Christian Schuler

Alumni

Empirical Inference

Overview
Publications

I work on empirical inference on image data. Taking a photo is a measurement process, which can be affected by different types of errors. This includes motion blur, optical aberrations, and noise.

With prior knowledge of both the typical image content and the source of corruption, the measurement error can be removed to a large extent. In particular, I'm interested in using machine learning techniques to extract and model this prior knowledge.

9 results (View BibTeX file of all listed publications)

2016

Learning to Deblur

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.

IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

2016

ei Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B. Learning to Deblur IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(7):1439-1451, IEEE, 2016 (article)

DOI [BibTex]

2015

Method and device for recovering a digital image from a sequence of observed digital images

Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B., Schuler, C.

European Patent, No. 11767924.1, November 2015 (patent)

[BibTex]

2015

ei Harmeling, S., Hirsch, M., Sra, S., Schölkopf, B., Schuler, C. Method and device for recovering a digital image from a sequence of observed digital images European Patent, No. 11767924.1, November 2015 (patent)

[BibTex]

Machine Learning Approaches to Image Deconvolution

Schuler, C.

University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

[BibTex]

ei Schuler, C. Machine Learning Approaches to Image Deconvolution University of Tübingen, Germany, University of Tübingen, Germany, September 2015 (phdthesis)

[BibTex]

Retrospective motion correction of magnitude-input MR images

Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B.

First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)

DOI [BibTex]

ei Loktyushin, A., Schuler, C., Scheffler, K., Schölkopf, B. Retrospective motion correction of magnitude-input MR images First International Workshop on Machine Learning Meets Medical Imaging (MLMMI 2015), held in conjunction with ICML 2015, 9487, pages: 3-12, Lecture Notes in Computer Science, (Editors: K. K. Bhatia and H. Lombaert), Springer, July 2015 (conference)

DOI [BibTex]

2014

Probabilistic Progress Bars

Kiefel, M., Schuler, C., Hennig, P.

In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

Abstract

Predicting the time at which the integral over a stochastic process reaches a target level is a value of interest in many applications. Often, such computations have to be made at low cost, in real time. As an intuitive example that captures many features of this problem class, we choose progress bars, a ubiquitous element of computer user interfaces. These predictors are usually based on simple point estimators, with no error modelling. This leads to fluctuating behaviour confusing to the user. It also does not provide a distribution prediction (risk values), which are crucial for many other application areas. We construct and empirically evaluate a fast, constant cost algorithm using a Gauss-Markov process model which provides more information to the user.

ei ps pn

website+code pdf DOI Project Page [BibTex]

2014

ei ps pn Kiefel, M., Schuler, C., Hennig, P. Probabilistic Progress Bars In Conference on Pattern Recognition (GCPR), 8753, pages: 331-341, Lecture Notes in Computer Science, (Editors: Jiang, X., Hornegger, J., and Koch, R.), Springer, GCPR, September 2014 (inproceedings)

website+code pdf DOI Project Page [BibTex]

Method and device for blind correction of optical aberrations in a digital image

Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B.

International Patent Application, No. PCT/EP2012/068868, April 2014 (patent)

[BibTex]

ei Schuler, C., Hirsch, M., Harmeling, S., Schölkopf, B. Method and device for blind correction of optical aberrations in a digital image International Patent Application, No. PCT/EP2012/068868, April 2014 (patent)

[BibTex]

Mask-Specific Inpainting with Deep Neural Networks

Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S.

In Pattern Recognition (GCPR 2014), pages: 523-534, (Editors: X Jiang, J Hornegger, and R Koch), Springer, 2014, Lecture Notes in Computer Science (inproceedings)

PDF DOI [BibTex]

ei Köhler, R., Schuler, C., Schölkopf, B., Harmeling, S. Mask-Specific Inpainting with Deep Neural Networks In Pattern Recognition (GCPR 2014), pages: 523-534, (Editors: X Jiang, J Hornegger, and R Koch), Springer, 2014, Lecture Notes in Computer Science (inproceedings)

PDF DOI [BibTex]

Learning to Deblur

Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B.

In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

link (url) [BibTex]

ei Schuler, C. J., Hirsch, M., Harmeling, S., Schölkopf, B. Learning to Deblur In NIPS 2014 Deep Learning and Representation Learning Workshop, 28th Annual Conference on Neural Information Processing Systems (NIPS), 2014 (inproceedings)

link (url) [BibTex]

2013

A machine learning approach for non-blind image deconvolution

Schuler, C., Burger, H., Harmeling, S., Schölkopf, B.

In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1067-1074, IEEE, CVPR, 2013 (inproceedings)

Web Web DOI [BibTex]

2013

ei Schuler, C., Burger, H., Harmeling, S., Schölkopf, B. A machine learning approach for non-blind image deconvolution In IEEE Conference on Computer Vision and Pattern Recognition, pages: 1067-1074, IEEE, CVPR, 2013 (inproceedings)

Web Web DOI [BibTex]