2,775 research outputs found
Geodesic Distance Histogram Feature for Video Segmentation
This paper proposes a geodesic-distance-based feature that encodes global
information for improved video segmentation algorithms. The feature is a joint
histogram of intensity and geodesic distances, where the geodesic distances are
computed as the shortest paths between superpixels via their boundaries. We
also incorporate adaptive voting weights and spatial pyramid configurations to
include spatial information into the geodesic histogram feature and show that
this further improves results. The feature is generic and can be used as part
of various algorithms. In experiments, we test the geodesic histogram feature
by incorporating it into two existing video segmentation frameworks. This leads
to significantly better performance in 3D video segmentation benchmarks on two
datasets
U-Net: Convolutional Networks for Biomedical Image Segmentation
There is large consent that successful training of deep networks requires
many thousand annotated training samples. In this paper, we present a network
and training strategy that relies on the strong use of data augmentation to use
the available annotated samples more efficiently. The architecture consists of
a contracting path to capture context and a symmetric expanding path that
enables precise localization. We show that such a network can be trained
end-to-end from very few images and outperforms the prior best method (a
sliding-window convolutional network) on the ISBI challenge for segmentation of
neuronal structures in electron microscopic stacks. Using the same network
trained on transmitted light microscopy images (phase contrast and DIC) we won
the ISBI cell tracking challenge 2015 in these categories by a large margin.
Moreover, the network is fast. Segmentation of a 512x512 image takes less than
a second on a recent GPU. The full implementation (based on Caffe) and the
trained networks are available at
http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .Comment: conditionally accepted at MICCAI 201
Non-smooth Non-convex Bregman Minimization: Unification and new Algorithms
We propose a unifying algorithm for non-smooth non-convex optimization. The
algorithm approximates the objective function by a convex model function and
finds an approximate (Bregman) proximal point of the convex model. This
approximate minimizer of the model function yields a descent direction, along
which the next iterate is found. Complemented with an Armijo-like line search
strategy, we obtain a flexible algorithm for which we prove (subsequential)
convergence to a stationary point under weak assumptions on the growth of the
model function error. Special instances of the algorithm with a Euclidean
distance function are, for example, Gradient Descent, Forward--Backward
Splitting, ProxDescent, without the common requirement of a "Lipschitz
continuous gradient". In addition, we consider a broad class of Bregman
distance functions (generated by Legendre functions) replacing the Euclidean
distance. The algorithm has a wide range of applications including many linear
and non-linear inverse problems in signal/image processing and machine
learning
Unsupervised feature learning by augmenting single images
When deep learning is applied to visual object recognition, data augmentation
is often used to generate additional training data without extra labeling cost.
It helps to reduce overfitting and increase the performance of the algorithm.
In this paper we investigate if it is possible to use data augmentation as the
main component of an unsupervised feature learning architecture. To that end we
sample a set of random image patches and declare each of them to be a separate
single-image surrogate class. We then extend these trivial one-element classes
by applying a variety of transformations to the initial 'seed' patches. Finally
we train a convolutional neural network to discriminate between these surrogate
classes. The feature representation learned by the network can then be used in
various vision tasks. We find that this simple feature learning algorithm is
surprisingly successful, achieving competitive classification results on
several popular vision datasets (STL-10, CIFAR-10, Caltech-101).Comment: ICLR 2014 workshop track submission (7 pages, 4 figures, 1 table
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