Σημασιολογική κατάτμηση αντικειμένων με τεχνικές φασματικής θεωρίας γράφων

Chatzipantazis, Evangelos; Χατζηπανταζής, Ευάγγελος

dc.contributor.author	Chatzipantazis, Evangelos	en
dc.contributor.author	Χατζηπανταζής, Ευάγγελος	el
dc.date.accessioned	2018-11-16T09:10:18Z
dc.date.available	2018-11-16T09:10:18Z
dc.date.issued	2018-11-16
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/48018
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.15422
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	Spectral	en
dc.subject	Φασματική	el
dc.subject	Semisupervised	en
dc.subject	Mustlink	en
dc.subject	Cannotlink	en
dc.subject	Graphcut	en
dc.subject	Ημιεπιβλεπόμενη	el
dc.subject	Σύζευξη	el
dc.subject	Αποσύζευξη	el
dc.subject	Γραφοτομή	el
dc.title	Σημασιολογική κατάτμηση αντικειμένων με τεχνικές φασματικής θεωρίας γράφων	el
dc.title	Semantic image segmentation using spectral graph-theoretic techniques	en
heal.type	bachelorThesis
heal.classification	Αντίληψη και όραση υπολογιστών	en
heal.classification	Perception and computer vision	en
heal.classificationURI	http://data.seab.gr/concepts/12c1c913dbe758d67c4c509a6768bdbc7905830c
heal.classificationURI	http://data.seab.gr/concepts/12c1c913dbe758d67c4c509a6768bdbc7905830c
heal.language	el
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2018-10-11
heal.abstract	Methods on data segmentation on graphs have in recent years shown an increase in interest due to their effectiveness in a variety of applications and a successful representation of the structure of the data and their relational properties. Particularly in the area of Computer Vision, the process of creating graphs from images has begun to solve many unsupervised learning problems with which classical methods have struck. One such example of data partitioning algorithms in the structure of a graph is the spectral graph clustering family that discovers the global properties of a graph and the underlying compact structure of the clusters that compose it by analyzing its spectrum. Through the association of image segmentation with data clustering and perceptual grouping and with intuition inspired by the laws of Gestalt in psychology, we apply an automated hierarchical method to transform image representation from the pixel-wise representation into a graph representation. We then formalize rigorously the spectral segmentation algorithms, analyze and solve the optimization problem formed by the Normalized Cuts and argue for its correctness by associating it simultaneously with other well-thought-out segmentation techniques. Finally, we open the "black box" of the spectral graph clustering algorithms, which are currently a non-supervised method, with the addition of extraneous information and we theoretically establish a framework for the effective introduction of external constraints	en
heal.advisorName	Maragos, Petros	en
heal.committeeMemberName	Ψυλλάκης, Χαράλαμπος	el
heal.committeeMemberName	Maragos, Petros	en
heal.committeeMemberName	Ποταμιάνος, Γεράσιμος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Ηλεκτρολόγων Μηχανικών και Μηχανικών Υπολογιστών. Τομέας Σημάτων, Ελέγχου και Ρομποτικής	el
heal.academicPublisherID	ntua
heal.numberOfPages	135 σ.
heal.fullTextAvailability	true