these other cues in early vision and segregation should be revisited.…”

Section: Discussionmentioning

confidence: 98%

Visual saliency and texture segregation without feature gradient

Ben-Shahar

2006

Proc. Natl. Acad. Sci. U.S.A.

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A central notion in the study of texture segregation is that of feature gradient (or feature contrast). In orientation-based texture segregation, orientation gradients have indeed played a fundamental role in explaining behavioral results. Here, however, we show that general, smoothly varying, orientation-defined textures (ODTs) exhibit striking perceptual singularities that are completely unpredictable from orientation gradients. These singularities defy not only popular texture segregation theories but also virtually all computational segmentation methods, and they confound previous behavioral studies with smoothly varying ODTs. We provide psychophysical evidence that perceptual singularities in smooth ODTs are salient visual features consistent across observers and with significant effect on the perception and segregation of oriented textures. We further show that, although orientation gradients cannot predict them, perceptual singularities in smooth ODTs emerge directly from, and can be spatially localized by, two ODT curvatures. Given the traditional role of feature gradients in early vision, the significance of these findings extends well beyond orientation-based texture segregation to issues ranging from curve integration and fragment grouping, through the perception of 3D shape, to the functional organization of the primary visual cortex.curvature ͉ perceptual organization ͉ perceptual singularity measure

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“…The curvature estimator in (17) is the tangential or isophote curvature. The normal (or gradient flow line) curvature [17] can be computed by exchanging the v and w coordinates in (12) and (13).…”

Section: Ymentioning

confidence: 99%

“…For noisy oriented patterns, segmentation fails, making these methods useless. The isophote (tangential) curvature (the second derivative along the isophote divided by the gradient magnitude) and the normal curvature [17] are segmentation-free [7], [8], [17], but also fail on these images. There are three reasons for this [5]: 1) The gradient is zero on ridges and in valleys.…”

Section: Introductionmentioning

confidence: 99%

Curvature estimation in oriented patterns using curvilinear models applied to gradient vector fields

Weijer¹,

Vliet

Verbeek

et al. 2001

IEEE Trans. Pattern Anal. Machine Intell.

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AbstractÐCurved oriented patterns are dominated by high frequencies and exhibit zero gradients on ridges and valleys. Existing curvature estimators fail here. The characterization of curved oriented patterns based on translation invariance lacks an estimation of local curvature and yields a biased curvaturedependent confidence measure. Using parameterized curvilinear models we measure the amount of local gradient energy along the model gradient as a function of model curvature. Minimizing the residual energy yields a closed-form solution for the local curvature estimate and the corresponding confidence measure. We show that simple curvilinear models are applicable in the analysis of a wide variety of curved oriented patterns.

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Shadows and shading flow fields

Cited by 53 publications

References 8 publications

Estimation of 3D shape from image orientations

Estimation of 3D shape from image orientations

Visual saliency and texture segregation without feature gradient

Curvature estimation in oriented patterns using curvilinear models applied to gradient vector fields

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