By Topic

Implicit Sparse Shape Representation: A Unified Framework for Object Segmentation and Recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
J. Yao ; J. Yao is with the Laboratory of State Key Discipline of Communication and Information System, Department of Information Science and Electronic Engineering, Zhejiang University, Hang Zhou, CO 310027 China.(email:yao.jincao@yahoo.cn) ; H. Yu

Given a classified probabilistic shape dictionary, and an image with a shape similar to some of the elements in the dictionary, this letter introduces a sparse representation based framework with a twofold goal. First, to select a sparse shape combination from the dictionary that best represents the shape, and second, to accurately segment the image taking into account both the sparse shape combination and the image information. A new energy function that combines the region-based segmentation with sparse representation is introduced to accomplish both goals simultaneously. The experimental results show the superior segmentation and recognition capabilities of the proposed model.

Published in:

IEEE Signal Processing Letters  (Volume:PP ,  Issue: 99 )