Structural Descriptions in Human-Assisted Robot Visual Learning

The paper presents an approach to using structural descriptions, obtained through a human-robot tutoring dialogue, as labels for the visual object models a robot learns. The paper shows how structural descriptions enable relating models for different aspects of one and the same object, and how being able to relate descriptions for visual models and discourse referents enables incremental updating of model descriptions through dialogue (either robot- or human-initiated). The approach has been implemented in an integrated architecture for human-assisted robot visual learning

The Impact of the Latest 3D Technologies on the Documentation of Underwater Heritage Sites

Documenting underwater cultural heritage is a challenging undertaking. Underwater environment is not a man’s natural habitat and special equipment and devices had to be invented so that he could enter and study this environment. Several decades of underwater research and many sacrifices were needed to fully understand the importance of underwater heritage and its protection. The means for accurate documentation underwater are very limited and demanding, due to required technical equipment it is also expensive. Emergence of modern 3D methods and accompanying software tools for processing of 3D data is therefore of utmost importance for documenting and protection of underwater cultural heritage. In comparison to manual and analog methods, 3D methods offer much better accuracy, they substantially shorten the necessary time spent underwater and in this way improve the safety at work as well as lower the entire cost of field work. For illustration of the above development we discuss archeological case studies from the North East Adriatic

Uvajanje 3D tehnologij pri varstvu kulturne dediščine

Pocenitev tridimenzionalnih merilnikov in napredek tehnik snemanja in zajemanja podatkov omogočata njihovo uporabo na različnih področjih. Nova strojna in programska orodja, ki omogočajo široko računalniško podprto tridimenzionalno dokumentiranje, so še posebej pomembna tudi pri varstvu kulturne dediščine. Različne metode zajemanja podatkov, bodisi z merilniki, bodisi z fotogrametričnimi ovrednotenji množic slikovnega gradiva, je sedaj mogoče 3D dokumentirati tako posamezne predmete kot tudi prostor, v katerem živimo. V zadnjih letih postaja tridimenzionalna dokumentacija zaradi dostopnosti do strojnih orodij nepogrešljivo orodje tudi v sodobnem varstvu kulturne dediščine. Žal pa je za namensko uporabo pri analizi, standardizaciji, dokumentiranju, arhiviranju in interpretaciji tridimenzionalne dokumentirane kulturne dediščine na razpolago zelo malo programskih orodij

Hierarchical statistical learning of generic parts of object structure

With the growing interest in object categorization various methods have emerged that perform well in this challenging task, yet are inherently limited to only a moderate number of object classes. In pursuit of a more general categorization system this paper proposes a way to overcome the computational complexity encompassing the enormous number of different object categories by exploiting the statistical properties of the highly structured visual world. Our approach proposes a hierarchical acquisition of generic parts of object structure, varying from simple to more complex ones, which stem from the favorable statistics of natural images. The parts recovered in the individual layers of the hierarchy can be used in a top-down manner resulting in a robust statistical engine that could be efficiently used within many of the current categorization systems. The proposed approach has been applied to large image datasets yielding important statistical insights into the generic parts of object structure.

Fotogrametrično zajemanje 3D podatkov

Fotogrametrično zajemanje 3D podatko

A System for Continuous Learning of Visual Concepts

We present an artificial cognitive system for learning visual concepts. It comprises of vision, communication and manipulation subsystems, which provide visual input, enable verbal and non-verbal communication with a tutor and allow interaction with a given scene. The main goal is to learn associations between automatically extracted visual features and words that describe the scene in an open-ended, continuous manner. In particular, we address the problem of cross-modal learning of visual properties and spatial relations. We introduce and analyse several learning modes requiring different levels of tutor supervision