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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)
Our paper proposes a vision-based hand gesture recognition system. It is implemented in a camera-projector system to achieve an augmented reality tool. In this configuration the main problem is that the hand surface reflects the projected background, thus we apply a robust hand segmentation method. Hand localizing is based on a background subtraction(More)
We have developed a hand gesture recognition system, based on the shape analysis of static gestures, for Human Computer Interaction purposes. Our appearance-based recognition uses modified Fourier descriptors for the classification of hand shapes. As always found in literature, such recognition systems consist of two phases: training and recognition. In our(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)
Film archives are continuously in need of automatic restoration tools to accelerate the correction of film artifacts and to decrease the costs. Blotches are a common type of film degradation and their correction needs a lot of manual interaction in traditional systems due to high false detection rates and the huge amount of data of high resolution images.(More)