Study of an object recognition algorithm

This project introduces an improvement of the vision capacity of the robot Robotino operating under ROS platform. A method for recognizing object class using binary features has been developed. The proposed method performs a binary classification of the descriptors of each training image to characterize the appearance of the object class. It presents the use of the binary descriptor based on the difference of gray intensity of the pixels in the image. It shows that binary features are suitable to represent object class in spite of the low resolution and the weak information concerning details of the object in the image. It also introduces the use of a boosting method (Adaboost) of feature selection al- lowing to eliminate redundancies and noise in order to improve the performance of the classifier. Finally, a kernel classifier SVM (Support Vector Machine) is trained with the available database and applied for predictions on new images. One possible future work is to establish a visual servo-control that is to say the reac- tion of the robot to the detection of the object.

Deep learning review and its applications

Deep neural networks have recently gained popularity for improv- ing state-of-the-art machine learning algorithms in diverse areas such as speech recognition, computer vision and bioinformatics. Convolutional networks especially have shown prowess in visual recognition tasks such as object recognition and detection in which this work is focused on. Mod- ern award-winning architectures have systematically surpassed previous attempts at tackling computer vision problems and keep winning most current competitions. After a brief study of deep learning architectures and readily available frameworks and libraries, the LeNet handwriting digit recognition network study case is developed, and lastly a deep learn- ing network for playing simple videogames is reviewed.

Comparative study of human age estimation based on hand-crafted and deep face features

In the past few years, human facial age estimation has drawn a lot of attention in the computer vision and pattern recognition communities because of its important applications in age-based image retrieval, security control and surveillance, biomet- rics, human-computer interaction (HCI) and social robotics. In connection with these investigations, estimating the age of a person from the numerical analysis of his/her face image is a relatively new topic. Also, in problems such as Image Classification the Deep Neural Networks have given the best results in some areas including age estimation. In this work we use three hand-crafted features as well as five deep features that can be obtained from pre-trained deep convolutional neural networks. We do a comparative study of the obtained age estimation results with these features.

Advances towards behaviour-based indoor robotic exploration

215 p.

Aurpegiaren detekziorako sistema baten garapen eta balidazioa

[ES]Hoy en día, la linea de investigación sobre la detección de rostros se ha incrementado, debido al uso y la influencia del mismo en diferentes aplicaciones. Por ejemplo, la mayoría de las cámaras digitales actuales, para mejorar la claridad de la imagen y la focalización, tienen incorporado un sistema de detección del rostro. La detección del rostro también es el primer paso para otras aplicaciones y lineas de investigación, como pueden ser el seguimiento de los ojos, y la vigilancia de la seguridad de varias aplicaciones, entre otros. Por esta razón, es necesario realizar una correcta detección facial. En esta tesis de máster, se realizará un análisis y estudio del estado del arte de la detección del rostro, para posteriormente realizar una aplicación práctica, así como su validación y análisis. El detector desarrollado es la conjunción del uso de diferentes cascadas de clasificadores basados en el método de Viola y Jones y las características de Lienhart, y un detector de piel.