<html><head><meta http-equiv="Content-Type" content="text/html; charset=utf-8"></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; line-break: after-white-space;" class=""><div class="">Ph.D. Thesis proposal - University of Caen Normandy & GREYC CNRS Research Lab </div><div class=""><br class=""></div><div class="">Title: Deep learning of binary partition trees for image analysis</div><div class=""><br class=""></div><div class="">Keywords : Hierarchical representations, Binary partition trees, Deep learning, Ultrametrics, Computer Aided Diagnosis of Skin lesions.</div><div class=""><br class=""></div><div class="">Subject</div><div class="">-------</div><div class="">There are many representations of digital images, each adapted to different contexts. In this thesis we are interested in hierarchical representations of images. These representations allow, from an over-segmentation of an image into super-pixels, to perform merging of regions at different scales. Such hierarchical representations allow to capture image features at different scales simultaneously, and are easily interpreted and manipulated by a human. Building good quality hierarchical representations is a very important step in image analysis. In image analysis, binary partition trees (BPT) are a popular hierarchical representation. Their construction relies on several key elements: an initial partition, a region model, a merging criterion, a merging order. This BPT construction often relies on region descriptors that are poorly suited to the data and on heuristic and greedy hierarchical clustering methods. We propose to take advantage of deep learning for the construction and manipulation of BPTs. The tree construction will then be able to exploit deep descriptors of superpixels, to learn the similarity between these descriptors and finally to have a learned merging criterion. As an ultrametric is a dual representation of a hierarchical representation, deep learning methods can be considered to learn not the ABP but directly the ultrametric from a graph representing the over-segmentation and by explicitly minimizing a cost function. The semantic segmentation of an image can then be seen as either a learned labeling of the vertices of the BPT or the learning of a cut in the BPT. A tree being a graph, convolution neural networks on graphs can be considered for this (convolution and pooling being very particular given the tree structure of the graph). Finally, applications in health (melanoma of the skin) and in satellite imagery will be made.</div><div class=""><br class=""></div><div class="">Qualifications</div><div class="">--------------</div><div class="">Candidates must have an MSc or engineering degree in a field related to computer science, electrical engineering, or applied mathematics, with strong programming skills (in particular with deep learning frameworks). Experience with image processing will be a plus. Candidates are expected to have abilities to write scientific reports and communicate research results at conferences in English.</div><div class=""><br class=""></div><div class=""><br class=""></div><div class="">Information and application: </div><div class="">-----------------------------</div><div class="">Applications should include the following documents in electronic format: i) A short motivation letter stating why you are interested in this thesis, ii) A detailed CV describing your past research background related to the position iii) The transcripts for master degrees. iv) The contact information for three references (do not include the reference letters with your applications as we will only ask for the reference letters for short-listed candidates). Please send your application package to <a href="mailto:olivier.lezoray@unicaen.fr" class="">olivier.lezoray@unicaen.fr</a> and <a href="mailto:sebastien.bougleux@unicaen.fr" class="">sebastien.bougleux@unicaen.fr</a>.</div><div class="">The position will start in October 2023 and will be located in Caen, France. Ideally located in the heart of Normandy, two hours from Paris and just 10 minutes away from the beaches, Caen, William the Conqueror’s hometown, is a lively and dynamic city. </div><div class=""><br class=""></div><div class="">Detailed pdf version</div><div class="">---------------------</div><div style="text-align: left;" class="">Available at <a href="https://lezoray.users.greyc.fr/tmp/sujetTheseLezoray2023_en.pdf" class="">https://lezoray.users.greyc.fr/tmp/sujetTheseLezoray2023_en.pdf</a></div><div class=""><br class=""></div><div class="">
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<span class=""><b class=""><span style="color:#000000;font-size:15px;font-family:helvetica, arial" class="">Olivier LÉZORAY</span></b><br class=""> Full Professor of Computer Science</span> <br class=""><br class="">
<span style="color:#000000;font-size:15px;font-family:helvetica, arial" class=""><b class="">University of Caen Normandy</b>
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<td class="">West Normandy Institute of Technology<br class="">Multimedia and Internet Department<br class="">F-50000 SAINT-LÔ
<a href="tel:+33 2 33 77 55 14" style="color:#3388cc;text-decoration:none" class="">+33(0)233775514</a>
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GREYC UMR CNRS 6072<br class="">Image Team - ENSICAEN<br class="">6 Bd. Marechal Juin<br class="">F-14000 CAEN
<a href="tel:+33 2 31 45 29 27" style="color:#3388cc;text-decoration:none" class="">+33(0)231452927</a>
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