Mid-Level Vision and Recognition of Non-Rigid Objects
J. Brian Subirana-Vilanova · DSpace@MIT (Massachusetts Institute of Technology) · 1993
In this dissertation I address the problem of visual recognition of non-rigid objects. I introduce the frame alignment approach to recognition and illustrate it in two types of non-rigid objects: contour textures and elongated flexible objects. Frame alignment is based on matching stored models to images and has three stages: first, a "frame curve" and a corresponding object are computed in the image. Second, the object is brought into correspondence with the model by aligning the model axis with the object axis; if the object is not rigid it is "unbent" achieving a canonical description for recognition. Finally, object and model are matched against each other. Rigid and elongated flexible objects are matched using all contour information. Contour textures are matched using filter outputs around the frame curve. The central contribution of this thesis is Curved Inertia Frames (C.I.F.), a scheme for computing frame curves directly on the image. C.I.F. is the first algorithm which can c...