Master’s Dissertation Defense, Guilherme Nascimento

We would like to congratulate Guilherme da Silva Nascimento for his new achievement, Master in Computer Science, at the UFMG.

Title: Modeling scenes with robust sparse representation for indoor scene recognition

 

Abstract
Convolutional Neural Networks brought to the Computer Vision and Pattern Recognition fields a new way to characterize objects and scenes, achieving the state-of-the-art results on many classification tasks. Although these features encode highly discriminative characteristics in many classification domains, scene recognition still remains as a challenging task because of its particular attributes. In this work we present a new method to build a representative vector composed of global (e.g., environment’s structure) and local (e.g., characteristics of common objects of a particular scene) features from fragments of the scene by using robust sparse representations. The experiments have shown that the final resulting representation is more robust to noise and occlusion and it achieves the highest accuracy on well-known scene datasets, outperforming the previous results of the literature.

 

Committee
Prof. Erickson Rangel do Nascimento – Advisor (DCC – UFMG)
Prof. Anisio Mendes Lacerda (DECOM – CEFET)
Prof. Mario Fernando Montenegro Campos (DCC – UFMG)
Prof. William Robson Schwartz (DCC – UFMG)
Guilherme_master_dissertation
Guilherme_master_dissertation
Committee
Guilherme_master_dissertation
Guilherme_master_dissertation
Presentation
Guilherme_master_dissertation
Guilherme_master_dissertation
Presentation

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