Computer Vision Methods for Unconstrained Gesture Recognition in the Context of Sign Language Annotation
type: Dataset
description: This PhD thesis concerns the study of computer vision methods for the automatic recognition of unconstrained gestures in the context of sign language annotation. Generally the annotation of SL video corpus is man- ually 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. We have studied some image processing techniques for assisting annotation. 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.
issued: 2026-05-29T14:45:56.816581
language: en
modified: 2026-05-29T14:45:56.816588
contact point: Organization
  fn: CCSD
keyword: analyse-de-gestes
keyword: annotation-automatique
keyword: automatique-annotation
keyword: gesture-analysis
keyword: infoeu-reposemanticsdoctoralthesis
keyword: infoinfo-clcomputer-science-cscomputation-and-language-cscl
keyword: langue-des-signes
keyword: sign-language
keyword: theses
landing page: https://theses.hal.science/tel-00768440

Computer Vision Methods for Unconstrained Gesture Recognition in the Context of Sign Language Annotation
type: Distribution
format: HTML
issued: 2026-05-29T14:45:56.907238
modified: 2026-05-29T14:45:56.777621
access URL: https://theses.hal.science/tel-00768440

test_moissonnage_selune
type: Agent