INTEGRATION OF TEXTURE ELEMENT DETECTION AND SURFACE SHAPE ESTIMATION FROM TEXTURE

DOROTHEA BLOSTEIN
NARENDRA AHUJA

OBJECTIVE
Given a slanted view of a planar, homogeneously textured surface, estimate the surface slant from the image texture gradient. 

APPROACH
(1) Identification of image texture elements (texels) that correspond to surface texture elements is itself a significant problem since the scale at which surface detail is captured varies continuously with the three-dimensional distance, and therefore across the image texture. The image texels may exhibit a systematic variation in a priori unknown properties, e. g., size, density or contrast. All regions are potential texels. Consequently, all regions, of all sizes and contrasts, are detected at each location and treated as candidate texels.

(2) The estimation of surface slope (slant and tilt) is integrated with the process of selecting texels from among the large number of detected regions. For any given slant and tilt, only those regions across the image are interpreted as texels whose properties, e. g., area distribution, match the spatial distribution predicted by the hypothesized slant and tilt, and which occupy the largest fraction of the image. The image area is used as a measure of  the extent of support for the particular slant-tilt pair. 

(3) All possible slant-tilt values are considered as hypotheses, and a search is conducted to find the hypothesis with most support. This is the estimated surface orientation.

RESULTS
Several real life texture images are shown below. For each, estimated values of the slant-tilt angle pairs are depicted graphically by showing how a hypothetical surface containing fixed size disks would look when viewed at the estimated slant and tilt. Thus, a visual comparison of the original texture image and its graphical depiction can be made to obtain a quick assessment of the quality of the derived estimates.

Each of the following figures consists of four parts: 

1. The original texture image, 

2. A set of bright regions detected at multiple scales and as candidate texels, 

3. The estimated surface slant and tilt values shown graphically, and 

4. The subset of bright regions supporting the estimates surface orientation, i.e., the estimated image texels, shown superposed on the original image.

   a. Original	b. All regions	c. Slant tilt estimates	d. Detected texels