Mobile robot teleoperation through eye-gaze (telegaze)

In most teleoperation applications the human operator is required to monitor the status of the robot, as well as, issue controlling commands for the whole duration of the operation. Using a vision based feedback system, monitoring the robot requires the operator to look at a continuous stream of images displayed on an interaction screen. The eyes of the operator therefore, are fully engaged in monitoring and the hands in controlling. Since the eyes of the operator are engaged in monitoring anyway, inputs from their gaze can be used to aid in controlling. This frees the hands of the operator, either partially or fully, from controlling which can then be used to perform any other necessary tasks. However, the challenge here lies in distinguishing between the inputs that can be used for controlling and the inputs that can be used for monitoring. In mobile robot teleoperation, controlling is mainly composed of issuing locomotion commands to drive the robot. Monitoring on the other hand, is looking where the robot goes and looking for any obstacles in the route. Interestingly, there exist a strong correlation between human's gazing behaviours and their moving intentions. This correlation has been exploited in this thesis to investigate novel means for mobile robot teleoperation through eye-gaze, which has been named TeleGaze for short

A Review on the use of Artificial Intelligence Techniques in Brain MRI Analysis

Over the past 20 years, the global research going on in Artificial Intelligence in applica-tions in medication is a venue internationally, for medical trade and creating an ener-getic research community. The Artificial Intelligence in Medicine magazine has posted a massive amount. This paper gives an overview of the history of AI applications in brain MRI analysis to research its effect at the wider studies discipline and perceive de-manding situations for its destiny. Analysis of numerous articles to create a taxono-my of research subject matters and results was done. The article is classed which might be posted between 2000 and 2018 with this taxonomy. Analyzed articles have excessive citations. Efforts are useful in figuring out popular studies works in AI primarily based on mind MRI analysis throughout specific issues. The biomedical prognosis was ruled by way of knowledge engineering research in its first decade, whilst gadget mastering, and records mining prevailed thereafter. Together these two topics have contributed a lot to the latest medical domain

Investigation of the complementary use of non-invasive techniques for the holistic analysis of paintings and automatic analysis of large scale spectral imaging data

The analysis of painting materials and methods is acknowledged for providing important information to art history. This study illustrates a detailed examination of the characteristics, advantages and capabilities that the combined application of a variety of non-invasive techniques, ranging from spectral imaging and optical coherence tomography (OCT), to fibre optics reflectance spectroscopy (FORS), X-ray Fluorescence (XRF) and Raman spectroscopy, has to offer. The analysis of painting materials is seen under the prism of a holistic examination of different types of cultural heritage objects. More specifically, the limitations that the individual techniques face and, most importantly, how their complementary use can overcome them are thoroughly investigated through the examination of a large and heterogeneous statistical sample, in a completely novel way. The heterogeneity of the sample is related both to the painting materials (i.e pigments, binding media and substrates) and the degradation level (i.e. paintings stored in storages of museum and murals of caves that are exposed in the environmental conditions of the desert). For the extraction of accurate conclusions about the painting materials and methods applied in a specific period, the examination of large number of artworks of this period is required. PRISMS, the spectral imaging system developed by our group enables the time efficient imaging of large painting surfaces, leading to the acquisition of large scale spectral imaging data, which makes such an analysis faster, more cost-effective and less laborious without diminishing the quality of the results. This study proposes methods based on the statistical analysis for the automatic processing of the spectral imaging data in two directions: the revealing of information that is obvious under visual observation and clustering of the spectral information. With regards to the automatic revealing of hidden information, the potential of principal component analysis (PCA) and independent principal analysis (ICA), two of the most commonly used statistical analysis methods, were examined giving very good results. In addition, the development of a new method for the automatic clustering of large scale spectral information based on the 'Self-organised mapping' (SOM) method is presented. The spectral feature of the analysed areas in the UV-VIS/NIR (400-900 nm) is indicative for its pigment composition, therefore the automatic clustering of the pixel-level spectral information that the PRISMS system provides can classify the areas according to their pigment composition. The application of statistical analysis methods in the preliminary stage of the analysis of large number of artworks (e.g. large painting collections) of large surface painting areas (e.g. murals) is of significant importance; as they highlight the areas that should be examined in detail. The multimodal non-invasive approach was applied on the examination of three artworks of significant importance for East Asian art history; the cave 465 of the Mogao complex in China, the export Chinese watercolor paintings from the collections of the Victoria and Albert (V&A) museum and the Royal Horticulture Society (RHS) and the Selden map. The examination of these three works of art, in addition to providing a wide and heterogeneous sample for the detailed examination of the multi-modal approach, has also helped addressing several historical questions

