Cell segmentation with random ferns and graph-cuts
The progress in imaging techniques have allowed the study of various aspect of cellular mechanisms. To isolate individual cells in live imaging data, we introduce an elegant image segmentation framework that effectively extracts cell boundaries, even in the presence of poor edge details. Our approach works in two stages. First, we estimate pixel interior/border/exterior class probabilities using random ferns. Then, we use an energy minimization framework to compute boundaries whose localization is compliant with the pixel class probabilities. We validate our approach on a manually annotated dataset.
Code (0)
등록된 구현이 없습니다.
Tasks
Cell SegmentationImage SegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
A Comparison of Random Forests and Ferns on Recognition of Instruments in Jazz Recordings
In this paper, we first apply random ferns for classification of real music recordings of a jazz band. No initial segmentation of audio data is assumed, i.e., no onset, offset, nor pitch data are needed. The notion of ra…
ClassificationGeneral ClassificationrFerns: An Implementation of the Random Ferns Method for General-Purpose Machine Learning
In this paper I present an extended implementation of the Random ferns algorithm contained in the R package rFerns. It differs from the original by the ability of consuming categorical and numerical attributes instead of…
AttributeBIG-bench Machine LearningRandom Ferns for Semantic Segmentation of PolSAR Images
Random Ferns -- as a less known example of Ensemble Learning -- have been successfully applied in many Computer Vision applications ranging from keypoint matching to object detection. This paper extends the Random Fern f…
Ensemble Learningobject-detectionObject DetectionSemantic SegmentationMulti-label Ferns for Efficient Recognition of Musical Instruments in Recordings
In this paper we introduce multi-label ferns, and apply this technique for automatic classification of musical instruments in audio recordings. We compare the performance of our proposed method to a set of binary random …
ClassificationGeneral ClassificationEmbedded all relevant feature selection with Random Ferns
Many machine learning methods can produce variable importance scores expressing the usability of each feature in context of the produced model; those scores on their own are yet not sufficient to generate feature selecti…
Allfeature selection