@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 Tomasz Adamek 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 = {Tomasz Adamek and Xavier Gir{\'o}-i-Nieto} } @conference {cO{\textquoteright}Connor03, title = {Region and object segmentation algorithms in the Qimera segmentation platform}, booktitle = {Third International Workshop on Content-Based Multimedia Indexing}, year = {2003}, pages = {95{\textendash}103}, abstract = {

In this paper we present the Qimera segmentation platform and describe the different approaches to segmentation that have been implemented in the system to date. Analysis techniques have been implemented for both region-based and object-based segmentation. The region-based segmentation algorithms include: a colour segmentation algorithm based on a modified Recursive Shortest Spanning Tree (RSST) approach, an implementation of a colour image segmentation algorithm based on the K-Means-with-Connectivity-Constraint (KMCC) algorithm and an approach based on the Expectation Maximization (EM) algorithm applied in a 6D colour/texture space. A semi-automatic approach to object segmentation that uses the modified RSST approach is outlined. An automatic object segmentation approach via snake propagation within a level-set framework is also described. Illustrative segmentation results are presented in all cases. Plans for future research within the Qimera project are also discussed.

}, isbn = {978-84-612-2373-2}, author = {O{\textquoteright}Connor, N. and Sav, S. and Tomasz Adamek and Mezaris, V. and Kompatsiaris, I. and Lui, T. and Izquierdo, E. and Ferran, C. and Josep R Casas} }