Image Segmentation with Cascaded Hierarchical Models and Logistic Disjunctive Normal Networks

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

U-Net: Convolutional Networks for Biomedical Image Segmentation (2015, background): “More recent approaches [11,4] proposed a classifier output that takes into account the features from multiple layers.”
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation (2016, background): “Challenging biomedical 2D images can be segmented with an accuracy close to human performance by CNNs today [3,11,12].”
Semantic Image Segmentation with Contextual Hierarchical Models (2015, methodology): “LDNN allows an intuitive initialization using k-means clustering and outperforms neural networks, SVMs, and random forests on several standard datasets [24].”
Submitted to Ieee Transactions on Pattern Analysis and Machine Intelligence 1 Multiscale Centerline Detection (unknown): “Finally, the approach of [47] relies on a cascade of classifiers at different resolutions to predict a boundary map.”
Projection onto the Manifold of Elongated Structures for Accurate Extraction (2015, methodology): “Recently, attention has shifted to classification based methods [5, 34, 38, 24, 12], which have produced significant improvements.”