Unsupervised learning by probabilistic latent semantic analysis
Machine Learning
Fast Approximate Energy Minimization via Graph Cuts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discovering Objects and their Localization in Images
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 - Volume 01
International Journal of Computer Vision
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Learning to Combine Bottom-Up and Top-Down Segmentation
International Journal of Computer Vision
Non-homogeneous Conditional Random Fields for Contextual Image Segmentation
ISM '08 Proceedings of the 2008 Tenth IEEE International Symposium on Multimedia
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part II
Segmentation of microorganism in complex environments
Pattern Recognition and Image Analysis
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Cell segmentation is a challenging problem in histology and cytology that can benefit from additional information obtained in using multispectral imaging. Unique transmission spectra of biological tissues are potentially useful for better classification and segmentation of sub-cellular structures. In this paper, we propose a conditional random (ield (CRF) model that interprets high-dimensional spectral data during inference and pixel labeling. High quality segmentations are computed by combining low-level cues and high-level contextual information extracted by unsupervised topic discovery. Comparative analysis of the proposed model against the commonly used 2-D CRF model in color space is also performed. Results of this evaluation show the benefits of our proposed model.