A fast learning algorithm for deep belief nets
Neural Computation
Automatic recognition of biological particles in microscopic images
Pattern Recognition Letters
International Journal of Computer Vision
Training an active random field for real-time image denoising
IEEE Transactions on Image Processing
Sequential deep learning for human action recognition
HBU'11 Proceedings of the Second international conference on Human Behavior Unterstanding
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We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.