Attila Licsár

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Our paper proposes a vision-based hand gesture recognition system with interactive training, aimed to achieve a user-independent application by on-line supervised training. Usual recognition systems involve a preliminary off-line training phase, separated from the recognition phase. If the system recognizes unknown (non-trainer) users the recognition rate(More)
I nformation technologies can open new avenues for preserving and circulating of culture. This article presents a folk song search-and-retrieval system that relies on a gesture-based interface. The system preserves both the multimedia content (the folk songs) and the core part of the Kodály music-teaching approach, which is a hand sign-based system of(More)
We have developed a static hand-gesture recognition system for the Human Computer Interaction based on shape analysis. This appearance-based recognition uses modified Fourier descriptors for the classification of hand shapes. Usually systems use two phases: training and running phase under the recognition. A new method is shown that under the running phase(More)
The paper will present a video surveillance and event detection and annotation framework for semi-supervised surveillance use. The system is intended to be used in automatic mode on camera feeds that are not actively watched by surveillance personnel, raising alarms and enrolling annotation data when unusual events occur. We present the current detector(More)
We have developed a new semi-automatic neural network based method to detect blotches with low false alarm rate on archive films. Blotches can be modeled as temporal intensity discontinuities, hence false detection results originate from object motion (e.g. occlusion), non-rigid objects or erroneous motion estimation. In practice, usually, after the(More)