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» A probabilistic framework for image segmentation
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ICML
2001
IEEE
14 years 8 months ago
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hid...
John D. Lafferty, Andrew McCallum, Fernando C. N. ...
DAGM
1995
Springer
13 years 11 months ago
Primary Image Segmentation
: This paper introduces the notion of primary image segmentation which serves as a well defined link between low- and high-level image analysis. A general algorithmic framework bas...
Ullrich Köthe
ICPR
2008
IEEE
14 years 9 months ago
On segmentation evaluation metrics and region counts
Five image segmentation algorithms are evaluated: mean shift, normalised cuts, efficient graph-based segmentation, hierarchical watershed, and waterfall. The evaluation is done us...
Allan Hanbury, Julian Stöttinger
ICIP
2008
IEEE
14 years 9 months ago
Implicit spatial inference with sparse local features
This paper introduces a novel way to leverage the implicit geometry of sparse local features (e.g. SIFT operator) for the purposes of object detection and segmentation. A two-clas...
Deirdre O'Regan, Anil C. Kokaram
IPMI
2005
Springer
14 years 1 months ago
A Generalized Level Set Formulation of the Mumford-Shah Functional for Brain MR Image Segmentation
Brain MR image segmentation is an important research topic in medical image analysis area. In this paper, we propose an active contour model for brain MR image segmentation, based ...
Lishui Cheng, Jie Yang, Xian Fan, Yuemin Zhu