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Toward a perceptual object recognition system
Awad, Dounia

Data: 2015
Resum: The main objective of this thesis is to propose a pipeline for an object recognition algorithm based on human perception which addresses the object recognition system complexity: query run time and memory allocation. In this context, we propose a filter based on a visual attention system to address the problems of extracting a large number of points of interest using existing region selection techniques. We chose to use bottom-up visual attention systems that encode attentional fixations in a topographic map, known as a saliency map. This map serves as basis for generating a mask to select salient points according to human interest, from the points extracted by a region selection technique. Furthermore, we addressed the problem of high dimensionality of descriptors in region appearance phase. We proposed a new hybrid descriptor representing the spatial frequency of some perceptual features, extracted by a visual attention system (color, texture, intensity. This descriptor consist of a concatenation of energy measures computed at the output of a filter bank, at each level of the multi-resolution pyramid of perceptual features. This descriptor has the advantage of being lower dimensional than traditional descriptors.
Nota: Advisor/s: Vincent Courboulay and Arnaud Revel. Date and location of PhD thesis defense: 5 September 2014, University of La Rochelle
Drets: Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial i la comunicació pública de l'obra, sempre que no sigui amb finalitats comercials, i sempre que es reconegui l'autoria de l'obra original. No es permet la creació d'obres derivades. Creative Commons
Llengua: Anglès.
Document: other ; abstract ; publishedVersion
Matèria: Computer vision
Publicat a: ELCVIA : Electronic Letters on Computer Vision and Image Analysis, Vol. 14 Núm. 3 (2015) , p. 11-12 (Special Issue on Recent PhD Thesis Dissemination (2014)) , ISSN 1577-5097

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DOI: 10.5565/rev/elcvia.714

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