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Annonce

26 mars 2018

PhD proposal on Material classification based on visual appearance


Catégorie : Doctorant


The Image Science and Computer Vision team of Hubert Curien laboratory (https://laboratoirehubertcurien.univ-st-etienne.fr/en/index.html) is looking for candidates for a Ph.D position on Transfer Learning for Material classification based on visual appearance correspondences.

The deadline for applications is 06/05/2018.

 

Image classification has received a lot of interest in the last decade and huge improvements have been observed in terms of classification accuracy for the classical datasets such as PASCAL VOC or ImageNet. Nevertheless, it appears that material classification is still an open problem because of the high variability of their appearance in images and because of the lack of learning data. In order to cope with these problems, recent papers resort to convolutional networks (https://arxiv.org/ftp/arxiv/papers/1710/1710.06854.pdf) in order to learn the variability as well as transfer learning approaches in order to be able to learn on different datasets and so increasing the amount of learning data (https://arxiv.org/pdf/1609.06188.pdf).

The aim of this PhD project is to study the visual appearance of materials from a computer vision perspective by combining computer vision techniques with machine learning and data mining techniques. More and more the design of new materials having specific visual appearance properties passes through the use of computer based approaches (see ref 3, 4 and 5).

The objective will be:

  1. To study different strategies to fuse/combine different datasets, to enrich existing datasets using data augmentation methods (e.g. light variations, scale, shadows, …), to transfer knowledge learnt from one dataset to another one (e.g. see https://arxiv.org/pdf/1609.06188.pdf), to mind/infer knowledge from data, etc.
  2. To create a new dataset of images of materials which could be complementary to the existing synthetized and real-world ones: Flickr Material Database (Sharan et al., 2010), the ImageNet7 dataset (Hu et al., 2011), the MINC-2500 (Bell et al., 2015), the University of Bonn synthetic dataset (Weinmann et al., 2014), …
  3. To classify images of materials according their visual appearance in order to infer/learn new knowledge on material properties (for example using auto-encoders, see https://arxiv.org/pdf/1711.03678.pdf). Several machine learning and data mining methods (e.g. CNN, deep learning, will be investigated.
  4. To learn how to characterize the visual appearance of some materials from a limited set of features and of image acquisitions. The auto-encoder could be a nice tool to access semantic features and observe their impact on the reconstructed material images. This could also help for material design.

The thesis will be co-supervised by Alain Trémeau (Full Professor, https://perso.univ-st-etienne.fr/tremeaua/) and Damien Muselet (Assistant Professor, https://perso.univ-st-etienne.fr/muda8804/).

The deadline for applications is 06/05/2018.

Bibliography

  1. Sébastien Lagarde, “Open Problems in Real-Time Rendering-Physically-Based Materials: Where Are We?” in ACM SIGGRAPH 2017,
    http://openproblems.realtimerendering.com/s2017/02-PhysicallyBasedMaterialWhereAreWe.pdf
  2. (2018) G. Kalliatakis, A. Sticlaru, G. Stamatiadis, S. Ehsan, A. Leonardis, J. Gall and K. D. McDonald-Maier, Material Classification in the Wild: Do Synthesized Training Data Generalise Better than Real-world Training Data?, Proceedings of VISAPP’2018.
  3. (2018) Reviewing the Novel Machine Learning Tools for Materials Design.
    https://link.springer.com/chapter/10.1007/978-3-319-67459-9_7;
  4. (2017) Data mining-aided materials discovery and optimization,
    http://www.sciencedirect.com/science/article/pii/S2352847817300618;
  5. (2017) Materials discovery and design using machine learning,
    http://www.sciencedirect.com/science/article/pii/S2352847817300515;
  6. (2016) An intuitive control space for material appearance, https://dl.acm.org/citation.cfm?id=2980242

Requested skills

The desired profile is Master (MSc or equivalent) or Engineer degree in Machine Learning and Data Mining / Image Processing and Computer Vision / Computer Science and Applied Mathematics, with excellent academic record and research experience, in-depth knowledge of machine learning (Computational Neural Networks, Deep Learning), data mining (Transfer Knowledge), optimization methods, with a specialization in one of the following areas: machine learning, data mining or computer vision.

We are looking for a curious student with excellent programming skills (e.g., in Matlab, Python, or C/C++).

Application

Interested candidates should send a resume, a cover letter, and transcripts of BSc and MSc (M1 and M2 years). Recommendation letters will be appreciated.

All applications must be sent electronically to Alain Trémeau (alain.tremeau@univ-st-etienne.fr) and Damien Muselet (damien.muselet@univ-st-etienne.fr

Contract

3-years contract on the basis of a monthly gross income of 1 760 euros approximatively. Part-time teaching can be considered. Start in autumn 2018.

 

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(c) GdR 720 ISIS - CNRS - 2011-2018.