Automatic Hierarchical Object Decomposition for Object Recognition
Markus Ulrich, Albert Baumgartner, Carsten T. Steger · 2002
Industrial applications of 2D object recognition such as quality control often demand robustness, highest accuracy, and real-time computation from the object recognition approach. Simultaneously fulfilling all of these demands is a hard problem and has recently drawn considerable attention within the research community of close-range photogrammetry and computer vision. The problem is complicated when dealing with objects or models consisting of several rigid parts that are allowed to move with respect to each other. In this situation, approaches searching for rigid objects fail since the appearance of the model may substantially change under the variations caused by the movements. In this paper, an approach is proposed that not only facilitates the recognition of such partsbased models but also fulfills the above demands. The object is automatically decomposed into single rigid parts based on several example images that express the movements of the object parts. The mutual movements between the parts are analyzed and represented in a graph structure. Based on the graph, a hierarchical model is derived that minimizes the search effort during a subsequent recognition of the object in an arbitrary image.