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Classifying materials from their appearance is challenging. Impressive results have been obtained under varying illumination and pose conditions. Still, the effect of scale variations and the possibility to generalise across different material samples are ...
This paper presents the algorithms and results of our par- ticipation to the image annotation task of ImageCLEFmed 2007. We proposed a multi-cue approach where images are represented both by global and local descriptors. These cues are combined following t ...
The state-of-the-art in control of hand prosthetics is far from optimal. The main control interface is represented by surface electromyography (EMG): the activation potentials of the remnants of large muscles of the stump are used in a non-natural way to c ...
ntegrating information coming from different sensors is a fundamental capability for autonomous robots. For complex tasks like topological localization, it would be desirable to use multiple cues, possibly from different modalities, so to achieve robust pe ...
Localization and context interpretation are two key competences for mobile robot systems. Visual place recognition, as opposed to purely geometrical models, holds promise of higher flexibility and association of semantics to the model. Ideally, a place rec ...
Auditory and visual cues are important sensor inputs for biological and artificial systems. They provide crucial information for navigating environments, recognizing categories, animals and people. How to combine effectively these two sensory channels is s ...
Automatic annotation of medical images is an increasingly important tool for physicians in their daily activity. Hospitals nowadays produce an increasing amount of data. Manual annotation is very costly and prone to human mistakes. This paper proposes a mu ...
It is of prime importance in everyday human life to cope with and respond appropriately to events that are not foreseen by prior experience. Machines to a large extent lack the ability to respond appropriately to such inputs. An important class of unexpect ...
We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been ...
We present a discriminative online algorithm with a bounded memory growth, which is based on the kernel-based Perceptron. Generally, the required memory of the kernel-based Perceptron for storing the online hypothesis is not bounded. Previous work has been ...
