What Is Actually Affected by the Scrambling of Objects When Localizing the Lateral Occipital Complex?

The lateral occipital complex (LOC), the cortical region critical for shape perception, is localized with fMRI by its greater BOLD activity when viewing intact objects compared with their scrambled versions (resembling texture). Despite hundreds of studies investigating LOC, what the LOC localizer accomplishes—beyond distinguishing shape from texture—has never been resolved. By independently scattering the intact parts of objects, the axis structure defining the relations between parts was no longer defined. This led to a diminished BOLD response, despite the increase in the number of independent entities (the parts) produced by the scattering, thus indicating that LOC specifies interpart relations, in addition to specifying the shape of the parts themselves. LOC's sensitivity to relations is not confined to those between parts but is also readily apparent between objects, rendering it—and not subsequent “place” areas—as the critical region for the representation of scenes. Moreover, that these effects are witnessed with novel as well as familiar intact objects and scenes suggests that the relations are computed on the fly, rather than being retrieved from memory.

SHAPE FROM TEXTURE USING LOCALLY SCALED POINT PROCESSES

Shape from texture refers to the extraction of 3D information from 2D images with irregular texture. This paper introduces a statistical framework to learn shape from texture where convex texture elements in a 2D image are represented through a point process. In a first step, the 2D image is preprocessed to generate a probability map corresponding to an estimate of the unnormalized intensity of the latent point process underlying the texture elements. The latent point process is subsequently inferred from the probability map in a non-parametric, model free manner. Finally, the 3D information is extracted from the point pattern by applying a locally scaled point process model where the local scaling function represents the deformation caused by the projection of a 3D surface onto a 2D image.

GEOMETRY AND TEXTURE MEASURES FOR INTERACTIVE VIRTUALIZED REALITY INDOOR MODELER

This paper discusses the algorithm to detect the distorted textures in the virtualized reality indoor models and automatically generate the necessary 3D planes to hold the undistorted textures. Virtualized reality (VR) interactive indoor modeler, our previous contribution enables the user to interactively create their desired indoor VR model from a single 2D image. The interactive modeler uses the projective texture mapping for mapping the textures over the manually created 3D planes. If the user has not created the necessary 3D planes, then the texture that belong to various objects are projected to the available 3D planes, which leads to the presence of distorted textures. In this paper, those distorted textures are detected automatically by the suitable principles from the shape from texture research. The texture distortion features such as the slant, tilt and the curvature parameters are calculated from the 2D image by means of affine transformation measured between the neighboring texture patches within the single image. This kind of affine transform calculation from a single image is useful in the case of deficient multiple view images. The usage of superpixels in clustering the textures corresponding to different objects, reduces the modeling labor cost. A standby database also stores the repeated basic textures that are found in the indoor model, and provides texture choices for the distorted floor, wall and other regions. Finally, this paper documents the prototype implementation and experiments with the automatic 3D plane creation and distortion detection with the above mentioned principles in the virtualized reality indoor environment.