Texture Boundary Detection for Tracking Applications

Ali Shahrokni, Tom Drummond and Pascal Fua

Most of the tracking techniques used to determine the pose of an object in a sequence rely on the fact that silhouettes can be extracted using relatively simple algorithms such as background subtraction or standard edge- and gradient-based techniques. However, in practice, this rarely is the case and these silhouette extraction methods can be very brittle. They tend to fail in the presence of highly textured objects and clutter, which produce too many irrelevant edges. In such situations, it is advantageous to detect texture boundaries instead. However, because texture segmentation techniques usually require computing statistics over image patches, they are more useful for detection in a single image than for tracking.

Alternatively, we can use all the assumptions that are applicable to our tracking problem to simplify the problem a bit. More precisely we can start from the estimated projection of a 3-D object model and performs a line search in the direction perpendicular to the projected edges. This allow us to compute the most probable location of a texture boundary on the search line to which we refer to as scanline. The main idea behind scanline texture boundary detection is illustrated in Figure1 where we which to find the point on the yellow lines for which the probability of texture crossing is maximum. This is expressed in terms of the product of the conditional probabilities of pixel sequences on both side of a given point along the scanline given an estimate of the texture model at both sides. This estimate can be updated as we are going through the scanline. This concept is formalized in detail in the subsequent sections and is based on the paper by Shahrokni et. al.[1].

Figure 1: The principles of scanline boundary detection. (a) scanlines that are used to search for the texture crossing points and (b) the detected points on the boundary.

(a)	(b)