Camouflaged object detection and segmentation

Στρατάκης, Μιχαήλ; Stratakis, Michail

dc.contributor.author	Στρατάκης, Μιχαήλ	el
dc.contributor.author	Stratakis, Michail	en
dc.date.accessioned	2023-05-15T10:40:56Z
dc.date.available	2023-05-15T10:40:56Z
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/57706
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.25403
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ελλάδα	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/gr/	*
dc.subject	Καμουφλαρισμένα Αντικείμενα, Τμηματοποίηση Εικόνας, Aυτό-Προσοχή	el
dc.subject	Camouflaged Object	en
dc.subject	Image Segmentation	en
dc.subject	Self-Attention	en
dc.title	Camouflaged object detection and segmentation	en
dc.title	Ανίχνευση και τμηματοποίηση καμουφλαρισμένων αντικειμένων	el
heal.type	bachelorThesis
heal.classification	Computer Vision	en
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2023-03-20
heal.abstract	Camouflaged object detection and segmentation is a branch of computer vision that aims to find objects that are difficult to detect by the human eye. This is a process opposite to finding a salient object, where the parts of the image to be detected are distinct, easily recognizable and their boundaries are differentiated enough from the rest of the background environment. In the case of camouflaged objects, the parts of the image to be detected often show a high similarity to the rest of the environment, greatly increasing the difficulty and challenges that must be faced to carry out this task. In this thesis we introduce a new architecture that combines the powerful capabilities of Transformer En- coders, for extracting global features, with the existing structures of Convolutional Encoders for capturing local features. When detecting a camouflaged object it is quite difficult to detect the exact details near its edges. Inspired by this ability of these objects, we introduce a novel method for combining the extracted features from these two encoders in order to produce rich features both at the level of detail and at the level of semantic interpretation. We evaluate and compare our model on common datasets and with common evaluation metrics and present our findings. The results obtained are quite encouraging as they achieve excellent performance and are able to stand up against the latest technologies in literature. Finally, we observe how the performance of our model changes as we modify either the algorithm or some parameters and afterwards we point out possible uses of our model for medical and other purposes.	en
heal.advisorName	Stamou, Giorgos	en
heal.committeeMemberName	Stamou, Giorgos	en
heal.committeeMemberName	Voulodimos, Athanasios	en
heal.committeeMemberName	Kollias, Stefanos	en
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Τεχνολογίας Πληροφορικής και Υπολογιστών	el
heal.academicPublisherID	ntua
heal.numberOfPages	56 σ.	el
heal.fullTextAvailability	false