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Ilya Levner
Researcher at University of Alberta
Publications - 21
Citations - 461
Ilya Levner is an academic researcher from University of Alberta. The author has contributed to research in topics: Feature extraction & Image segmentation. The author has an hindex of 9, co-authored 20 publications receiving 426 citations.
Papers
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Journal ArticleDOI
Feature selection and nearest centroid classification for protein mass spectrometry
TL;DR: This study tested a number of popular feature selection methods using the nearest centroid classifier and found that several reportedly state-of-the-art algorithms in fact perform rather poorly when tested via stratified cross-validation, providing clear evidence that algorithm evaluation should be performed on several data sets using a consistent cross- validation procedure in order for the conclusions to be statistically sound.
Journal ArticleDOI
Classification-Driven Watershed Segmentation
Ilya Levner,Hong Zhang +1 more
TL;DR: This paper presents a novel approach for creation of topographical function and object markers used within watershed segmentation that uses two classifiers, one trained to produce markers, the other training to produce object boundaries.
Journal ArticleDOI
Ore image segmentation by learning image and shape features
TL;DR: An image segmentation system specifically targeted for oil sand ore size estimation that achieves superior accuracy over the current state of the art systems is presented.
Proceedings Article
Lookahead pathologies for single agent search
TL;DR: This paper demonstrates that it is not necessarily the case that deeper lookahead increases the chances of taking the optimal action, even when admissible and consistent heuristic functions are used.
Proceedings Article
Machine learning for adaptive image interpretation
Ilya Levner,Vadim Bulitko +1 more
TL;DR: This paper proposes and implements several extensions of ADORE addressing its primary limitations that enable the first successful application of this emerging AI technology to a natural image interpretation domain and is shown to be robust with respect to noise in the training data, illumination, and camera angle variations.