Object Recognition and Segmentation Using a Shock Graph Based Shape Model




Description

In this project, we are developing an object recognition and segmentation framework that uses a shock graph based shape model. Our fragment-based generative model is capable of generating a wide variation of shapes as instances of a given object category. In order to recognize and segment objects, we make use of a progressive selection mechanism to search among the generated shapes for the category instances that are present in the image. The search begins with a large pool of candidates identified by the dynamic programming (DP) algorithm and progressively reduces it in size by applying series of criteria.

People

Faculty:

Benjamin Kimia

Students:

Daniel Moreno %2

Alumni:

Maria Isabel Restrepo