Paper Info
Title
Segmentation Of Objects In Temporal Images Using The Hidden Markov Model
Abstract
Automatic segmentation of objects in images is an ongoing research problem with applications in many fields. If a scene is imaged serially over time, an advantage can be gained by using segmentation results from previous and subsequent images when segmenting the current image. This paper discusses a probabilistic framework for making use of temporal information in the segmentation process. A subset of Dynamic Bayesian Networks, the Hidden Markov Model is described as a means to improve segmentation over statistical classification techniques that use static pixel intensity information alone. An application of this technique to the segmentation of tumors in magnetic resonance images (MRIs) is described. The segmentation accuracy was increased compared to a popular 3D spatial only segmentation method.
Year
DOI
Venue
2005
10.1109/ICIP.2005.1529672
2005 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), VOLS 1-5
Keywords
Field
DocType
magnetic resonance image,probability,image resolution,hidden markov model,hidden markov models,image segmentation,dynamic bayesian network
Computer vision,Scale-space segmentation,Pattern recognition,Computer science,Segmentation,Segmentation-based object categorization,Image segmentation,Artificial intelligence,Region growing,Hidden Markov model,Minimum spanning tree-based segmentation,Dynamic Bayesian network
Conference
ISSN
Citations 
PageRank 
1522-4880
0
0.34
References 
Authors
3
3
Name
Order
Citations
PageRank
Jeffrey Solomon1234.37
John A. Butman25211.18
Arun Sood312429.36