Top-Down Induction of Clustering Trees
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Random Subwindows for Robust Image Classification
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Regression and Classification Approaches to Eye Localization in Face Images
FGR '06 Proceedings of the 7th International Conference on Automatic Face and Gesture Recognition
Machine Learning
Keypoint Recognition Using Randomized Trees
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Fast Emulators of Binary Decision Processes
International Journal of Computer Vision
Methods for automated high-throughput toxicity testing using Zebrafish embryos
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Hough Forests for Object Detection, Tracking, and Action Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going through normal cell cycles, how organisms evolve in different experimental conditions, etc. But, with the large number of images acquired in high-throughput experiments, this manual inspection becomes lengthy, tedious and error-prone. In this paper, we propose to automatically detect specific interest points in microscopy images using machine learning methods with the aim of performing automatic morphometric measurements in the context of Zebrafish studies. We systematically evaluate variants of ensembles of classification and regression trees on four datasets corresponding to different imaging modalities and experimental conditions. Our results show that all variants are effective, with a slight advantage for multiple output methods, which are more robust to parameter choices.