Mitigating non-Lambertian surfaces issues in Stereo Matching with Neural Radiance Fields

Depth estimation from images has long been regarded as a preferable alternative compared to expensive and intrusive active sensors, such as LiDAR and ToF. The topic has attracted the attention of an increasingly wide audience thanks to the great amount of application domains, such as autonomous driving, robotic navigation and 3D reconstruction. Among the various techniques employed for depth estimation, stereo matching is one of the most widespread, owing to its robustness, speed and simplicity in setup. Recent developments has been aided by the abundance of annotated stereo images, which granted to deep learning the opportunity to thrive in a research area where deep networks can reach state-of-the-art sub-pixel precision in most cases. Despite the recent findings, stereo matching still begets many open challenges, two among them being finding pixel correspondences in presence of objects that exhibits a non-Lambertian behaviour and processing high-resolution images. Recently, a novel dataset named Booster, which contains high-resolution stereo pairs featuring a large collection of labeled non-Lambertian objects, has been released. The work shown that training state-of-the-art deep neural network on such data improves the generalization capabilities of these networks also in presence of non-Lambertian surfaces. Regardless being a further step to tackle the aforementioned challenge, Booster includes a rather small number of annotated images, and thus cannot satisfy the intensive training requirements of deep learning. This thesis work aims to investigate novel view synthesis techniques to augment the Booster dataset, with ultimate goal of improving stereo matching reliability in presence of high-resolution images that displays non-Lambertian surfaces.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.

Analisi di video con visione binoculare

Nel TCR - Termina container Ravenna, è importante che nel momento di scarico del container sul camion non siano presenti persone nell’area. In questo elaborato si descrive la realizzazione e il funzionamento di un sistema di allarme automatico, in grado di rilevare persone ed eventualmente interrompere la procedura di scarico del container. Tale sistema si basa sulla tecnica della object segmentation tramite rimozione dello sfondo, a cui viene affiancata una classificazione e rimozione delle eventuali ombre con un metodo cromatico. Inoltre viene identificata la possibile testa di una persona e avendo a disposizione due telecamere, si mette in atto una visione binoculare per calcolarne l’altezza. Infine, viene presa in considerazione anche la dinamica del sistema, per cui la classificazione di una persona si può basare sulla grandezza, altezza e velocità dell’oggetto individuato.

Machine learning and convolutional neural networks for melanomas identification

Il machine learning negli ultimi anni ha acquisito una crescente popolarità nell’ambito della ricerca scientifica e delle sue applicazioni. Lo scopo di questa tesi è stato quello di studiare il machine learning nei suoi aspetti generali e applicarlo a problemi di computer vision. La tesi ha affrontato le difficoltà del dover spiegare dal punto di vista teorico gli algoritmi alla base delle reti neurali convoluzionali e ha successivamente trattato due problemi concreti di riconoscimento immagini: il dataset MNIST (immagini di cifre scritte a mano) e un dataset che sarà chiamato ”MELANOMA dataset” (immagini di melanomi e nevi sani). Utilizzando le tecniche spiegate nella sezione teorica si sono riusciti ad ottenere risultati soddifacenti per entrambi i dataset ottenendo una precisione del 98% per il MNIST e del 76.8% per il MELANOMA dataset

Neural Radiance Fields for Relighting and View Synthesis

Neural scene representation and neural rendering are new computer vision techniques that enable the reconstruction and implicit representation of real 3D scenes from a set of 2D captured images, by fitting a deep neural network. The trained network can then be used to render novel views of the scene. A recent work in this field, Neural Radiance Fields (NeRF), presented a state-of-the-art approach, which uses a simple Multilayer Perceptron (MLP) to generate photo-realistic RGB images of a scene from arbitrary viewpoints. However, NeRF does not model any light interaction with the fitted scene; therefore, despite producing compelling results for the view synthesis task, it does not provide a solution for relighting. In this work, we propose a new architecture to enable relighting capabilities in NeRF-based representations and we introduce a new real-world dataset to train and evaluate such a model. Our method demonstrates the ability to perform realistic rendering of novel views under arbitrary lighting conditions.

