Application of video processing methods for linguistic research

Evolution and changes of all modern languages is a well-known fact. However, recently it is reaching dynamics never seen before, which results in loss of the vast amount of information encoded in every language. In order to preserve such heritage, properly annotated recordings of world languages are necessary. Since creating those annotations is a very laborious task, reaching times 100 longer than the length of the annotated media, innovative video processing algorithms are needed, in order to improve the efficiency and quality of annotation process

Severe hydrops in the infant of a Rhesus D-positive mother due to anti-c antibodies diagnosed antenatally: a case report

<p>Abstract</p> <p>Introduction</p> <p>Rhesus haemolytic disease of the newborn is a prototype of maternal isoimmunisation and fetal haemolytic disease. There are other rare blood group antigens capable of causing alloimmunisation and haemolytic disease such as c, C, E, Kell and Duffy. In India, after the confirmation of a newborn's blood group, antibodies are screened only if the mother is Rehsus D-negative negative and the father is Rhesus D-positive. Hydrops in Rhesus positive women are investigated along the lines of non-immune hydrops.</p> <p>Case presentation</p> <p>We report the case of a patient from India where irregular antibodies were requested for an O-positive 26-year-old mother in order to investigate fetal hydrops. Anti-c antibody was revealed and the fetus was treated successfully with compatible O negative and c negative intrauterine blood transfusions. The baby was treated postnatally with double volume exchange transfusion with the same compatible blood, and was discharged 30 days after birth.</p> <p>Conclusion</p> <p>We highlight the importance of conducting irregular antibody screening for women with significant obstetric history and fetal hydrops. This could assist in diagnosing and successfully treating the fetus with appropriate antigen negative cross-matched compatible blood. We note, however, that anti-c immunoglobulin is not yet readily available.</p

AV Processing in eHumanities - a paradigm shift

Introduction Speech research saw a dramatic change in paradigm in the 90-ies. While earlier the discussion was dominated by a phoneticians’ approach who knew about phenomena in the speech signal, the situation completely changed after stochastic machinery such as Hidden Markov Models [1] and Artificial Neural Networks [2] had been introduced. Speech processing was now dominated by a purely mathematic approach that basically ignored all existing knowledge about the speech production process and the perception mechanisms. The key was now to construct a large enough training set that would allow identifying the many free parameters of such stochastic engines. In case that the training set is representative and the annotations of the training sets are widely ‘correct’ we could assume to get a satisfyingly functioning recognizer. While the success of knowledge-based systems such as Hearsay II [3] was limited, the statistically based approach led to great improvements in recognition rates and to industrial applications

Towards automatic gesture stroke detection

Automatic annotation of gesture strokes is important for many gesture and sign language researchers. The unpredictable diversity of human gestures and video recording conditions require that we adopt a more adaptive case-by-case annotation model. In this paper, we present a work-in progress annotation model that allows a user to a) track hands/face b) extract features c) distinguish strokes from non-strokes. The hands/face tracking is done with color matching algorithms and is initialized by the user. The initialization process is supported with immediate visual feedback. Sliders are also provided to support a user-friendly adjustment of skin color ranges. After successful initialization, features related to positions, orientations and speeds of tracked hands/face are extracted using unique identifiable features (corners) from a window of frames and are used for training a learning algorithm. Our preliminary results for stroke detection under non-ideal video conditions are promising and show the potential applicability of our methodology