A folk song retrieval system with a gesture-based interface

This article describes how a folk song retrieval system uses a gesture-based interface to recognize Kodály hand signs and formulate search queries

Behavior and event detection for annotation and surveillance

Visual surveillance and activity analysis is an active research field of computer vision. As a result, there are several different algorithms produced for this purpose. To obtain more robust systems it is desirable to integrate the different algorithms. To achieve this goal, the paper presents results in automatic event detection in surveillance videos, and a distributed application framework for supporting these methods. Results in motion analysis for static and moving cameras, automatic fight detection, shadow segmentation, discovery of unusual motion patterns, indexing and retrieval will be presented. These applications perform real time, and are suitable for real life applications

Trainable Post-Processing Method To Reduce False Alarms In The Detection Of Small Blotches Of Archive Films

Abstract—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 automatic detection step the false alarms are removed manually by an operator, significantly decreasing the efficiency of the restoration process. Our post-processing method classifies each detected blotch by its image features to minimize false results and the necessity of human intervention. The proposed method is tested on real archive sequences. Keywords-digital film restoration; blotch detection; machine learning I

Supervised training based hand gesture recognition system

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 new practical approach, following the chosen appearance-based model, training and recognition is done in an interactive supervised way: the adaptation for untrained gestures is also solved by hand signals. Our experimental results with three different users are reported. In this paper, besides describing the recognition itself, we demonstrate our interactive training method in a practical application. ∗ 1

T.: Hand gesture recognition in camera-projector system

Abstract. 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 method, which adapts to the changes of the projected background. Hand poses are described by a method based on modified Fourier descriptors, which involves distance metric for the nearest neighbor classification. The proposed classification method is compared to other feature extraction methods. We also conducted tests on several users. Finally, the recognition efficiency is improved by the recognition probabilities of the consecutive detected gestures by maximum likelihood approach.

Hand-Gesture Based Film Restoration

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 of the system users can interactive modify and learn hand gestures by the gesture motion, so they could improve the efficiency of the system. With this interactive learning algorithm our system is able to adapt to similar gestures of other users or small changing of hand posture. We will show a gesture recognition application applying these methods for the controlling of old film restoration

Trainable blotch detection on high resolution archive films minimizing the human interaction

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. Blotch detectors need reliable motion estimation to avoid the false detection of uncorrupted regions. In case of erroneous detection, usually an operator has to remove the false alarms manually, which significantly decreases the efficiency of the restoration process. To reduce manual intervention we developed a two-step false alarm reduction technique including pixel and object based methods as post-processing. The proposed pixel based algorithm compensates motion, decreasing false alarms at low computational cost, while the following object based method further reduces the residual false alarms by machine learning techniques. We introduced a new quality metric for detection methods by measuring the required amount of manual work after the automatic detection. In our novel evaluation technique the ground truth is collected from digitized archive sequences where defective pixel positions are detected in an interactive process