Three-dimensional shape from non-homogeneous textures: carved and stretched surfaces.

We examined the perception of 3D shape for surfaces folded, carved, or stretched out of textured materials. The textures were composed of sums of sinusoidal gratings or of circular dots, and were designed to differentiate between orientation and frequency information present in perspective images of the surfaces. Correct perception of concavities, convexities, saddles, and slants required the visibility of signature patterns of orientation modulations. These patterns were identical to those identified previously for developable surfaces (A. Li & Q. Zaidi, 2000; Q. Zaidi & L. Li, 2002), despite the fact that textures were statistically homogeneous on developable surfaces but not on carved or stretched surfaces. Frequency modulations in the image were interpreted as cues to distance from the observer, which led to weak but qualitatively correct percepts for some carved and stretched surfaces but to misperceptions for others, similar to the misperceptions for developable surfaces (A. Li & Q. Zaidi, 2003). Irrespective of whether texture on the surface is homogeneous or non-homogeneous, similar neural modules can be used to locate signature orientation modulations and thus extract shape from texture cues.

[1]  Q. Zaidi,et al.  Veridicality of three-dimensional shape perception predicted from amplitude spectra of natural textures. , 2001, Journal of the Optical Society of America. A, Optics, image science, and vision.

[2]  J. Movshon,et al.  Selectivity and spatial distribution of signals from the receptive field surround in macaque V1 neurons. , 2002, Journal of neurophysiology.

[3]  P. Lennie,et al.  Local signals from beyond the receptive fields of striate cortical neurons. , 2003, Journal of neurophysiology.

[4]  Andrea Li,et al.  Limitations on shape information provided by texture cues , 2002, Vision Research.

[5]  Andrea Li,et al.  Observer strategies in perception of 3-D shape from isotropic textures: developable surfaces , 2003, Vision Research.

[6]  A. Robert,et al.  HAUGEN, . The New Finance: The Case against Efficient Markets (Englewood Prentice Hall. , 1995 .

[7]  D Mumford,et al.  On the computational architecture of the neocortex. II. The role of cortico-cortical loops. , 1992, Biological cybernetics.

[8]  Andrea Li,et al.  Perception of three-dimensional shape from texture is based on patterns of oriented energy , 2000, Vision Research.

[9]  David A. Forsyth,et al.  Shape from Texture without Boundaries , 2002, International Journal of Computer Vision.

[10]  Gary Stix,et al.  Encoding the "Neatness" of Ones and Zeroes , 1991 .

[11]  Kent A. Stevens,et al.  The Visual Interpretation of Surface Contours , 1981, Artif. Intell..

[12]  A. Yuille Deformable Templates for Face Recognition , 1991, Journal of Cognitive Neuroscience.

[13]  R. Bracewell Two-dimensional imaging , 1994 .

[14]  Jonas Gårding,et al.  Shape from texture for smooth curved surfaces in perspective projection , 1992, Journal of Mathematical Imaging and Vision.

[15]  Jitendra Malik,et al.  Computing Local Surface Orientation and Shape from Texture for Curved Surfaces , 1997, International Journal of Computer Vision.

[16]  Tai Sing Lee,et al.  Hierarchical Bayesian inference in the visual cortex. , 2003, Journal of the Optical Society of America. A, Optics, image science, and vision.

[17]  Stéphane Mallat,et al.  The Texture Gradient Equation for Recovering Shape from Texture , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[18]  A. Torralba,et al.  Specular reflections and the perception of shape. , 2004, Journal of vision.

[19]  Paul Schrater,et al.  Shape perception reduces activity in human primary visual cortex , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[20]  D. Mumford On the computational architecture of the neocortex , 2004, Biological Cybernetics.