Computer Vision

In order to handle the ever growing amount of digital images, in this project we develop a content-based image retrieval system offering the possibility to efficiently search large image databases by automatically finding similar images. To this end, we use automatically extracted image features, derived from a selection of image candidates. read more »

An important capability for robotic vision systems is to distinguish objects from their background. In this project we study figure-ground segmentation for online object learning in an unconstrained interaction scenario: An object of interests is presented to the robot by hand in a dynamically changing, cluttered background. Employing attention mechanism and color-based clustering, we can successfully segment the object.

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The ultimate goal of biological vision systems is to infer knowledge about the outside world that is relevant for the system in order to interact with its environment. Therefore, it is not sufficient to just determine the category or the mere object identity. Many variables of interest must be estimated, for example the distance towards an object, the size, orientation, velocity or even such abstract variables like the mood of another animal. In this project we aim to extend existing invariant recognition approaches by using a new approach to hierarchical generative networks in order to implicitly represent (and learn) visual objects, and finally even scenes.

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Vision is a highly developed ability both in animals and in man. Exploring computer algorithms for the recognition of patterns and of 3D shapes and using eye-tracking experiments for investigating the control of visual attention can provide insights into the different processing steps underlying vision. This is a basis to synthesize important strategies of biological vision systems, among them visual learning, perceptual grouping and active gaze control in artificial vision systems, in this way providing an important ability for many application fields. read more »