Robust algorithms for recognizing shape changes of deformable linear objects in video image sequences

Abstract

A new algorithm for segmenting and tracking deformable (shape-changing) linear (thin and long with negligible diameter) objects in video images is presented. The core of the algorithm is to detect the two boundary curves of a deformable linear object within adaptive tracking windows. The boundaries are found by analyzing the gray value gradients within the tracking windows, The algorithm provides a robust tracking of the shape- and width-changing object by using a fuzzy estimation of the object's membership function at the tracked points. The result of the segmentation is a list of base describing the shape of the object. This list is used to define features for shape change detection. For the feature analysis, we propose an algorithm which works well for several classes of feature curves. Experiments on robotic applications using both algorithms show promising results.