@inproceedings{b5d577d724d647779773ee06958c0d59,
title = "A learning algorithm for model based object detection",
abstract = "Detecting objects in images and videos is a difficult task that has challenged the field of computer vision. Most of the algorithms for object detection are sensitive to background clutter and occlusion, and cannot localize the edge of the object. An objects shape is typically the most discriminative cue for its recognition by humans. This paper introduces a model based object detection method which uses only shape-fragment features. The object shape model is learned from a very small set of training images. And the object model is composed of shape fragments. The model of the object is in multi-scales. The results presented in this paper are competitive with other state-of-the-art object detection methods. The major contributions of this paper are the application of learned shape fragments based model for object detection in complex environment and a novel two-stage object detection framework.",
keywords = "Object detection, image segmentation, shape fragment, shape matching",
author = "Guodong Chen and Zeyang Xia and Rongchuan Sun and Zhenhua Wang and Zhiwu Ren and Lining Sun",
year = "2011",
doi = "10.1109/URAI.2011.6145941",
language = "英语",
isbn = "9781457707223",
series = "URAI 2011 - 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence",
pages = "101--106",
booktitle = "URAI 2011 - 2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence",
note = "2011 8th International Conference on Ubiquitous Robots and Ambient Intelligence, URAI 2011 ; Conference date: 23-11-2011 Through 26-11-2011",
}