@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} }