Vizuális kategóriák tanulása

The primary goal of present work is to develop methods for the representation of visual
information that integrates appearance, structure and motion visual cues. We believe that
this integration can increase current performance of visual information categorization
and recognition methods.
The ability to detect and classify objects and object categories is one of the most
useful functions of our visual system. We recognize almost all visual properties of
objects and scenes at a glance. We are able to learn to discriminate between object
categories (e.g. people from cars) and within them (e.g. face of father from face of
brother within the category of faces). At the same time, the best algorithmic methods are
far from human abilities in number of categories learned, in classification speed, in the
ease and flexibility of learning new categories.
Replicating humans' abilities of learning and recognition of object categories would
revolutionize our everyday life. The list of possible applications that could be
developed based on more efficient object category recognition technologies would contain
hundreds of items, e.g. security, personalized healthcare, personal robots, design of
autonomous cars and many more.
During the research we will work on methods for representation of appearance and
structure of visual information from single and multiple views, on models for the
analysis and integration of different visual cues, on the application of statistical
learning methods for categories and on methods for categorization of objects and actions
(events).