Example Based Non-rigid Shape Detection

Since it is hard to handcraft the prior knowledge in a shape detection framework, machine learning methods are preferred to exploit the expert annotation of the target shape in a database. In the previous approaches [1, 2], an optimal similarity transformation is exhaustively searched for to maximize the response of a trained classification model. At best, these approaches only give a rough estimate of the position of a non-rigid shape. In this paper, we propose a novel machine learning based approach to achieve a refined shape detection result. We train a model that has the largest response on a reference shape and a smaller response on other shapes. During shape detection, we search for an optimal nonrigid deformation to maximize the response of the trained model on the deformed image block. Since exhaustive searching is inapplicable for a non-rigid deformation space with a high dimension, currently, example based searching is used instead. Experiments on two applications, left ventr...

• Type: Conference
• Year: 2006
• Where: ECCV
• Authors: Yefeng Zheng, Xiang Sean Zhou, Bogdan Georgescu, Shaohua Kevin Zhou, Dorin Comaniciu
• Keywords: Computer Vision, ECCV 2006, Non-rigid Deformation Space, Non-rigid Shape, Reference Shape, Shape Detection Framework, Target Shape