@mastersthesis {xNespereira, title = {Siamese Convolutional Neural Network for Learning Object Similarities in RGB-D Images}, year = {2016}, abstract = {

Student: Alejandro Nespereira

Advisors: Farzad Husain (Catchoom), Tomasz Adamek (Catchoom) and Xavier Gir{\'o}-i-Nieto (UPC)

Program:\ Master in Computer Vision\ (Class of 2016)

This report explores the suitability of using a Siamese Convolutional Neural Network (CNN) for the task of false positive rejection. We present a Siamese CNN model trained with an in-house dataset of weakly textured objects. Our model is able to successfully assert the classification of an object detection pipeline with unseen new objects. Additionally, we also compare it with a hand-crafted method in order to compare its performance. We demonstrate the usage of our model by learning to discriminate between inter and intra object classes for a challenging dataset.

}, author = {Nespereira, Alejandro}, editor = {Husain, Farzad and Adamek, Tomasz and Xavier Gir{\'o}-i-Nieto} } @mastersthesis {xFerrarons-Betrian, title = {Mobile Visual Search at Catchoom}, year = {2014}, abstract = {

Author: Miquel Ferrarons-Betrian

Advisors:\ Xavier Gir{\'o}-i-Nieto\ (UPC) and Tomasz Adamek\ (Catchoom)

Degree: Master in Computer Vision (1 year)

}, keywords = {feature selection, large-scale visual search, Mobile visual search, synthetic views matching, visual word}, author = {Ferrarons-Betrian, Miquel}, editor = {Adamek, Tomasz and Xavier Gir{\'o}-i-Nieto} }