Operazioni di assemblaggio automatizzato tramite robot antropomorfo e visione artificiale

Obiettivo dello studio condotto è l’implementazione di cicli di operazioni per l’assemblaggio automatizzato di componenti che costituiscono un sistema di trasporto a catena presente in alcune macchine automatiche. L’automazione del processo, fino ad oggi svolto manualmente, prevede l’utilizzo di un robot e, per il controllo di quest’ultimo, di un sistema di visione artificiale. L’attività di tirocinio associata alla tesi di laurea, che ha incluso una parte sperimentale oltre alla scrittura degli algoritmi di controllo del robot, è stata svolta all’interno del laboratorio TAILOR (Technology and Automation for Industry LabORatory) presso Siropack Italia S.r.l dove è presente una cella dotata di robot antropomorfo (Mitsubishi Electric) e di sistema di visione artificiale con camere 2D (Omron). La presenza di quest’ultimo è risultata strategica in termini di possibilità di adattare il montaggio anche a diversi posizionamenti degli oggetti all’interno dello spazio di lavoro, fermo restando che gli stessi risultassero appoggiati su una superficie piana. In primo luogo, affinché fosse garantita la ripetibilità del processo, sono state testate le prestazioni del sistema di visione tramite opportuna calibrazione della camera e del sistema di illuminazione ad esso collegata, al fine di ottenere un’acquisizione delle immagini che fosse sufficientemente robusta e risoluta mediante lo sfruttamento del software di elaborazione Omron FH Vision System. Un’opportuna programmazione della traiettoria del robot in ambiente di simulazione RT Toolbox 3, software integrato nel sistema di controllo del robot Mitsubishi Electric, ha infine consentito le regolari operazioni di assemblaggio, garantendo un processo affidabile ed, allo stesso tempo, adattabile ad ambienti eventualmente non strutturati in cui esso si trova ad operare.

Generative Neural Networks for image iper-resolution and improvement of Optical Character Recognition's performances

The usage of Optical Character Recognition’s (OCR, systems is a widely spread technology into the world of Computer Vision and Machine Learning. It is a topic that interest many field, for example the automotive, where becomes a specialized task known as License Plate Recognition, useful for many application from the automation of toll road to intelligent payments. However, OCR systems need to be very accurate and generalizable in order to be able to extract the text of license plates under high variable conditions, from the type of camera used for acquisition to light changes. Such variables compromise the quality of digitalized real scenes causing the presence of noise and degradation of various type, which can be minimized with the application of modern approaches for image iper resolution and noise reduction. Oneclass of them is known as Generative Neural Networks, which are very strong ally for the solution of this popular problem.

Antibody-Antigen Interface Characterization with Contextualized Pre-trained Embeddings

Artificial Intelligence (AI) has substantially influenced numerous disciplines in recent years. Biology, chemistry, and bioinformatics are among them, with significant advances in protein structure prediction, paratope prediction, protein-protein interactions (PPIs), and antibody-antigen interactions. Understanding PPIs is critical since they are responsible for practically everything living and have several uses in vaccines, cancer, immunology, and inflammatory illnesses. Machine Learning (ML) offers enormous potential for effectively simulating antibody-antigen interactions and improving in-silico optimization of therapeutic antibodies for desired features, including binding activity, stability, and low immunogenicity. This research looks at the use of AI algorithms to better understand antibody-antigen interactions, and it further expands and explains several difficulties encountered in the field. Furthermore, we contribute by presenting a method that outperforms existing state-of-the-art strategies in paratope prediction from sequence data.

Framework for anomaly detection on photovoltaic plants

Technological advancement has undergone exponential growth in recent years, and this has brought significant improvements in the computational capabilities of computers, which can now perform an enormous amount of calculations per second. Taking advantage of these improvements has made it possible to devise algorithms that are very demanding in terms of the computational resources needed to develop architectures capable of solving the most complex problems: currently the most powerful of these are neural networks and in this thesis I will combine these tecniques with classical computer vision algorithms to improve the speed and accuracy of maintenance in photovoltaic facilities.

The indoor localization system using vision for multiple mobile robots

This paper presents the development of an indoor localization system using camera vision. The localization system has a capability to determine 2D coordinate (x, y) for a team of mobile robots, Miabot. The experimental results show that the system outperforms our existing sonar localizer both in accuracy and a precision.

Groundtruth system for underwater benchmarking

Oceans - San Diego, 2013

User friendly knowledge acquisition system for medical devices actuation

Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica