scholarly journals Delaunay triangulation-based pit density estimation for the classification of polyps in high-magnification chromo-colonoscopy

2012 ◽  
Vol 107 (3) ◽  
pp. 565-581 ◽  
Author(s):  
M. Häfner ◽  
M. Liedlgruber ◽  
A. Uhl ◽  
A. Vécsei ◽  
F. Wrba
Keyword(s):  
Density Estimation ◽  
Delaunay Triangulation ◽  
High Magnification

scholarly journals Directional Kernel Density Estimation for Classification of Breast Tissue Spectra

2017 ◽  
Vol 36 (1) ◽  
pp. 64-73 ◽  
Author(s):  
Arturo Pardo ◽  
Eusebio Real ◽  
Venkat Krishnaswamy ◽  
Jose Miguel Lopez-Higuera ◽  
Brian W. Pogue ◽  
...  
Keyword(s):  
Density Estimation ◽  
Kernel Density Estimation ◽  
Breast Tissue ◽  
Kernel Density

Classification of Extended Delaunay Triangulation for Fingerprint Indexing

2014 ◽  
Author(s):  
Ilaiah Kavati ◽  
VamshiKrishna Chenna ◽  
Munaga V.N.K. Prasad ◽  
Chakravarthy Bhagvati
Keyword(s):  
Delaunay Triangulation ◽  
Fingerprint Indexing

scholarly journals Multivariate mixed kernel density estimators and their application in machine learning for classification of biological objects based on spectral measurements

2019 ◽  
Vol 43 (4) ◽  
pp. 677-691
Author(s):  
A.A. Sirota ◽  
A.O. Donskikh ◽  
A.V. Akimov ◽  
D.A. Minakov
Keyword(s):  
Machine Learning ◽  
Density Estimation ◽  
Data Augmentation ◽  
Kernel Density ◽  
Machine Learning Algorithms ◽  
Spectral Measurements ◽  
Density Estimates ◽  
Biological Objects ◽  
Density Estimators

A problem of non-parametric multivariate density estimation for machine learning and data augmentation is considered. A new mixed density estimation method based on calculating the convolution of independently obtained kernel density estimates for unknown distributions of informative features and a known (or independently estimated) density for non-informative interference occurring during measurements is proposed. Properties of the mixed density estimates obtained using this method are analyzed. The method is compared with a conventional Parzen-Rosenblatt window method applied directly to the training data. The equivalence of the mixed kernel density estimator and the data augmentation procedure based on the known (or estimated) statistical model of interference is theoretically and experimentally proven. The applicability of the mixed density estimators for training of machine learning algorithms for the classification of biological objects (elements of grain mixtures) based on spectral measurements in the visible and near-infrared regions is evaluated.

Classification of Endoscopic Images Using Delaunay Triangulation-Based Edge Features

2010 ◽  
pp. 131-140 ◽  
Author(s):  
M. Häfner ◽  
A. Gangl ◽  
M. Liedlgruber ◽  
Andreas Uhl ◽  
A. Vécsei ◽  
...  
Keyword(s):  
Delaunay Triangulation ◽  
Endoscopic Images ◽  
Edge Features
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