Publication

Filter Learning for Linear Structure Segmentation

Pascal Fua
2011
Reports

Abstract

We introduce an approach to learning convolution filters whose joint output can be fed to a classifier that labels them as belonging to linear structures or not. The filters are learned using sparse synthesis techniques but we show that enforcing constraints is not required at run-time to achieve good classification performance. In practice, this is important as it drastically reduces the computational cost. We show that our approach outperforms the state-of-the-art on difficult, and very different, images of roads, retinal scans, and dendritic networks.

Official source

https://infoscience.epfl.ch/entities/publication/1f213650-e64c-4041-9bee-772021b714ea

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Pascal Fua
2011
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/1f213650-e64c-4041-9bee-772021b714ea

About this result

Related concepts (32)

Convolutional neural network

Convolutional neural network (CNN) is a regularized type of feed-forward neural network that learns feature engineering by itself via filters (or kernel) optimization. Vanishing gradients and exploding gradients, seen during backpropagation in earlier neural networks, are prevented by using regularized weights over fewer connections. For example, for each neuron in the fully-connected layer 10,000 weights would be required for processing an image sized 100 × 100 pixels.

Linear algebra

Linear algebra is the branch of mathematics concerning linear equations such as: linear maps such as: and their representations in vector spaces and through matrices. Linear algebra is central to almost all areas of mathematics. For instance, linear algebra is fundamental in modern presentations of geometry, including for defining basic objects such as lines, planes and rotations. Also, functional analysis, a branch of mathematical analysis, may be viewed as the application of linear algebra to spaces of functions.

Deep learning

Deep learning is part of a broader family of machine learning methods, which is based on artificial neural networks with representation learning. The adjective "deep" in deep learning refers to the use of multiple layers in the network. Methods used can be either supervised, semi-supervised or unsupervised.

Related publications (32)

Filter Learning for Linear Structure Segmentation

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