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Master thesis : Deep-Butterflies : Automatic Landmark Detection

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Marganne, Louis ULiège
Promotor(s) : Geurts, Pierre ULiège ; Marée, Raphaël ULiège
Date of defense : 27-Jun-2022/28-Jun-2022 • Permalink : http://hdl.handle.net/2268.2/14509
Details
Title : Master thesis : Deep-Butterflies : Automatic Landmark Detection
Translated title : [fr] Deep-Butterflies : Détection Automatique de Landmarks
Author : Marganne, Louis ULiège
Date of defense  : 27-Jun-2022/28-Jun-2022
Advisor(s) : Geurts, Pierre ULiège
Marée, Raphaël ULiège
Committee's member(s) : Louppe, Gilles ULiège
Wehenkel, Antoine ULiège
Language : English
Number of pages : 74
Keywords : [en] Machine Learning
[en] Deep Learning
[en] Automatic Landmark Detection
[en] Landmark
[en] Morpho
[en] Cytomine
Discipline(s) : Engineering, computing & technology > Computer science
Target public : Researchers
Professionals of domain
Student
Institution(s) : Université de Liège, Liège, Belgique
Degree: Master en ingénieur civil en informatique, à finalité spécialisée en "intelligent systems"
Faculty: Master thesis of the Faculté des Sciences appliquées

Abstract

[en] In the biomedical research field, and more precisely the field of morphometric studies, the detection of anatomical landmarks is a crucial step in order to quantify the shape and size of an object under study. The annotations of theses landmarks is quite laborious and often requires dedicated human expertise. Thus the use of automatic landmark detection techniques using artificial intelligence began to gain importance.

This thesis explores different kinds of approach in order to tackle the problem on butterflies from the Morpho genus. Each butterfly possesses two types of landmarks: true landmarks and semi-landmarks, and has two views: ventral and dorsal, resulting in a total of 4 distinct datasets. Moreover, the Morpho genus contain about 30 species which induces a great variety between the images.

Several approaches have been experimented, namely computer vision, machine learning and deep learning. The results shows that the deep learning approach outperforms the others in most cases. Nevertheless the machine learning approach has proven its performance on smaller part of the data, \textit{i.e.} datasets composed of one specie only. Unfortunately, the computer vision approach did not lead to any convincing results.

Finally, this work presents the Cytomine application that has been built along with the most consistent model from the experimentations. This application provides a user-friendly interface for both training and predicting with a new model.


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  • Marganne, Louis ULiège Université de Liège > Master ingé. civ. info., à fin.

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