Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Jake Porway"'
Publikováno v:
Pattern Recognition. 42:1297-1307
This paper illustrates a hierarchical generative model for representing and recognizing compositional object categories with large intra-category variance. In this model, objects are broken into their constituent parts and the variability of configur
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::57056711130c225b9706bb048234c4ea
https://doi.org/10.1016/j.patcog.2008.10.033
https://doi.org/10.1016/j.patcog.2008.10.033
Autor:
Hieu Nguyen, Jim Utt, Anurag Ganguli, Prakash Ramu, Jake Porway, Bonnie Schwartz, Joseph Yadegar
Publikováno v:
AIAA Infotech@Aerospace 2010.
Unmanned Aircraft Systems (UAS) are becoming indispensable tools for a huge number of aeronautic tasks, such as data collection and wide area surveillance. Currently, however, UAS face limitations on their utilization in civil airspace because they h
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7147c7a914c192fa81e16ee646502ec5
https://doi.org/10.2514/6.2010-3338
https://doi.org/10.2514/6.2010-3338
In this chapter we present a method for learning a compositional model in a minimax entropy framework for modeling object categories with large intra-class variance. The model we learn incorporates the flexibility of a stochastic context free grammar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe644fb57658ed64fb72e8c6a9d842c0
https://doi.org/10.1017/cbo9780511635465.014
https://doi.org/10.1017/cbo9780511635465.014
Publikováno v:
CVPR
In this paper we present a novel method for parsing aerial images with a hierarchical and contextual model learned in a statistical framework. We learn hierarchies at the scene and object levels to handle the difficult task of representing scene elem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8533eb015a40e5b1b3e8708ddeec1570
https://doi.org/10.1109/cvpr.2008.4587359
https://doi.org/10.1109/cvpr.2008.4587359
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783540741954
EMMCVPR
EMMCVPR
In this paper, we present a framework for object categorization via sketch graphs, structures that incorporate shape and structure information. In this framework, we integrate the learnable And-Or graph model, a hierarchical structure that combines t
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::976a1dc09b1549f9b284075fcf38d51e
https://doi.org/10.1007/978-3-540-74198-5_16
https://doi.org/10.1007/978-3-540-74198-5_16
Publikováno v:
ICCV
In this paper we present an empirical study of object category recognition using generalized samples and a set of sequential tests. We study 33 categories, each consisting of a small data set of 30 instances. To increase the amount of training data w
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ebd338cadb28a0dac6e96a923f679169
https://doi.org/10.1109/iccv.2007.4408873
https://doi.org/10.1109/iccv.2007.4408873
Publikováno v:
Porway, Jake; Wang, Qiongchen; & Zhu, Song Chun. (2010). A Hierarchical and Contextual Model for Aerial Image Parsing. International Journal of Computer Vision, 88(2), pp 254-283. doi: 10.1007/s11263-009-0306-1. Retrieved from: http://www.escholarship.org/uc/item/2t7919dw
In this paper we present a hierarchical and contextual model for aerial image understanding. Our model organizes objects (cars, roofs, roads, trees, parking lots) in aerial scenes into hierarchical groups whose appearances and configurations are dete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::937053d71b352cd744f53111a62cf439