Search Results for author: Stefan Harmeling

Found 20 papers, 6 papers with code

Non-Iterative Phase Retrieval With Cascaded Neural Networks

no code implementations18 Jun 2021 Tobias Uelwer, Tobias Hoffmann, Stefan Harmeling

Fourier phase retrieval is the problem of reconstructing a signal given only the magnitude of its Fourier transformation.

Learning to Plan via a Multi-Step Policy Regression Method

no code implementations18 Jun 2021 Stefan Wagner, Michael Janschek, Tobias Uelwer, Stefan Harmeling

We propose a new approach to increase inference performance in environments that require a specific sequence of actions in order to be solved.

Contour Proposal Networks for Biomedical Instance Segmentation

1 code implementation7 Apr 2021 Eric Upschulte, Stefan Harmeling, Katrin Amunts, Timo Dickscheid

We construct CPN models with different backbone networks, and apply them to instance segmentation of cells in datasets from different modalities.

Instance Segmentation Object Detection +1

2D histology meets 3D topology: Cytoarchitectonic brain mapping with Graph Neural Networks

no code implementations9 Mar 2021 Christian Schiffer, Stefan Harmeling, Katrin Amunts, Timo Dickscheid

By solving the brain mapping problem on this graph using graph neural networks, we obtain significantly improved classification results.

General Classification Node Classification

Convolutional Neural Networks for cytoarchitectonic brain mapping at large scale

no code implementations25 Nov 2020 Christian Schiffer, Hannah Spitzer, Kai Kiwitz, Nina Unger, Konrad Wagstyl, Alan C. Evans, Stefan Harmeling, Katrin Amunts, Timo Dickscheid

Here we present a new workflow for mapping cytoarchitectonic areas in large series of cell-body stained histological sections of human postmortem brains.

3D Reconstruction

Contrastive Representation Learning for Whole Brain Cytoarchitectonic Mapping in Histological Human Brain Sections

no code implementations25 Nov 2020 Christian Schiffer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid

Cytoarchitectonic maps provide microstructural reference parcellations of the brain, describing its organization in terms of the spatial arrangement of neuronal cell bodies as measured from histological tissue sections.

Contrastive Learning General Classification +2

Fast Multi-Level Foreground Estimation

1 code implementation26 Jun 2020 Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling

Alpha matting aims to estimate the translucency of an object in a given image.

PyMatting: A Python Library for Alpha Matting

1 code implementation25 Mar 2020 Thomas Germer, Tobias Uelwer, Stefan Conrad, Stefan Harmeling

Alpha matting describes the problem of separating the objects in the foreground from the background of an image given only a rough sketch.

Phase Retrieval Using Conditional Generative Adversarial Networks

1 code implementation10 Dec 2019 Tobias Uelwer, Alexander Oberstraß, Stefan Harmeling

In this paper, we propose the application of conditional generative adversarial networks to solve various phase retrieval problems.

On the Vulnerability of Capsule Networks to Adversarial Attacks

1 code implementation9 Jun 2019 Felix Michels, Tobias Uelwer, Eric Upschulte, Stefan Harmeling

This paper extensively evaluates the vulnerability of capsule networks to different adversarial attacks.

Modular Block-diagonal Curvature Approximations for Feedforward Architectures

1 code implementation5 Feb 2019 Felix Dangel, Stefan Harmeling, Philipp Hennig

We propose a modular extension of backpropagation for the computation of block-diagonal approximations to various curvature matrices of the training objective (in particular, the Hessian, generalized Gauss-Newton, and positive-curvature Hessian).

Improving Cytoarchitectonic Segmentation of Human Brain Areas with Self-supervised Siamese Networks

no code implementations13 Jun 2018 Hannah Spitzer, Kai Kiwitz, Katrin Amunts, Stefan Harmeling, Timo Dickscheid

We show that the self-supervised model has implicitly learned to distinguish several cortical brain areas -- a strong indicator that the proposed auxiliary task is appropriate for cytoarchitectonic mapping.

Parcellation of Visual Cortex on high-resolution histological Brain Sections using Convolutional Neural Networks

no code implementations30 May 2017 Hannah Spitzer, Katrin Amunts, Stefan Harmeling, Timo Dickscheid

Its high resolution allows the study of laminar and columnar patterns of cell distributions, which build an important basis for the simulation of cortical areas and networks.

Attribute-Based Classification for Zero-Shot Visual Object Categorization

no code implementations IEEE Transactions on Pattern Analysis and Machine Intelligence 2013 Christoph H. Lampert, Hannes Nickisch, Stefan Harmeling

To tackle the problem, we introduce attribute-based classification: Objects are identified based on a high-level description that is phrased in terms of semantic attributes, such as the object’s color or shape.

Classification Object Recognition +1

A Machine Learning Approach for Non-blind Image Deconvolution

no code implementations CVPR 2013 Christian J. Schuler, Harold Christopher Burger, Stefan Harmeling, Bernhard Scholkopf

In this work, we also rely on a two-step procedure, but learn the second step on a large dataset of natural images, using a neural network.

Image Deconvolution

Space-Variant Single-Image Blind Deconvolution for Removing Camera Shake

no code implementations NeurIPS 2010 Stefan Harmeling, Hirsch Michael, Bernhard Schölkopf

Modelling camera shake as a space-invariant convolution simplifies the problem of removing camera shake, but often insufficiently models actual motion blur such as those due to camera rotation and movements outside the sensor plane or when objects in the scene have different distances to the camera.

How to Explain Individual Classification Decisions

no code implementations6 Dec 2009 David Baehrens, Timon Schroeter, Stefan Harmeling, Motoaki Kawanabe, Katja Hansen, Klaus-Robert Mueller

After building a classifier with modern tools of machine learning we typically have a black box at hand that is able to predict well for unseen data.

Classification General Classification

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