Classificazione di oggetti in immagini attraverso il modello Bag of Visual Words

Generic object recognition is an important function of the human visual system and everybody finds it highly useful in their everyday life. For an artificial vision system it is a really hard, complex and challenging task because instances of the same object category can generate very different images, depending of different variables such as illumination conditions, the pose of an object, the viewpoint of the camera, partial occlusions, and unrelated background clutter. The purpose of this thesis is to develop a system that is able to classify objects in 2D images based on the context, and identify to which category the object belongs to. Given an image, the system can classify it and decide the correct categorie of the object. Furthermore the objective of this thesis is also to test the performance and the precision of different supervised Machine Learning algorithms in this specific task of object image categorization. Through different experiments the implemented application reveals good categorization performances despite the difficulty of the problem. However this project is open to future improvement; it is possible to implement new algorithms that has not been invented yet or using other techniques to extract features to make the system more reliable. This application can be installed inside an embedded system and after trained (performed outside the system), so it can become able to classify objects in a real-time. The information given from a 3D stereocamera, developed inside the department of Computer Engineering of the University of Bologna, can be used to improve the accuracy of the classification task. The idea is to segment a single object in a scene using the depth given from a stereocamera and in this way make the classification more accurate.

The Presence of Words in Visual Art from Surrealism to Conceptualism

Without a doubt, one of the biggest changes that affected XXth century art is the introduction of words into paintings and, in more recent years, in installations. For centuries, if words were part of a visual composition, they functioned as reference; strictly speaking, they were used as a guideline for a better perception of the subject represented. With the developments of the XXth century, words became a very important part of the visual composition, and sometimes embodied the composition itself. About this topic, American art critic and collector Russell Bowman wrote an interesting article called Words and images: A persistent paradox, in which he examines the American and the European art of the XXth century in almost its entirety, dividing it up in six “categories of intention”. The aforementioned categories are not based on the art history timeline, but on the role that language played for specific artists or movements. Taking inspiration from Bowman's article, this paper is structured in three chapters, respectively: words in juxtaposition and free association, words as means of exploration of language structures, and words as means for political and personal messages. The purpose of this paper is therefore to reflect on the role of language in contemporary art and on the way it has changed from artist to artist.

Non-line-of-sight localization with frequency-selective metasurfaces

An emerging technology, that Smart Radio Environments rely on to improve wireless link quality, are Reconfigurable Intelligent Surfaces (RISs). A RIS, in general, can be understood as a thin layer of EM composite material, typically mounted on the walls or ceilings of buildings, which can be reconfigured even after its deployment in the network. RISs made by composing artificial materials in an engineered way, in order to obtain unconventional characteristics, are called metasurfaces. Through the programming of the RIS, it is possible to control and/or modify the radio waves that affect it, thus shaping the radio environment. To overcome the limitations of RISs, the metaprism represents an alternative: it is a passive and non-reconfigurable frequency-selective metasurface that acts as a metamirror to improve the efficiency of the wireless link. In particular, using an OFDM (Orthogonal Frequency-Division Multiplexing) signaling it is possible to control the reflection of the signal, suitably selecting the sub-carrier assigned to each user, without having to interact with the metaprism or having to estimate the CSI. This thesis investigates how OFDM signaling and metaprism can be used for localization purposes, especially to extend the coverage area at low cost, in a scenario where the user is in NLoS (Non-line-of-sight) conditions with respect to the base station, both single antenna. In particular, the paper concerns the design of the analytical model and the corresponding Matlab implementation of a Maximum Likelihood (ML) estimator able to estimate the unknown position, behind an obstacle, from which a generic user transmits to a base station, exploiting the metaprism.