Visual Hand Posture Recognition in Monocular Image Sequences

Dick, Thorsten; Zieren, Jörg; Kraiss, Karl-Friedrich

doi:10.1007/11861898_57

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Many different hand postures result in the same contour (e.g., a fist and a pointing index finger seen from the pointing direction), rendering this feature problematic for unrestricted posture recognition from arbitrary viewing angles. This section presents a very promising approach for hand posture recognition in monocular image sequences that allows measurement of joint angles, viewing angle, and position in space [11,48]. This model-based method imposes no posture restrictions and requires no initialization or signer-dependent training.…”

Section: Detailed Hand Features: Hand Posture Recognitionmentioning

confidence: 99%

Recent developments in visual sign language recognition

Agris

Zieren

Canzler

et al. 2007

Univ Access Inf Soc

Self Cite

111

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Research in the field of sign language recognition has made significant advances in recent years. The present achievements provide the basis for future applications with the objective of supporting the integration of deaf people into the hearing society. Translation systems, for example, could facilitate communication between deaf and hearing people in public situations. Further applications, such as user interfaces and automatic indexing of signed videos, become feasible. The current state in sign language recognition is roughly 30 years behind speech recognition, which corresponds to the gradual transition from isolated to continuous recognition for small vocabulary tasks. Research efforts were mainly focused on robust feature extraction or statistical modeling of signs. However, current recognition systems are still designed for signer-dependent operation under laboratory conditions. This paper describes a comprehensive concept for robust visual sign language recognition, which represents the recent developments in this field. The proposed recognition system aims for signer-independent operation and utilizes a single video camera for data acquisition to ensure userfriendliness. Since sign languages make use of manual and facial means of expression, both channels are employed for recognition. For mobile operation in uncontrolled environments, sophisticated algorithms were developed that robustly extract manual and facial features. The extraction of manual features relies on a multiple hypotheses tracking approach to resolve ambiguities of hand positions. For facial feature extraction, an active appearance model is applied which allows identification of areas of interest such as the eyes and mouth region. In the next processing step, a numerical description of the facial expression, head pose, line of sight, and lip outline is computed. The system employs a resolution strategy for dealing with mutual overlapping of the signer's hands and face. Classification is based on hidden Markov models which are able to compensate time and amplitude variances in the articulation of a sign. The classification stage is designed for recognition of isolated signs, as well as of continuous sign language. In the latter case, a stochastic language model can be utilized, which considers uni-and bigram probabilities of single and successive signs. For statistical modeling of reference models each sign is represented either as a whole or as a composition of smaller subunits-similar to phonemes in spoken languages. While recognition based on word models is limited to rather small vocabularies, subunit models open the door to large vocabularies. Achieving signer-independence constitutes a challenging problem, as the articulation of a sign is subject to high interpersonal variance. This problem cannot be solved by simple feature normalization and must be addressed at the classification level. Therefore, dedicated adaptation methods known from speech recognition were implemented and modified to consider the specifics of sign languages. F...

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Section: Detailed Hand Features: Hand Posture Recognitionmentioning

confidence: 99%

Recent developments in visual sign language recognition

Agris

Zieren

Canzler

et al. 2007

Univ Access Inf Soc

Self Cite

111

View full text Add to dashboard Cite

show abstract

“…Dick, Zieren and Kraiss [9] introduced a conceptually similar system which was used to track a gloved hand for an interactive sign language tutor (detailed in Zieren's PhD thesis [10]). Glove markers included coloured fingers, as well as a rectangular marker on the back of the palm.…”

Section: Related Workmentioning

confidence: 99%

Vision-based hand pose estimation through similarity search using the earth mover's distance

2012

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Vision-based hand pose estimation presents unique challenges, particularly if high fidelity reconstruction is desired. Searching large databases of synthetic pose candidates for items similar to the input offers an attractive means of attaining this goal. The Earth Mover's Distance (EMD) is a perceptually meaningful measure of dissimilarity that has shown great promise in content-based image retrieval. It is in general, however, a computationally expensive operation, and must be used sparingly. We investigate a way of economising on its use while preserving much of its accuracy when applied naively in the context of searching for hand pose candidates in large synthetic databases. In particular, a two-tier search method is proposed which achieves similar accuracy with a speed increase of two orders of magnitude. The system performance is evaluated using real input and the results obtained using the different approaches are compared.

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Ergonomie und Mensch-Maschine-Systeme

Schmidt¹,

Schlick²,

Grosche³

2008

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Visual Hand Posture Recognition in Monocular Image Sequences

Cited by 4 publications

References 8 publications

Recent developments in visual sign language recognition

Recent developments in visual sign language recognition

Vision-based hand pose estimation through similarity search using the earth mover's distance

Ergonomie und Mensch-Maschine-Systeme

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