Determining subunits for sign language recognition by evolutionary cluster-based segmentation of time series

Abstract. The paper considers partitioning time series into subsequences which form homogeneous groups. To determine the cut points an evolutionary optimization procedure based on multicriteria quality assessment of the resulting clusters is applied. The problem is motivated by automatic recognition of signed expressions, based on modeling gestures with subunits, which is similar to modeling speech by means of phonemes. In the paper the problem is formulated, its solution method is proposed and experimentally verified

Accessible options for deaf people in e-Learning platforms: technology solutions for sign language translation

AbstractThis paper presents a study on potential technology solutions for enhancing the communication process for deaf people on e-learning platforms through translation of Sign Language (SL). Considering SL in its global scope as a spatial-visual language not limited to gestures or hand/forearm movement, but also to other non-dexterity markers such as facial expressions, it is necessary to ascertain whether the existing technology solutions can be effective options for the SL integration on e-learning platforms. Thus, we aim to present a list of potential technology options for the recognition, translation and presentation of SL (and potential problems) through the analysis of assistive technologies, methods and techniques, and ultimately to contribute for the development of the state of the art and ensure digital inclusion of the deaf people in e-learning platforms. The analysis show that some interesting technology solutions are under research and development to be available for digital platforms in general, but yet some critical challenges must solved and an effective integration of these technologies in e-learning platforms in particular is still missing

GCTW Alignment for isolated gesture recognition

In recent years, there has been increasing interest in developing automatic Sign Language Recognition (SLR) systems because Sign Language (SL) is the main mode of communication between deaf people all over the world. However, most people outside the deaf community do not understand SL, generating a communication problem, between both communities. Recognizing signs is a challenging problem because manual signing (not taking into account facial gestures) has four components that have to be recognized, namely, handshape, movement, location and palm orientation. Even though the appearance and meaning of basic signs are well-defined in sign language dictionaries, in practice, many variations arise due to different factors like gender, age, education or regional, social and ethnic factors which can lead to significant variations making hard to develop a robust SL recognition system. This project attempts to introduce the alignment of videos into isolated SLR, given that this approach has not been studied deeply, even though it presents a great potential for correctly recognize isolated gestures. We also aim for a user-independent recognition, which means that the system should give have a good recognition accuracy for the signers that were not represented in the data set. The main features used for the alignment are the wrists coordinates that we extracted from the videos by using OpenPose. These features will be aligned by using Generalized Canonical Time Warping. The resultant videos will be classified by making use of a 3D CNN. Our experimental results show that the proposed method has obtained a 65.02% accuracy, which places us 5th in the 2017 Chalearn LAP isolated gesture recognition challenge, only 2.69% away from the first place.Trabajo de investigació

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

Novel computational methods for in vitro and in situ cryo-electron microscopy

Over the past decade, advances in microscope hardware and image data processing algorithms have made cryo-electron microscopy (cryo-EM) a dominant technique for protein structure determination. Near-atomic resolution can now be obtained for many challenging in vitro samples using single-particle analysis (SPA), while sub-tomogram averaging (STA) can obtain sub-nanometer resolution for large protein complexes in a crowded cellular environment. Reaching high resolution requires large amounts of im-age data. Modern transmission electron microscopes (TEMs) automate the acquisition process and can acquire thousands of micrographs or hundreds of tomographic tilt se-ries over several days without intervention. In a first step, the data must be pre-processed: Micrographs acquired as movies are cor-rected for stage and beam-induced motion. For tilt series, additional alignment of all micrographs in 3D is performed using gold- or patch-based fiducials. Parameters of the contrast-transfer function (CTF) are estimated to enable its reversal during SPA refine-ment. Finally, individual protein particles must be located and extracted from the aligned micrographs. Current pre-processing algorithms, especially those for particle picking, are not robust enough to enable fully unsupervised operation. Thus, pre-processing is start-ed after data collection, and takes several days due to the amount of supervision re-quired. Pre-processing the data in parallel to acquisition with more robust algorithms would save time and allow to discover bad samples and microscope settings early on. Warp is a new software for cryo-EM data pre-processing. It implements new algorithms for motion correction, CTF estimation, tomogram reconstruction, as well as deep learn-ing-based approaches to particle picking and image denoising. The algorithms are more accurate and robust, enabling unsupervised operation. Warp integrates all pre-processing steps into a pipeline that is executed on-the-fly during data collection. Inte-grated with SPA tools, the pipeline can produce 2D and 3D classes less than an hour into data collection for favorable samples. Here I describe the implementation of the new algorithms, and evaluate them on various movie and tilt series data sets. I show that un-supervised pre-processing of a tilted influenza hemagglutinin trimer sample with Warp and refinement in cryoSPARC can improve previously published resolution from 3.9 Å to 3.2 Å. Warp’s algorithms operate in a reference-free manner to improve the image resolution at the pre-processing stage when no high-resolution maps are available for the particles yet. Once 3D maps have been refined, they can be used to go back to the raw data and perform reference-based refinement of sample motion and CTF in movies and tilt series. M is a new tool I developed to solve this task in a multi-particle framework. Instead of following the SPA assumption that every particle is single and independent, M models all particles in a field of view as parts of a large, physically connected multi-particle system. This allows M to optimize hyper-parameters of the system, such as sample motion and deformation, or higher-order aberrations in the CTF. Because M models these effects accurately and optimizes all hyper-parameters simultaneously with particle alignments, it can surpass previous reference-based frame and tilt series alignment tools. Here I de-scribe the implementation of M, evaluate it on several data sets, and demonstrate that the new algorithms achieve equally high resolution with movie and tilt series data of the same sample. Most strikingly, the combination of Warp, RELION and M can resolve 70S ribosomes bound to an antibiotic at 3.5 Å inside vitrified Mycoplasma pneumoniae cells, marking a major advance in resolution for in situ imaging

Computational Models for the Automatic Learning and Recognition of Irish Sign Language

This thesis presents a framework for the automatic recognition of Sign Language sentences. In previous sign language recognition works, the issues of; user independent recognition, movement epenthesis modeling and automatic or weakly supervised training have not been fully addressed in a single recognition framework. This work presents three main contributions in order to address these issues. The first contribution is a technique for user independent hand posture recognition. We present a novel eigenspace Size Function feature which is implemented to perform user independent recognition of sign language hand postures. The second contribution is a framework for the classification and spotting of spatiotemporal gestures which appear in sign language. We propose a Gesture Threshold Hidden Markov Model (GT-HMM) to classify gestures and to identify movement epenthesis without the need for explicit epenthesis training. The third contribution is a framework to train the hand posture and spatiotemporal models using only the weak supervision of sign language videos and their corresponding text translations. This is achieved through our proposed Multiple Instance Learning Density Matrix algorithm which automatically extracts isolated signs from full sentences using the weak and noisy supervision of text translations. The automatically extracted isolated samples are then utilised to train our spatiotemporal gesture and hand posture classifiers. The work we present in this thesis is an important and significant contribution to the area of natural sign language recognition as we propose a robust framework for training a recognition system without the need for manual labeling

Aerospace medicine and biology: A continuing bibliography with indexes (supplement 377)

This bibliography lists 223 reports, articles, and other documents recently introduced into the NASA Scientific and Technical Information System. Subject coverage includes: aerospace medicine and physiology, life support systems and man/system technology, protective clothing, exobiology and extraterrestrial life, planetary biology, and flight crew behavior and performance

In vivo evaluation of curcumin as an anti-inflammatory drug in a mouse model of chronic neuroinflammation

The increased average life expectancy of the world population has resulted in a higher incidence and prevalence of chronic neurodegenerative diseases. Alzheimer’s disease (AD) is the most commonly diagnosed dementia in humans. The typical cognitive and behavioral symptoms of the patients include changes in their personality, difficulties in daily life activities, deficits in language, speech, visuospatial skills, executive and memory functions. Abnormal aggregation of amyloid-β and hyperphosphorylated tau protein is hallmark histopathological features in the brains of AD patients. An inflammatory reaction is commonly observed in the progression of AD which involves activated microglia, astrocytes and elevated levels of cytotoxic cytokines interleukin-1 (IL-1), IL-6 and TNF-α in the patients’ brains. Clinical evidence indicates a vicious cycle of the inflammatory process in different brain regions of AD patients by excessive release of various mediating factors and causing toxicity, injury to synapses and neurons. The role of IL-6 is particularly important in the AD process as clinical evidence shows a decline in cognitive functions of the patients by increased brain levels of this cytokine along with advancing age. GFAP-IL6 transgenic mouse model with overexpression of IL-6 in the brain is a suitable model to investigate structural and functional changes in the brain caused by IL-6 initiated inflammatory cascade. These mice exhibit significant phenotypic features similar to the human condition including brain inflammation, motor and cognitive impairment. Natural compounds with cytokine-suppressive anti-inflammatory activity could be protective against the neurological disease of the GFAP-IL6 mice. Curcumin is a plant-based natural compound with potent in vivo antioxidant, anti-inflammatory and cytokine-suppressive properties and no adverse effects are known by its long-term consumption. Long-term feeding of the GFAP-IL6 mice with the Longvida curcumin, high bioavailability nanoformulation of the compound could be protective against the development of their functional deficits and neurological disease. We aimed to investigate and compare ataxia phenotype, motor and memory performances of the GFAP-IL6 and WT mice and assess whether feeding with Longvida curcumin food could be preventive for neurological disease and functional impairment of the GFAP-IL6 mice

Computer vision methods for unconstrained gesture recognition in the context of sign language annotation

Cette thèse porte sur l'étude des méthodes de vision par ordinateur pour la reconnaissance de gestes naturels dans le contexte de l'annotation de la Langue des Signes. La langue des signes (LS) est une langue gestuelle développée par les sourds pour communiquer. Un énoncé en LS consiste en une séquence de signes réalisés par les mains, accompagnés d'expressions du visage et de mouvements du haut du corps, permettant de transmettre des informations en parallèles dans le discours. Même si les signes sont définis dans des dictionnaires, on trouve une très grande variabilité liée au contexte lors de leur réalisation. De plus, les signes sont souvent séparés par des mouvements de co-articulation. Cette extrême variabilité et l'effet de co-articulation représentent un problème important dans les recherches en traitement automatique de la LS. Il est donc nécessaire d'avoir de nombreuses vidéos annotées en LS, si l'on veut étudier cette langue et utiliser des méthodes d'apprentissage automatique. Les annotations de vidéo en LS sont réalisées manuellement par des linguistes ou experts en LS, ce qui est source d'erreur, non reproductible et extrêmement chronophage. De plus, la qualité des annotations dépend des connaissances en LS de l'annotateur. L'association de l'expertise de l'annotateur aux traitements automatiques facilite cette tâche et représente un gain de temps et de robustesse. Le but de nos recherches est d'étudier des méthodes de traitement d'images afin d'assister l'annotation des corpus vidéo: suivi des composantes corporelles, segmentation des mains, segmentation temporelle, reconnaissance de gloses. Au cours de cette thèse nous avons étudié un ensemble de méthodes permettant de réaliser l'annotation en glose. Dans un premier temps, nous cherchons à détecter les limites de début et fin de signe. Cette méthode d'annotation nécessite plusieurs traitements de bas niveau afin de segmenter les signes et d'extraire les caractéristiques de mouvement et de forme de la main. D'abord nous proposons une méthode de suivi des composantes corporelles robuste aux occultations basée sur le filtrage particulaire. Ensuite, un algorithme de segmentation des mains est développé afin d'extraire la région des mains même quand elles se trouvent devant le visage. Puis, les caractéristiques de mouvement sont utilisées pour réaliser une première segmentation temporelle des signes qui est par la suite améliorée grâce à l'utilisation de caractéristiques de forme. En effet celles-ci permettent de supprimer les limites de segmentation détectées en milieu des signes. Une fois les signes segmentés, on procède à l'extraction de caractéristiques visuelles pour leur reconnaissance en termes de gloses à l'aide de modèles phonologiques. Nous avons évalué nos algorithmes à l'aide de corpus internationaux, afin de montrer leur avantages et limitations. L'évaluation montre la robustesse de nos méthodes par rapport à la dynamique et le grand nombre d'occultations entre les différents membres. L'annotation résultante est indépendante de l'annotateur et représente un gain de robustese important.This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Sign Language (SL) is a visual-gestural language developed by deaf communities. Continuous SL consists on a sequence of signs performed one after another involving manual and non-manual features conveying simultaneous information. Even though standard signs are defined in dictionaries, we find a huge variability caused by the context-dependency of signs. In addition signs are often linked by movement epenthesis which consists on the meaningless gesture between signs. The huge variability and the co-articulation effect represent a challenging problem during automatic SL processing. It is necessary to have numerous annotated video corpus in order to train statistical machine translators and study this language. Generally the annotation of SL video corpus is manually performed by linguists or computer scientists experienced in SL. However manual annotation is error-prone, unreproducible and time consuming. In addition de quality of the results depends on the SL annotators knowledge. Associating annotator knowledge to image processing techniques facilitates the annotation task increasing robustness and speeding up the required time. The goal of this research concerns on the study and development of image processing technique in order to assist the annotation of SL video corpus: body tracking, hand segmentation, temporal segmentation, gloss recognition. Along this PhD thesis we address the problem of gloss annotation of SL video corpus. First of all we intend to detect the limits corresponding to the beginning and end of a sign. This annotation method requires several low level approaches for performing temporal segmentation and for extracting motion and hand shape features. First we propose a particle filter based approach for robustly tracking hand and face robust to occlusions. Then a segmentation method for extracting hand when it is in front of the face has been developed. Motion is used for segmenting signs and later hand shape is used to improve the results. Indeed hand shape allows to delete limits detected in the middle of a sign. Once signs have been segmented we proceed to the gloss recognition using lexical description of signs. We have evaluated our algorithms using international corpus, in order to show their advantages and limitations. The evaluation has shown the robustness of the proposed methods with respect to high dynamics and numerous occlusions between body parts. Resulting annotation is independent on the annotator and represents a gain on annotation consistency

Specifications and programs for computer software validation

Three software products developed during the study are reported and include: (1) FORTRAN Automatic Code Evaluation System, (2) the Specification Language System, and (3) the Array Index Validation System