Multi-stage Contour Based Detection of Deformable Objects

We present an efficient multi stage approach to detection of deformable objects in real, cluttered images given a single or few hand drawn examples as models. The method handles deformations of the object by first breaking the given model into segments at high curvature points. We allow bending at these points as it has been studied that deformation typically happens at high curvature points. The broken segments are then scaled, rotated, deformed and searched independently in the gradient image. Point maps are generated for each segment that represent the locations of the matches for that segment. We then group k points from the point maps of k adjacent segments using a cost function that takes into account local scale variations as well as inter-segment orientations. These matched groups yield plausible locations for the objects. In the fine matching stage, the entire object contour in the localized regions is built from the k-segment groups and given a comprehensive score in a method...

• Type: Conference
• Year: 2008
• Where: ECCV
• Authors: Saiprasad Ravishankar, Arpit Jain, Anurag Mittal
• Keywords: Adjacent Segments, Broken Segments, Computer Vision, Considerable Object Deformations, ECCV 2008, Entire Object Contour, Method Handles Deformations