Visual Analytics for the Exploratory Analysis and Labeling of Cultural Data

Cultural data can come in various forms and modalities, such as text traditions, artworks, music, crafted objects, or even as intangible heritage such as biographies of people, performing arts, cultural customs and rites. The assignment of metadata to such cultural heritage objects is an important task that people working in galleries, libraries, archives, and museums (GLAM) do on a daily basis. These rich metadata collections are used to categorize, structure, and study collections, but can also be used to apply computational methods. Such computational methods are in the focus of Computational and Digital Humanities projects and research. For the longest time, the digital humanities community has focused on textual corpora, including text mining, and other natural language processing techniques. Although some disciplines of the humanities, such as art history and archaeology have a long history of using visualizations. In recent years, the digital humanities community has started to shift the focus to include other modalities, such as audio-visual data. In turn, methods in machine learning and computer vision have been proposed for the specificities of such corpora. Over the last decade, the visualization community has engaged in several collaborations with the digital humanities, often with a focus on exploratory or comparative analysis of the data at hand. This includes both methods and systems that support classical Close Reading of the material and Distant Reading methods that give an overview of larger collections, as well as methods in between, such as Meso Reading. Furthermore, a wider application of machine learning methods can be observed on cultural heritage collections. But they are rarely applied together with visualizations to allow for further perspectives on the collections in a visual analytics or human-in-the-loop setting. Visual analytics can help in the decision-making process by guiding domain experts through the collection of interest. However, state-of-the-art supervised machine learning methods are often not applicable to the collection of interest due to missing ground truth. One form of ground truth are class labels, e.g., of entities depicted in an image collection, assigned to the individual images. Labeling all objects in a collection is an arduous task when performed manually, because cultural heritage collections contain a wide variety of different objects with plenty of details. A problem that arises with these collections curated in different institutions is that not always a specific standard is followed, so the vocabulary used can drift apart from another, making it difficult to combine the data from these institutions for large-scale analysis. This thesis presents a series of projects that combine machine learning methods with interactive visualizations for the exploratory analysis and labeling of cultural data. First, we define cultural data with regard to heritage and contemporary data, then we look at the state-of-the-art of existing visualization, computer vision, and visual analytics methods and projects focusing on cultural data collections. After this, we present the problems addressed in this thesis and their solutions, starting with a series of visualizations to explore different facets of rap lyrics and rap artists with a focus on text reuse. Next, we engage in a more complex case of text reuse, the collation of medieval vernacular text editions. For this, a human-in-the-loop process is presented that applies word embeddings and interactive visualizations to perform textual alignments on under-resourced languages supported by labeling of the relations between lines and the relations between words. We then switch the focus from textual data to another modality of cultural data by presenting a Virtual Museum that combines interactive visualizations and computer vision in order to explore a collection of artworks. With the lessons learned from the previous projects, we engage in the labeling and analysis of medieval illuminated manuscripts and so combine some of the machine learning methods and visualizations that were used for textual data with computer vision methods. Finally, we give reflections on the interdisciplinary projects and the lessons learned, before we discuss existing challenges when working with cultural heritage data from the computer science perspective to outline potential research directions for machine learning and visual analytics of cultural heritage data

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio