Sign Language Recognition

This chapter covers the key aspects of sign-language recognition (SLR), starting with a brief introduction to the motivations and requirements, followed by a précis of sign linguistics and their impact on the field. The types of data available and the relative merits are explored allowing examination of the features which can be extracted. Classifying the manual aspects of sign (similar to gestures) is then discussed from a tracking and non-tracking viewpoint before summarising some of the approaches to the non-manual aspects of sign languages. Methods for combining the sign classification results into full SLR are given showing the progression towards speech recognition techniques and the further adaptations required for the sign specific case. Finally the current frontiers are discussed and the recent research presented. This covers the task of continuous sign recognition, the work towards true signer independence, how to effectively combine the different modalities of sign, making use of the current linguistic research and adapting to larger more noisy data set

A New System for Automatic Recognition of Italian Sign Language

This work proposes a preliminary study of an automatic recognition system for the Italian Sign Language (Lingua Italiana dei Segni - LIS). Several other attempts have been made in the literature, but they are typically oriented to international languages. The system is composed of a feature extraction stage, and a sign recognition stage. Each sign is represeted by a single Hidden Markov Model, with parameters estimated through the resubstitution method. Then, starting from a set of features related to the position and the shape of head and hands, the Sequential Forward Selection technique has been applied to obtain feature vectors with the minimum dimension and the best recognition performance. Experiments have been performed using the cross-validation method on the Italian Sign Language Database A3LIS-147, maintaining the orthogonality between training and test sets. The obtained recognition accuracy averaged across all signers is 47.24%, which represents an encouraging result and demonstrates the effectiveness of the idea

mRNA Translation: Fungal Variations on a Eukaryotic Theme

The accurate transfer of information from a nucleotide-based code to a protein-based one is at the heart of all life processes. The actual information transfer occurs during protein synthesis or translation, and is catalysed by ribosomes, supported by a large host of additional protein activities—the translation factors. This chapter reviews how the different eukaryotic initiation, elongation and termination factors assist the ribosome in establishing appropriate contacts with mRNAs during translation initiation, decode the genetic code during translation elongation, and finally release the newly made polypeptide and reuse the ribosomes during the termination and recycling phases